Handbook of Research on Advanced Research Methodologies for a Digital Society [1 ed.] 2021008422, 2021008423, 9781799884736, 9781799884743, 9781799874225, 9781799867920, 9781799876007, 9781799857723, 9781799865124, 9781799845881, 9781522588733, 9781799866220

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Handbook of Research on Advanced Research Methodologies for a Digital Society Gabriella Punziano University of Naples Federico II, Italy Angela Delli Paoli University of Salerno, Italy

A volume in the Advances in Knowledge Acquisition, Transfer, and Management (AKATM) Book Series

Published in the United States of America by IGI Global Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA, USA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com Copyright © 2022 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Names: Punziano, Gabriella, editor. | Delli Paoli, Angela, editor. Title: Handbook of research on advanced research methodologies for a digital society / Gabriella Punziano, and Angela Delli Paoli, editors. Description: Hershey, PA : Information Science Reference, 2021. | Includes bibliographical references and index. | Summary: “This edited book brings together researchers from different disciplines who engage in wide forms of reflection on the future of the research methods in the study of the digital society in its broadest sense with the aim to develop a broader theoretical reflection on the future of social research in its challenge to always be fitting, suitable, adaptable, and pertinent to the society to be studied”-- Provided by publisher. Identifiers: LCCN 2021008422 (print) | LCCN 2021008423 (ebook) | ISBN 9781799884736 (hardcover) | ISBN 9781799884743 (ebook) Subjects: LCSH: Social sciences--Research--Methodology. | Information society. Classification: LCC H62 .H245636 2021 (print) | LCC H62 (ebook) | DDC 300.72/1--dc23 LC record available at https://lccn.loc.gov/2021008422 LC ebook record available at https://lccn.loc.gov/2021008423 This book is published in the IGI Global book series Advances in Knowledge Acquisition, Transfer, and Management (AKATM) (ISSN: 2326-7607; eISSN: 2326-7615) British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. For electronic access to this publication, please contact: [email protected].

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Handbook of Research on Organizational Culture Strategies for Effective Knowledge Management and Performance Dana Tessier (Independent Researcher, Canada) Business Science Reference • © 2021 • 394pp • H/C (ISBN: 9781799874225) • US $295.00 Digital Technology Advancements in Knowledge Management Albert Gyamfi (Regina University, Canada) and Idongesit Williams (Aalborg University, Denmark) Information Science Reference • © 2021 • 275pp • H/C (ISBN: 9781799867920) • US $195.00 Promoting Qualitative Research Methods for Critical Reflection and Change Viktor Wang (University of Montana Western, USA) Information Science Reference • © 2021 • 367pp • H/C (ISBN: 9781799876007) • US $195.00 Enhancing Academic Research and Higher Education With Knowledge Management Principles Suzanne Zyngier (Zyngier Consulting, Australia) Information Science Reference • © 2021 • 323pp • H/C (ISBN: 9781799857723) • US $195.00 Role of Information Science in a Complex Society Elaine da Silva (São Paulo State University (UNESP), Brazil) and Marta Lígia Pomim Valentim (São Paulo State University (UNESP), Brazil) Information Science Reference • © 2021 • 297pp • H/C (ISBN: 9781799865124) • US $195.00 Theoretical and Practical Approaches to Social Innovation Chamindika Weerakoon (Swinburne University of Technology, Australia) and Adela McMurray (RMIT University, Australia) Information Science Reference • © 2021 • 265pp • H/C (ISBN: 9781799845881) • US $195.00 Developing Knowledge Societies for Distinct Country Contexts Nuno Vasco Lopes (University of Minho, Portugal) and Rehema Baguma (Makerere University, Uganda) Information Science Reference • © 2021 • 296pp • H/C (ISBN: 9781522588733) • US $185.00 Approaches and Processes of Social Science Research Icarbord Tshabangu (Leeds Trinity University, UK) Stefano Ba’ (Leeds Trinity University, UK) and Silas Memory Madondo (CeDRE International Africa, Zimbabwe) Information Science Reference • © 2021 • 260pp • H/C (ISBN: 9781799866220) • US $195.00

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INTRODUCTION This book introduces a wide variety of digital research methods that use computer-based products and solutions for data collection and analysis. It is not aimed to be exhaustive, but rather it presents a methodological outlook for research within and with the web. This is because the terrain of digital research is quite complex, variegated and rapidly changing. It can be considered a heterogenous collection of contributions united in their endeavor to engage with the complexities of methodological possibilities and challenges with which social researchers are dealing with. Doing research is an ever-changing challenge for social scientists. This is especially true in the digital society recently announced as Society 5.0 (Gladden 2019). Internet and computer mediated communication (CMC) are being incorporated into every aspect of daily life and social life has been deeply penetrated by the Internet. This is due to recent technological developments which increase the scope and range of online social spaces and the forms and time of participation widening the opportunities for user-generated content, and to the emergence of an “internet of things” and of ubiquitous mobile devices which allow to always be connected. We can say that digital technologies are becoming central in understanding culture and society, human experience and social world since computer software and hardware actively constitute self-hood, embodiment, social life, social relations, social institutions, in a word us as humans (Lupton 2015). Thus, digital technologies are entangled in the structures of society in many different, complex, and even contradictory ways and are deeply changing the practices, symbols, and shared meanings of our societies. The distinction between real and virtual, material and immaterial, bounded and unbounded spaces, in group, outgroup become confused and overlapping (Veltri, 2021; Rabelo, Bhide and Gutierrez, 2019) with frequent incursions of virtual reality into real life, social relations developed in physically unspecified places, online interactions losing their space-time anchorage and strength of linkages and incorporation of technology into our daily materiality. The generation of data about social life becomes not only routinized but a constituent part of social life and everyday practices. It may be intentional as in the case of people commenting on an event or posting photos or videos about their private life or unintentional as the automatic recording of domestic energy consumption or internet usage (Marres 2012). The question of new social formations, phenomena, and practices arising through internet access is different and separated from methods to carry out social research using ICTs. However, these two themes co-occur and need to converge (Fielding et al 2008): “The opportunities for social scientists will be driven both by changes in societies and advances in our research methods” (Fisher et al 2008). 

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The visions about the implication of technology for social research can be viewed over a continuum with two polar positions: an optimistic and a pessimistic perspective (Marres 2012). The optimists call for a democratization of social research due to the proliferation of recording, analysis, and visualization capacity enabled by digital technologies which support new forms of amateur-led social research. For example, tools for online survey make it possible to everyone to administrate a questionnaire; blogs, online communities, social media generate masses of data for social analysis and provide possibilities for analytics accessible to everyone. Such tools enhance the empirical and analytical possibilities of social research. The pessimists call for privatization of social research which is progressively confined in the laboratories of big IT firms, in few well-resourced research centers, equipped for the central storage and processing of big data (Savage and Burrows 2007). This would sign the end of social research as we know it, making obsolete the entire methodological apparatus of social research (Addeo Masullo 2021). In the middle between these two polar positions, we can locate those who attribute to digital technologies the ability of redistributing methods among different agents involved in social research (Marres 2012) reconfiguring the relations between research actors, research subjects and objects, technological infrastructure, IT firms, involving different domains in the research practice (academia, marketing organizations, government, activist organizations, etc.), so opening new and different space of intervention for digital social methods, as highlighted by the chapter of Marrazzo. In the following pages, we will highlight some of the ground concerned with social research methods in the digital society covered in this handbook. To make the content readily accessible, the chapters are allocated into six sections representing their dominant research area. The remaining paragraphs of this chapter correspond to the main sections of the handbook itself.

DIGITAL METHODS: CHALLENGES AND OPPORTUNITIES The impact of the digital turn on the epistemological and methodological asset of social research is undeniable due to the specificity of such digital data (Agodi 2010) and the opportunities of creative and innovative research practices (Giuffrida, Rinaldi, Zarba, 2016). Thus, some hypothesize a shift toward a fourth paradigm in social sciences based on the power of algorithms and computers (Stefanizzi, 2016; 2021). We can accept or not the notion of paradigm for such developments but what is undeniable is that the pervasive digitalization calls for interpretative schemes and methodological options more suitable for grasping the current complexity as Addeo and D’Auria point out in their chapter. Being not confined to communicative processes, digital research calls for new epistemological orientations, as Cipolla clearly states in his chapter also detecting different types of digital research and outlining future scenarios. This calls for tuning the methodological stances of social research from a twofold perspective, as Amaturo and Aragona highlight in their chapter: first by adapting the established social research methods to the practices and the interactions made by people when acting online (digitalized methods), then by creating new methods and techniques to analyze those online experiences that could not be framed using the tools of the traditional social research methodology (digital methods). Regarding the specificity, a crucial epistemological and ethical issue refers to the nature of data collected online which are drastically different from those collected through questionnaires, surveys or interviews: they are mostly collected without the actor being aware of it (Corposanto, Valastro 2014). vi

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It is a new ontological object referred to in different ways (digital life, digital shadow, digital footprint, algorithmic identity) (Addeo, Masullo 2021). On the one hand this confers to this information a naturalistic character, making them paradoxically closer to ethnographic materials than provoked data (such as answers to surveys and interviews) (Cardano, 2011), on the other hand, it raises important ethical issues. The ethical problems are related to the instrumental use that this type of observation makes of individuals seen as a means to an end (see the chapter by Attademo and Maccaro). On the quantitative side, big data together with the development in computational science have allowed for the spread of innovative explanatory models and simulations (topic modelling, machine learning), although their feature of being “searched” and “found” online may drive social research toward a data-driven approach and a naïve new empirism (Kitchin 2014, Amaturo, Aragona 2019a; Amaturo, Aragona 2019b). As highlighted in the chapter by Mangone, the role of theories, hypotheses and research questions in digital research becomes crucial. They may lose relevance in favor of decontextualized statistical analyses.

DIGITAL DATA COLLECTION AND CAPTURE Digital technologies open a new source of primary and secondary data. Primary information can be collected through technologically supported data collection methods alternatively termed web survey, internet survey, online survey. On the one hand, they democratise access to surveys. On the other hand, they increase the risk of low-quality surveys (see the chapter of Punziano, Addeo and Velotti). Nevertheless, it is important to acknowledge the potential benefits of online surveys. Indeed, they leverage the benefits of self-completion methodologies, reduce costs and material errors, increase respondents’ motivation, and better survey design (for example, by making it possible to integrate answers with interactive audio and video). However, it is equally important not to underestimate the potential problems such as the sampling bias due to the homogeneity of internet users who do not represent all sectors of the population, the likelihood of semantic misunderstanding which may affect information quality and compromise the comparability of responses to the same questions, the technical features of the different survey software. Advantages and disadvantages are clearly outlined in the chapter by Mauceri while the chapter by Coşkun and Mor dirlik investigates the factors that affect the participants’ response behaviors in web surveys. Secondary information derives from acquiring records of computer-mediated social interactions being them self-report on an individual’s social network or digital traces left by an individual’s online activities (digital footprint, digital shadow, algorithmic identity). To capture these data, the methodological toolbox needs to be enhanced with new competences which are not familiar for social researcher such as the capability of writing scripts, of scraping, of managing relational databases, of detecting errors in data collection. This last aspect and the related problems deriving from technical affordances and algorithms are especially outlined in the chapter by Molano and Molano. Moreover, following the APIcalypse (Bruns, 2019) caused by Cambridge Analytica case in 2018, data access is becoming more and more restricted, opening new scenario in data capture, as highlighted in the chapter by Acampa, Padricelli and Sorrentino. Thus, both in primary data collection and secondary data capture, many issues arise concerning accessibility due to the APIcalypse and the proprietary closure of data. These data are mostly owned by private vii

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companies and not entirely accessible for research purposes. And, also, the lack of socio-demographic information, which are mostly not available, making the digital research typically a post-demographic approach (see the chapter of De Falco, Acampa and Trezza and of Punziano).

DIGITAL METHODS: AMONG TRANSPOSED AND MIXED APPROACHES This section collects contributions on the digital adaptation of traditional methods and on the methodological challenges opened by the digital which implies embracing the natural logic of online communication affordances in gathering, ordering, and analyzing data. Indeed, the digital - with its own dynamics, features, affordances, and infrastructures - does not allow for a mere transposition of traditional methods and frameworks and imposes to find a place also for technological objects (devices, technology, robots, AI, algorithms, etc.) in a social research (see the chapter of Scarano and also that of Cipolla previously mentioned). Apart from providing new objects of analysis and foci for social research itself, it can be conceived as a source of methods for example when native social media devices such as mentions, like, retweets, tags, hashtags can be used for selecting, filtering and sampling texts, videos and images or when they become grounded categories for coding and interpreting content (Caliandro, 2018; Rogers, 2013), as suggested in the chapter of Punziano. Moreover, natively digital data provide interactional and position or opinionrelated information: likes can be representative of opinions, thought or positions, hashtags and clicks can be interpreted as proxies for interest in a given object, shares, mentions, tags and comments can be considered proxies for social ties, and so on. This creates new opportunities for rediscovering and enriching methods that had never been mainstream, such as content analysis (see the chapter by Punziano) and social network analysis (see the chapter by Corbisiero). Also, other traditional methods are potentiated in the hyper-digital age. It is the case for example of the diary method which is evolving in video diary thanks to the mass diffusion of smart devices and the TikTokification which allowed the proliferation of short video format. It consists of asking participants to maintain a record of their behaviours, actions, opinions or feelings about a topic through short videos on their smartphones (see the chapter of Bartlett and Vettini and that of Moretti). It is also the case of Delphi (see the chapter of Tintori and Ciancimino) and walkthrough methods (see the chapter of Cavagnuolo, Capozza and Matrella). The different methods presented in this section testify to a hybridization of approaches between qualitative and quantitative approaches, non-intrusive and intrusive techniques, human and non-human entities, hard and soft science. Such hybridization, particularly between qualitative and quantitative methods that drives toward mixed methods is necessary to gain insight into the real meaning of big data (see the chapter of Punziano and the chapter of Bagnini and Russo). What does seem clear is that continuing developments in new technologies will have clear implications in the research process, from the collection of data through its management, manipulation and analysis, to the finding dissemination (see the previously mentioned chapter of Cipolla). We can say that digital adaptation of traditional methods requires new methodological toolkits which include both traditional and innovative competencies being the latter related to digital capabilities and knowledge about the affordances of media, the ability to harness artificial intelligence, the availability of folksonomy classification and the recognition of the role of technologies, algorithms, devices and relational ontologies in shaping human interactions online, as recognized in the chapter of Punziano and that of Scarano and previously in the chapter of Cipolla. viii

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DIGITAL ETHNOGRAPHY Technologies have created new social spaces of narration and self-reflection (see the chapter of Grassi) as the heterogeneous production of stories within Wattpad during pandemic status demonstrates (see the chapter of Cantale). The spread of spaces of online narration and storytelling have strengthened the possibility of digital ethnography and biographic research (Delli Paoli, D’Auria 2021; Masullo, Addeo, Delli Paoli 2020). As a result, digital ethnography has become a widely accepted research method. It can be broadly defined as a qualitative research approach that adapts the traditional ethnographic techniques to online fields observing online social spaces of discussion. Initially applied to merely online phenomena especially manifesting in online communities, this approach is now spanning in different research fields, overcoming its dependence on consumption and marketing and opening to social science and disparate research topics such as culture, identity, social relationships, civic empowerment, social conflicts, gender and sexuality, religion and spirituality, deviant behaviours and illegal acts, illnesses, health concerns and interests. Although it shares with physical ethnography many features such as its naturalistic character, its flexibility to issues arising from the field, and its multi-method propensity (as the combination between digital ethnography and social network analysis proposed in the chapter by Ziglioli and Alhassan demonstrates), it differs from Ethnography under some crucial points as chapters by Delli Paoli and Padricelli, Punziano and Saracino show. Just to cite some differences, digital fields vary from online communities and blogs to social media sites and trans-medial fields (see the chapter by Delli Paoli). Entering the online sites diverges from face-to-face entrée in terms of accessibility and research design. From a data collection perspective, digital ethnography is far less time consuming; however, it requires a new set of skills due to the specificities of computer-mediated communication and its dramatically increased field site accessibility, which requires choices about field sites and decisions about types of data to gather and analyze. Moreover, some forms of digital ethnography are less intrusive than their physical parallel as they allow for researcher invisibility: cyberspace makes it possible for researchers to be unseen from people observed. At the same time, it seems to be a particularly suitable approach to investigate sensitive topics, for which it is not advisable to apply traditional research methods such as questionnaires, surveys and interviews. This clearly emerges in the chapter of Dimitrova and Öhman about financial information, more easily shared online than offline. What is more, it is especially appropriate for studying difficult to reach segments whose daily lives have been deeply penetrated by technologies such as adolescents and young people, as in the chapter of Rodríguez-Hoyos, Calvo-Salvador and Gutiérrez. On the other hand, this allows to document the explicit language of informants without the risk of obtrusiveness and disturbance (Addeo et al 2020) but, on the other hand, it raises the ethical dilemmas of unseen observations using private opinions and information. Differently from ethnography which intrinsically requires participation, in digital ethnography the participation of the researcher may be perceived as a continuum ranging from mere observation (lurking or covert observation) without establishing any social contact with the community members to revealing his presence as researcher with different degrees of participation (overt observation) to auto-ethnography based on autobiographical reflections about the researcher own digital experience, online practices and behaviors as in the chapter by Risi and Pronzato.

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SOCIAL MEDIA ANALYSIS Social media include a collection of web-based technologies and services such as blogs, microblogs (i.e. Twitter), collaborative editing tools (e.g. wikis), text messaging, discussion forums, video and image sharing services (e.g. YouTube, Flickr, Picasa, StumbleUpon, Last.fm), social networking platforms (Facebook, Myspace, etc.), virtual worlds for social gaming (Active worlds, Forterra systems, Second Life, etc.). The rise of social media applications has dramatically increased textual and multimedia data flow which are more than ever user generated. Therefore, social media can be considered not only an object of study per se but also a useful source for understanding social phenomena through complex analysis based on composite information deriving from different languages and contents. Social media content is multimodal and multi-layered as clearly demonstrates the use of meme (see the chapter of Giorgi) and to detect meanings different analytical frameworks can be used, as clearly outlined in the chapter of Elhamy. Social media research spans from the analysis of emotional involvement of users and polarization of their opinions as in the chapter of Trezza and Di Lisio which analyses the sentiment of people toward COVID vaccine, to the analysis of particular social phenomena and issues through the extraction of keywords or hashtags, as in the chapter of Felaco, Nocerino, Parola and Tofani which explore cancel culture on social media and in the chapter of Amatruda which aims to define the character of the neoCeltic culture, or the analysis of particular populations such as in the chapter of Luise and Lodetti which focuses on startuppers. What is more, the possibility of detecting also geographical positions through geotagging (users who assign spatial coordinates to their posts), geocoding or geoparsing (the identification of geographical location through algorithms) opens to integration between online and offline worlds (see for example the chapter of Crescentini, De Falco and Ferracci, that of Brandano, Iovino and Mantegazzi and that of De Chiara and Napolitano). Social network analysis (SNA) is particularly used in social media analysis not only to explore the formation of networks of relationships through retweets and shares but also as a tool to identify a conversational pattern, fake news, the spread of influence, information/disinformation propagation, as clearly showed in the chapter of Vitale, Catone, Giordano and Primerano. Twitter is the most studied platform for researchers thanks to its structure and configuration in hashtags. By allowing to index topics and follow communication flows, hashtags transform the platform in a huge search engine of public opinions and topic networks. Such networks can be different according to the topics and people driving conversations. Himelboim et al (2017) identify 6 structures: divided, unified, fragmented, clustered, in and out hub-and-spoke networks. Moreover, by using hashtags users build affinity spaces as emerges in the chapter of Akcaoglu and Hodges.

DIGITAL RESEARCH PRACTICES One of the striking things about digital methods is their variety in terms of methods, targets, topics, research problems and media, as clearly shows this section which collects heterogenous digital research practices. Digital research practices ranges from digital humanities to engineering dealing with different themes: from digital history in the chapter of Laruffa, cybersecurity in the chapter of Barrio and Poy, crime in the chapter of Aniyar, gender and sexualities in the chapters of Masullo, Delli Paoli and Tox

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masiello; of Monaco, of Coppola and of Maiello, climate change in the chapter of Ruiu and Ragnedda, wellbeing in the chapter of Iorio, Palmieri, Roberti, social identity in the chapter of Cirklová. In terms of targets, this section demonstrates that digital research may be particularly appropriate for “hidden populations” or “hard-to-reach populations” allowing to overcome some of the main barriers of traditional research practices. The heterogeneity of these practices emerges also in the differentiation of data on which they are based in terms of how data are generated (they can be automatically or user generated), data owner (public or private), data structuring (structured, semi-structured or unstructured) and data origin (digitalized or natively digital).

CONCLUSION Instead of entering deeply into highly optimist or pessimist vision of digital methods, this book testifies a substantial need for critical reflection on the epistemological and methodological implications derived from technological developments in social research. Research methods are, and need to be, always in motion. Digital methods are not the answer to all research questions but can enrich social research’s information and potentialities. Implementing them does not imply that we need to leave all we know behind. It does imply to rethink and adapt established methods, break out from their conventional surroundings remaining sensitive to context and without losing the awareness that data cannot speak for themselves, free of human framing and interpretation. The book provides insights into digital research methods at the intersection of emerging and established methods, qualitative and quantitative approaches, social science, humanities, informatics, media and communication studies with the twofold aim of providing researchers with inputs for critical thinking about research methods and hands-on research literacies for designing and conducting a digital research. By recognising that there is no single answer to the questions raised by the digital for social research, we are aware that this volume does not incorporate all possible and meaningful methods currently employed or emerging. However, since research methods are not only tools for investigating reality but especially tools for thinking about reality, this book can be considered an inspiration for thinking about emerging methodological possibilities.

REFERENCES Addeo, F., Delli Paoli, A., Esposito, M., & Bolcato, M. Y. (2020). Doing Social Research on Online Communities: The benefits of Netnography. Athens Journal of Social Sciences, 7(1), 9–38. Addeo, F., & Masullo, G. (2021). Studying the digital society: digital methods between tradition and innovation in social research. Italian Sociological Review. Agodi, M. C. (2010). L’estrazione di dati dalla rete: Una nota introduttiva. Quaderni di Sociologia, 54(3), 11–21. doi:10.4000/qds.671 Amaturo, E., & Aragona, B. (2019a). Per un’epistemologia del digitale: note sull’uso di big data e computazione nella ricerca sociale. Quaderni di Sociologia, 81(63), 71-90.

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Amaturo, E., & Aragona, B. (2019b). Methods for big data in social sciences. Mathematical Population Studies, 26(2), 65–68. doi:10.1080/08898480.2019.1597577 Bruns, A. (2019). After the ‘APIcalypse’: Social media platforms and their fight against critical scholarly research. Information Communication and Society, 22(11), 1544–1566. doi:10.1080/136911 8X.2019.1637447 Caliandro, A. (2018). Digital Methods for Ethnography: Analytical concepts for ethnographers exploring social media environments. Journal of Contemporary Ethnography, 47(5), 551–578. Cardano, M. (2011). La ricerca qualitativa. Il Mulino. Corposanto, C., & Valastro, A. (Eds.). (2014). Blog, Fb & Tw. Fare ricerca quali-quantitativa online. Giuffrè. Delli Paoli, A., & D’Auria, V. (2021). Digital ethnography: a systematic literature review. Italian Sociological Review. Fielding, N., Lee, R. M., & Blank, G. (2008). The Sage Handbook of Online Research Methods. Sage. doi:10.4135/9780857020055 Fisher, M., Lyon, S., & Zeitlyn, D. (2008). The Internet and the future of social science research. In The Sage Handbook of Online Research Methods. Sage. Giuffrida, G., Mazzeo Rinaldi, F., & Zarba, C. (2016). Big data e news online Possibilità e limiti per la ricerca sociale. Sociologia e ricerca sociale, 109, 159-173. Gladden, M. E. (2019). Who will be the members of society 5.0? Towards and Antropology of Technologically Posthumanized Future Societies. Social Sciences, 8(148), 1–39. Himelboim, I., Smith, M. A., Rainie, L., Shneiderman, B., & Espina, C. (2017). Classifying Twitter topic-networks using social network analysis. Social Media + Society, 3(1), 1–13. doi:10.1177/2056305117691545 Kitchin, R. (2014). Big Data, New Epistemologies and Paradigm Shifts. Big Data & Society, 1(1), 1-12. Lupton, D. (2015). Digital Sociology. Routledge. Marres, N. (2012). The redistribution of methods: On intervention in digital social research, broadly conceived. The Sociological Review, 60(S1), 139–165. doi:10.1111/j.1467-954X.2012.02121.x Masullo, G., Addeo, F., & Delli Paoli, A. (2020). L’approccio etnografico e netnografico nelle scienze sociali: definizioni, strumenti, prospettive future. In G. Masullo, F. Addeo, & A Delli Paoli (Eds.), Etnografia e netnografia Riflessioni teoriche, sfide metodologiche ed esperienze di ricerca (pp. 25-58). Loffredo. Rabelo, L., Bhide, S., & Gutierrez, E. (2019). Artificial Intelligence: Advances in Research and Applications. Nova Science Pub Inc. Rogers, R. (2013). Digital methods. MIT Press. doi:10.7551/mitpress/8718.001.0001 Savage, M., & Burrows, R. (2007). The Coming Crisis of Empirical Sociology. Sociology, 40(5), 885–899.

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Stefanizzi, S. (2016). Small, open, big i dati e la conoscenza scientifica. Sociologia e ricerca sociale, 109, 117-126. Stefanizzi, S. (2021) The use of Big Data: some epistemological and methodological considerations. Italian Sociological Review. Veltri, G. A. (2021). La ricerca sociale digitale. Mondadori Università.

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Acknowledgment

The editors would like to express their special thanks to all the people involved in this project for their commitment to and belief in this project. Without their support, this book would not exist. First, the editors would like to thank all the contributing authors for their drive for deepening understanding of contemporary digital methods. Our sincere gratitude goes to the chapter’s authors who contributed their time and expertise to this book. Second, the editors wish to acknowledge the valuable roles of the reviewers for their productive and supportive feedback which contributed to improve the quality, coherence, and content presentation of chapters. Most of the authors also served as referees; we highly appreciate their double task. Some of the contributions of this book come from the 2020 edition of the annual conference on Digital Research Methods held by the International Lab for Innovative Social Research (ILIS), established at the University of Salerno. We thank the ILIS and the other components of its board apart the editors, Giuseppe Masullo and Felice Addeo, for creating a strong international movement of discussion around the issues of digital social research, the challenges to be faced and the pitfalls to be contained. Last but not the least, a special thank to IGI Global, an exquisite publisher that has enthusiastically welcomed our proposal helping us to realize this work for us so hard and so dear. Gabriella Punziano University of Naples Federico II, Italy Angela Delli Paoli University of Salerno, Italy

 

Section 1

Digital Social Research: Challenges and Opportunities This section collects epistemological and theoretical contributions aimed at introducing challenges, opportunities, characteristics, peculiarities, and future scenarios of digital social research.

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Chapter 1

Epistemology of the Digital Enrica Amaturo University of Naples Federico II, Italy Biagio Aragona University of Naples Federico II, Italy

ABSTRACT The debate on the consequences that big data and computational techniques have generated in social sciences has developed from two opposite extremes. A consistent group of scholars today supports an active commitment of sociologists in dealing with the technological dimension of social investigation. The works developed by these “digital sociologists” focus on the definition of a method of social research that adopts a critical posture on the role that digital technology must have in scientific research but, at the same time, creative on the possibilities offered by technology to research. This posture requires great attention to the epistemology of the digital.

INTRODUCTION The debate on the consequences that big data and computational techniques have generated in social sciences has developed from two opposite extremes. On the one hand, those who argued, with sometimes triumphal tones, that big data and computation represented the new gold of the social sciences (Lazer, 2009; Mayer-Schonberger and Cuckier, 2013); on the other hand, those who considered them a dangerous new form of quantophrenia (boyd and Crawford, 2012), or even a threat to empirical sociology based on surveys and interviews (Savage and Burrows, 2007). Nevertheless, a consistent group of scholars today supports an active commitment of sociologists in dealing with the technological dimension of social investigation (Orton-Jhonson and Prior, 2013; Lupton, 2015; Daniels et al. 2016). The works developed by these “digital sociologists” focus on the definition of a method of social research that adopts a critical posture on the role that digital technology must have in scientific research, but, at the same time, creative on the possibilities offered by technology to research (Lupton, 2014; Marres, 2017; Savage and Halford, 2017). This posture requires great attention to the epistemology of the digital, which refers not only to the evaluation of the limits of scientific knowledge produced through digital techniques, but also to the DOI: 10.4018/978-1-7998-8473-6.ch001

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analysis of the short and long-term consequences that the digital is having on the relationship between the objects of study of sociology and their representation in data, on the relationship between these data and the sociological theories, and on the consequences of technology on the social research methods. The article is structured as follows. The first paragraph reconstructs the main sociological perspectives on the construction of the objects of study of our discipline and the data that represent them. The second paragraph, recalling the main objectives of social research, focuses on the link between data and theories, and on the possible sequences that these two elements can take on in digital social research. Finally, the third paragraph addresses the issue of innovation in the sociologist’s toolbox, trying to define points of discontinuity and continuity between digital techniques and those already consolidated in our discipline. The concluding paragraph summarizes the main epistemological precautions for using big data and computation in an aware and critical way, drawing some lines on which reflection should focus in the future.

DIGITAL DATA AND SOCIAL REALITY The question of the object of study of sociology refers to the ontological question on the existence of a reality to be investigated that is independent of the social researcher In the philosophy of science, the answers to this question have been realism, on the one hand, and constructivism, on the other. Realism, marked by the Durkheimian rule “considering social facts as things” (Durkheim 1895: 1963, 35), even in its critical form of twentieth-century neo-positivism, still supports the existence of an objective social reality, independent of both the social actor which is part of it and the scholar who intends to know it. For example, Lakatos (1976) considered social facts nonetheless objective, but their representation as the result of the techniques and background knowledge that the social researcher uses to detect them. On the other hand, constructivism believes that the social actor interprets reality by giving it an individual meaning, and the scholar interferes with reality through points of view (Weber, 1904). Weberian constructivism represented the starting point from which subsequent ontological positions in sociology developed: from phenomenology (Schutz, 1962), to symbolic interactionism (Denzin, 1970), to ethnomethodology (Cicourel, 1976). All these different positions share the idea that the construction of the object of study of sociology is not independent of that numerous set of choices made in every process that leads from abstraction to the translation of social phenomena into empirical data (Cicourel, 1976). Also, Schutz (1962), recalling Weber, does not consider the existence of an object of study given once and for all (“real”) relevant, but it is relevant whether its representation is made through procedures shared by the community of observers. The object of study is constructed through the method, and in this way, it emerges as real. Starting from these considerations, Latour’s (1987) epistemological concept developed in the field of sociology of science. For the purposes of our project of digital epistemology, two elements of Latour’s conception seem fundamental. The first is the questioning of the distinction between science and technology, which Latour replaces with the term technoscience. The empirical representations of the objects of study of sociology, the data, and the techniques used to construct them, should be understood as “black boxes”, mechanisms that are too complex to be entirely analyzed. Only the input and output are known and they are used without being questioned, in fact reifying themselves, becoming real objects. For Hacking (1999), data are social facts that oppose changes and generate reactions on the part of the subjects with whom they relate. For example, the classifications that are made in the social sciences can, 2

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when implemented in institutions, change the way individuals perceive themselves. Therefore empirical representations are not a mere reflection of a world that exists, but are meticulously “produced”. Once the data are stabilized, they become autonomous, independent of the construction procedures followed to obtain them, and without memory of their origins (Neresini, 2015). The second element of interest of the Latourian perspective concerns the questioning of the distinction between human and non-human actors. A researcher, a platform, a citation, a client or a survey tool are all actors who contribute with equal dignity to the construction of the black boxes. The data is then endowed with agency because their meaning and what they represent is constructed as the result of a long chain of human and nonhuman actors. Therefore the representations of these objects are real; the object becomes real in its socio-material representation. The relationship between the object of study and its representation is not only denotative, semantic, as realist epistemology would like, but also connotative. The data is contextual, but they indeed contribute to context creation. The realism of the empirical representations (the data) clearly shields constructivism from the accusation of relativism. Therefore, attention to the process becomes fundamental to the method used to represent the object of study. Deborah Lupton (2015) notes that starting from the reflections of Latour and his colleagues, a sociological literature has emerged that considers research techniques themselves as phenomena being studied in sociology. This challenges the practices adopted in research contexts, and considers techniques as actors who influence the way sociologists do research. Research techniques, therefore, reproduce social reality, but are at the same time configured by it, becoming both material and social (Ruppert et al., 2013). The volatility of the internet and its contents, always constantly updated, and the rapidity of the changes that occur in the platforms on the network through the modifications of the codes and of the Application Programming Interfaces (API) confirm the idea that an object of investigation that is given once and for all cannot exist. The term “methodological dispositif” indicates this inextricable relationship between techniques and objects, which are connected and constitute each other. Dispositifs are not only methods for research, but can themselves become objects of analysis, and in this case it is impossible to keep objects, subjects and research techniques separate. Reflecting on methodological dispositifs does not simply help understand how valid they are in representing reality, but how they go to configure the objects of study, and how they can be used to exercise power. Rogers (2013) emphasizes that when the representations of the phenomenon under study are already given by the analytical tools available within digital ecosystems, these representations should themselves become the object of research for the sociologist. As software studies experts have argued, the software that defines the functioning of digital objects has its own policy (Manovich, 2013), which has a structuring and modeling effect on which data to collect, which to consider important, and which to keep for the ‘analysis. For example, again, Rogers (2013), argues that search engines possess algorithmic authority because they act as socio-epistemological machines that exert power over sources considered important. The results of the research are therefore not mere information, but data that indicate precise power relationships. Precisely the analysis of these power relationships that take place in the large assemblage of data is one of the possible objects of study of digital sociology (Aragona et al. 2018). Considering the different positions explained above, it is possible to trace some essential elements of the way to define the objects of study and their representations in the context of a digital epistemology. First of all, we can say that the objects of study of digital sociology are constructed intersubjectively, and depend on the socio-technical activities carried out to investigate them. Intersubjectivity also occurs in the relationship between human and non-human actors. Internet platforms (Van Dijck et al., 2018) 3

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and methodological dispositifs (Ruppert, 2013) play an active role in the configuration of the objects of study, co-constitute them together with the social researcher. However, any possibility of relativism is overcome, because it is the same techniques that become objects that impose themselves on the researcher, and that capture, and simultaneously configure, his object of study. There is a continuous dialectic between objects and method. Representing one’s study objects through digital techniques also means having digital techniques as objects of study.

DATA AND SCIENTIFIC KNOWLEDGE The epistemological reflection on the impact of big data, computation and digital methods on social research has been developed starting from three dichotomies concerning the relationship between data and theories: correlation -vs - causation; data-driven research - vs- theory-driven research; induction -vs- deduction (Resnyansky, 2019). First of all - in the commercial and technological fields, as well as in the natural and behavioral sciences - digital data was presented and welcomed as a revolutionary change in the way of knowing, which marked the transition from a research that pursues causation, to a research that pursues correlation (Calude and Longo, 2017). Microsoft researcher Jim Gray has argued that digital data and its methods have not only contributed to the shift from causation to correlation, but have even defined a new way of producing knowledge, “exploratory science” (Hay et al. 2009), which is based on the idea that large amounts of data can be easily transformed into a new form of scientific knowledge (Stefanizzi, 2016). However, the main question concerns whether the idea that scientific knowledge can be produced without referring to theories is agreeable. One thing is identifying regularities (trends) within the data; another thing is to discover the mechanisms that generate them. The latter operation cannot be carried out without a theory and a deep and contextualized knowledge of the subject of investigation. As social researchers, however, we cannot forget that it is the data itself that incorporates theories, they are “loaded with theory” (Phillips, 1999), because they are influenced by the theoretical assumptions of those who build them. The thesis that data are always dependent on theories is widely present in the works of Thomas Kuhn (1962) and Paul Feyerabend (1969), and even Lackatos (1976) considered the empirical basis as the result of a triadic relationship between theory, evidence and background knowledge, where the latter represented the entire set of facts and choices made for the construction of that particular datum. Digital data are built by organizations and companies guided by specific information needs and cognitive objectives. Therefore, they are necessarily selective with respect to the aspects of the phenomena they are able to represent. Furthermore, digital data, which are collected, managed and analyzed through automated tools (algorithms), require greater attention to the construction processes of these algorithms, as well as to the preparation procedures for the analysis (what is often called the ” curation “of the data), in which data are selected, reduced and organized for analysis. When data are collected with web crawling and web scraping techniques, the pre-analysis work can be very tiring and time-consuming. The final point on which the discussion of the relationship between theories and data in digital research was articulated, which is closely linked to the previous one, concerns how inferences are constructed. According to Kitchin (2014), big data and computational techniques have given way to a way of constructing inferences that can save both neo-empiricist aspirations and the hypothetical deductive 4

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method used up to now in quantitative social research. This new scientific method tries to generate hypotheses starting from data and not from theories, but induction is not the final point of the method (as for the empiricists), but only a first step to formulating hypotheses to be checked through a deductive moment. Thus, this method rather than following neo-empiricist drifts, preserves the main dogmas of the post-positivist scientific method, but promotes the joint use of induction and deduction. In practice, the method is abductive (Pierce, 1883) and aims to insert unexpected results into an interpretative framework; abduction is also called the inference of the best explanation (Poston and McCain, 2017). If sociology really wants to benefit from the analysis of digital data, is to adopt a perspective that overcomes all the dichotomies that mark the relationship between data and theories. First, the difference between correlation and causation must be overcome. Although quantitative computational social science has become the most widespread way of doing computational social science, it should not be forgotten that the ambitions of the scholars who wrote the Manifesto of Computational Social Science (Conte et al., 2012) were quite different. Conte (2016) emphasizes that there was no such quantitative approach initially, but computational social science was mainly generative and aimed to reveal the mechanisms that produce social phenomena through simulations in computer ecosystems. This way of doing CSS has produced many theories on social phenomena such as cooperation, coordination and social conventions. The theoretical ambitions of the authors of the Manifesto have been supplanted by an emphasis on quantitative CSS simply because, as Merton first noted (1968), science moves towards areas where data are abundant, that “life on the net” (Lazer, 2009). Furthermore, with the overcoming of the dichotomy between induction and deduction through abduction, one can finally abandon even the most ancient dichotomy concerning the relationship between data and theories, that between empiricism and rationalism, adopting a synthesis of the two: criticism. Criticism, inaugurated in philosophy by Kant’s reflections, recognizes that sensitive experience (data) is shaped by our mental structures (theories). Knowledge thus becomes a compromise between the a priori knowledge of the rationalists and the a posteriori knowledge of the empiricists; it is in fact a synthesis between a priori elements, already present in the researcher’s background knowledge (such as categories, space, time, etc.), and a posteriori elements coming from the outside, from the phenomenon to be known. It is only in an abductive and critical epistemological framework that the current technological character of digital social investigation can be profitably reported within the different paradigmatic traditions that coexist in our discipline. The question of what the baggage of techniques should be contained in the toolbox of the digital sociologist remains open.

DIGITAL METHODS AND TECHNOLOGY Since the birth of the scientific method, the link between technology and method has been very close. If some Dutch spectacles had not built his lenses, Galileo Galilei could not have made the telescope with which he observed the motion of the stars. Marradi (1996) believes that the essence of the work of a social researcher consists precisely in the choice between the different techniques that other researchers have already identified and developed, and in the possibility of conceiving new ones. Non-intrusive data collection techniques have increased compared to the predigital past. Researchers can often collect web page information without their owners taking any action, especially when interfacing with social media platforms to access their data, the so-called APIs, which establish protocols for query a platform and its data. 5

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An important step forward in API research occurred around the beginning of the 2010s, with the spread of social media platforms (such as Facebook, Twitter and Instagram). Public APIs released by major social media platforms on the market have given researchers access to a rich set of digital data. Access to social media APIs has initiated a small revolution in digital methods, as social media platforms have allowed researchers to explore not only the socio-technical structures that shape online communication, but also the cultural processes emerging from users’ daily digital practices. Access to social media data via API has been progressively reduced since 2018, when the Cambridge Analytica scandal was brought to public attention. In response to the scandal, and to better protect the privacy of its users, Facebook (along with other platforms) started a policy of closing and restricting its previously opened APIs. Axel Bruns (2019) argues that the Cambridge Analytica scandal has been a convenient way for social media companies like Facebook and Twitter to make their data progressively inaccessible. A move that only increases the commercial value of that data (given that the business model of social media platforms consists precisely in selling user data to third parties (Srnicek, 2017), rather than increasing user privacy. User data, no longer collected via public APIs, are still accessible, for a fee, to private companies - which use them for commercial and marketing purposes. Therefore, it is no coincidence that academics have been among the most affected from the reduction of social media APIs. This initiative had as a consequence that social researchers had increasingly difficult access to platforms data. Producers and users may be very distant, and data that are generated by someone may be shared, sold, combined, merged and then analyzed to produce knowledge on some specific domains. In this context, where many actors are involved in the production and use of data, the empirical process truly becomes a cultural process that needs to be understood as such. What all this view on data suggests is that to put meaning into data and to understand what piece of reality that data is representing, we need to have a close look on what Kitchin and Lauriault (2014) call data assemblages. Data assemblages are complex socio-technical systems composed of many apparatuses and thoroughly entwined elements, whose central concern is the production of data. Data assemblages are made of two main activities: a technical process (operational definitions, data selection, data curation) which shapes the data as they are; a cultural process, which shapes the background knowledge (beliefs, instruments and other things that are shared in a scientific community) which enables the sharing of meanings. An epistemology of the digital requires, therefore, interdisciplinary and cross-cutting approaches, combining skills and viewpoints that cut across disciplines. The example of API big data collection is another example that reaffirms the pluralism that distinguishes the method of our discipline. Many techniques that have developed in other disciplinary contexts have led to the development of our discipline and its method (for example, just to name a few, scaling techniques in psychology, comparison in political science, the biographical method in history), the same is happening with digital techniques. Instead of rejecting new basis of information because they are unfamiliar, or because they are not produced according to the quality standards we are used to, big data should be considered for their characteristics, for what they can offer to social research, and for what they cannot offer to it. Adopting a pluralistic, pragmatic and critical posture means paying great attention to how to configure digital techniques in a fruitful way for social research. The goal is to build research designs that are able to solve the limits of digital techniques through the use of other techniques, including non-digital ones, with the idea that there is always a need for mutual adaptation and mismatch between research techniques, both inside and outside digital contexts. It is no coincidence that some of the most accurate reflections on the impact of big data in the social sciences today come from the proponents of the Mixed Methods approach (Hesse-Biber and Johnson, 2013), obviously open to any prospect of methodological integration. 6

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CONCLUSION Sociologists have started to develop more research about not only the technical addresses which guide digital research, but also about the normative and political imperatives that shape the background knowledge that it is used to make sense of digital data. It is because they, more than other scholars, have in their epistemological traditions critical approaches to data, reality and social knowledge. In the few pages of this chapter, it was certainly not possible to condense all the issues, and the still open questions, which are connected to the changes that the digital has brought about, and is continuing to operate, in the relationships between the sociologist and his objects of study, between data and sociological theories, and between the techniques and the data produced. However, it seems appropriate to underline at least two of the main considerations we made earlier. First, two elements of constructivist epistemology, intersubjectivity and the attention to the context in which the representations of the phenomena are made, constitute fundamental starting points for a digital epistemology. The problem of the connection between reality, data and theory is more important than ever, because many actors with different cultural and technical backgrounds are involved in the production and use of new data, and they all aliment the data assemblages. Digital technologies have in fact confirmed to us that the objects of study of sociology are constructed intersubjectively. As Lupton (2014) pointed out they are theorized from the moment in which digital research techniques are employed. Therefore, it is not possible to separate the analysis of the digital as an object of study, from the analysis with digital techniques, because both require focusing on how they are co-constituted. Furthermore, the last essential aspect for a digital epistemology is the adoption of a methodological posture that is both optimistic and critical at the same time. Recognizing the role of technology in the configurations that social research can assume does not imply technological determinism, and that technology must guide scientific knowledge. Scholars have appealed to critical optimism to explain how we should relate to the digital, since when facing technology often, also in the scientific literature, we find extreme opinions, that vary from a blind faith in the opportunities opened by the new computational tools to the conviction that “everything is a disaster”, or, in other words, that social research is condemned (Amaturo and Aragona, 2021). Critical optimism – a posture that can overcome both the worries about the end of traditional research methods, and the naive enthusiasms about the disruptive changes brought about by big data, computation and digital methods – is the right choice to unfold how an epistemology of the digital is evolving.

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Lakatos, I. (1976). Proof and refutations. In The logic of mathematical discovery. Cambridge University Press. doi:10.1017/CBO9781139171472 Latour, B. (1987). Science in action. Harvard University Press. Latour, B. (2012). We have never been modern. Harvard University Press. Lazarsfeld, P. F. (1955). Interpretation of statistical relations as a research operation. In The language of social research: A reader in the methodology of social research. Free Press. Lazer, D., Brewer, D., Christakis, N., Fowler, J., & King, G. (2009). Life in the Network: The Coming Age of Computational Social Science. Science, 323(5915), 721–723. Lupton, D. (2014). Digital sociology. Routledge. doi:10.4324/9781315776880 Manovich, L. (2013). Software takes command (Vol. 5). A&C Black. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute. Marradi, A. (1994). Referenti, pensiero e linguaggio: una questione rilevante per gli indicatori. Sociologia e ricerca sociale, 15(43), 137-207. Marradi, A. (2007). Metodologia della ricerca sociale. Il Mulino. Marres, N. (2017). Digital sociology: The reinvention of social research. John Wiley & Sons. Mayer-Schönberger, V., & Cukier, K. (2012). Big Data: A revolution that transforms how we work, live, and think. Houghton Mifflin Harcourt. Merton, R. K. (1968). Social theory and Social Structure. Free Press. Mitchell, M. (1998). An Introduction to Genetic Algorithms. MIT Press. doi:10.7551/mitpress/3927.001.0001 Neresini, F. (2015). Quando i numeri diventano grandi: Che cosa possiamo imparare dalla scienza. Rassegna italiana di sociologia, 56(3-4), 405–432. O’Sullivan, D. (2017). Big Data: why (oh why?) this computational social science? www.escholarship.org Orton-Johnson, K., & Prior, N. (2013). Digital sociology: Critical perspectives. Springer-Verlag. Peirce, C. S. (1883). Studies in logic. Little, Brown and Company. Phillips, D. (1999). How to play the game: a Popperian approach to the conduct of research. In Critical Rationalism and Educational Discourse. Rodopi. Popper, K. R. (1967). Knowledge: Subjective versus Objective. In A Pocket Popper (pp. 58–77). Oxford University Press. Popper, K. R. (1972). Objective Knowledge. An Evolutionary Approach. Clarendon Press. Poston, T., & McCain, K. (2017). Best explanations. In Best explanations: New essays on inference to the best explanation. Oxford University Press.

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Resnyansky, L. (2019). Conceptual frameworks for social and cultural Big Data analytics: Answering the epistemological challenge. Big Data & Society, 6(1), 1-12. Rogers, R. (2013). Digital methods. MIT Press. doi:10.7551/mitpress/8718.001.0001 Ruppert, E. (2013). Rethinking empirical social sciences. Dialogues in Human Geography, 3(3), 268–273. Ruppert, E., Law, J., & Savage, M. (2013). Reassembling social science methods: The challenge of digital devices. Theory, Culture & Society, 30(4), 22–46. Schutz, A. (1962). Common sense and scientific interpretation of human action. In Collected papers. Springer. doi:10.1007/978-94-010-2851-6_1 Stefanizzi, S. (2016). Small, open, big: I dati e la conoscenza scientifica. Sociologia e Ricerca Sociale, 109(2), 38–57. Van Dijck, J., Poell, T., & De Waal, M. (2018). The platform society: Public values in a connective world. Oxford University Press. doi:10.1093/oso/9780190889760.001.0001 Weber M. (1904). Die «Objektivität» sozialwissenschaftlicher und sozialpolitischer Erkenntnis. Archiv für Sozialwissenschaft und Sozialpolitik, 19. Wright Mills, C. (1959). The Sociological Imagination. Oxford University Press.

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Chapter 2

The Difficult Joining of Theory and Empirical Research: Strengths and Weaknesses of Digital Research Methods Emiliana Mangone https://orcid.org/0000-0002-9958-4346 University of Salerno, Italy

ABSTRACT Many approaches to the study of the social sciences rely on the interpretation of reality itself, giving rise to the quantitative/qualitative dispute. These methods cannot exist one without the other – nor can they necessarily find themselves on opposite poles. To follow one does not mean to forsake the other; on the contrary, both offer the opportunity to observe from different angles aspects of the phenomenon investigated, granting more effective readings of its complexity. While sociology has reproduced its various stances in its scholarly analyses, the most recent debate has relinquished this debate to focus on two alternative features. Both pertain to sociology and the role of social science researchers: the conjugation between theory and empirics and the crisis of sociology in providing answers to societal changes. This contribution aims to address the issues related to the conjugation between theory and empirical research considering digital research methods. The author outlines their strengths and weaknesses without forgetting the original status of sociology as a science.

INTRODUCTION Plenty of theories, schools, and approaches in the social sciences hinge on interpreting not only cases but also real data, breeding the so-called quantitative/qualitative dispute. Sociology has known this querelle from its earliest stages of development. First, with Durkheim’s Suicides (1897), we are amid the positivist (quantity) phase. Later, with The Polish Peasant in Europe and America (Thomas & Znaniecki, 1918-1920) the focus shifts to qualitative aspects. The switch aims to emancipate sociology from what DOI: 10.4018/978-1-7998-8473-6.ch002

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 The Difficult Joining of Theory and Empirical Research

Sorokin called quantophrenia and testomania (Sorokin, 1955), just a few years after the first edition of Thomas and Znaniecki’s book. He meant the reductivist conceptions that follow mechanical or robotic models (Sorokin, 1956) and leave no room for an open and profound look at the interpretation of social reality through which to give orientation, value, and meaning to the research itself. And yet, these methods cannot exist one without the other – nor they necessarily find themselves on opposite poles. To follow one does not mean to forsake the other; on the contrary, both offer the opportunity to observe from different angles aspects of the phenomenon investigated, granting more effective readings of its complexity. Researchers, particularly sociologists, must combine the system (objective dimension) with the individuals (subjective dimension); they must blend objective and subjective aspects (Mangone, 2009). The bridge is the interpretation and construction of reality through the relationships between individuals – and between them, society, and culture. The ensuing problems can be overcome only if, in the definition of the sociologist’s work, “knowledge is transferred and not ignored”. Therefore, research activities must presuppose a connection with knowledge, particularly in an ever-complex scenario where the demarcation of the territory to which to direct actions is increasingly less precise – also due to the mass media (with the multiplication and overlapping of information) and the globalisation processes. All the social phenomena studied by sociology have, of course, reproduced these opposing stances in the analyses of its scholars. However, the most recent debate has relinquished this controversy to focus instead on two other features, both connected to sociology and the role of social science researchers. First, the conjugation between theory and empirical research; second, the crisis of sociology in providing answers to the societal changes. The inherent complexity of socio-cultural phenomena drives the need to move towards methods that best enable enriching our knowledge of a phenomenon. It is particularly poignant in the current historical phase, with its shift from the network society (Castells, 1996) to the platform society (van Dijck, Poell & De Waal, 2018). The former is characterised, on the one hand, by the consequences of technological innovation and a change in capitalist structures and, on the other, by cultural transformations based on individual freedom and social autonomy through which to express identity. In the latter, platforms1 are areas that host a variety of activities: exchange of communicative practices, diverse forms of being together and participating in public life, technologies that allow both citizens and institutions to engage and achieve their goals. They generate a new ecosystem (Boccia Artieri, 2012), to the point of defining a novel perspective, that of media ecology. This approach offers an additional key to interpreting socio-cultural processes because its vision is not centred on the medium but includes the relationships between micro and macro aspects of social life interconnected thanks to digital media. The present contribution aims to address the issues related to the conjugation of empirical research and theory, which is the basis of all the activities of researchers, also considering the digital research methods. For the latter, I will try to outline both their limitations and opportunities in investigating the socio-cultural phenomena of an ever-changing society without forgetting the original status of sociology as a science.

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THE SCIENTIFIC STATUS OF THE SOCIAL SCIENCES (OR, MORE PRECISELY, OF SOCIOLOGY) The debate on the autonomy of sociology has been lively since its inception and essentially evolved in two opposing currents of thought. The first one advocated a discipline closely related to the natural sciences that should embrace the empirical methods adopted by the latter (positive method – quantity). The other, instead, supported the absolute autonomy of the social sciences, not allowing for procedural contaminations from the natural sciences in its scientific investigation (interpretive method – quality). All the social phenomena studied by sociology have reproduced these two positions in their scholarly analyses. However, during the history of this discipline, the debate focused eventually on two other aspects: the object of study of sociology2 and the conjugation between theory and empirics. In an essay by Pitirim A. Sorokin – first published in Russian in 1913 by Obrazovanie in St. Petersburg and then posthumously in English in 1998 under the title The Boundaries and Subject Matter of Sociology – one can read: To define the field of sociology, as with any science, means to select the category of facts that are the object of its study − in other words, to establish a special point of view on a series of phenomena that is distinct from the point of view of other sciences. No matter how diverse the definitions by means of which sociologists characterize the existence of social or superorganic phenomena, all of them have something in common, namely, that the social phenomenon − the object of sociology − is first of all considered the interaction of one or more kinds of center, or interaction manifesting specific symptoms. The principle of interaction lies at the base of these definitions; they are all in agreement on this point, and their differences occur further on, regarding the character and form of this interaction (Sorokin, 1998, p. 59) Unlike others in the history of the discipline, this definition of the field of study of sociology appears clear in both its objectives and aims. Sociology is a tool for understanding the interconnections of the social and sociality because it does not inspect specific aspects of society as such, but their interactions, links, and reciprocal conditioning. Assuming that sociology was meant to help society reflect on itself, sociological knowledge becomes indispensable for reading social phenomena. The role of sociology is to produce the knowledge through which society can observe its phenomena and acknowledge its problems, thus enabling its day-to-day improvement. Its main task is “the critical unhinging of the manoeuvring and manipulation of citizens and of consumers that rely on perverse usages of science” (Bourdieu, 2013, p. 12) going beyond the induced (not real) needs that are offered by common sense or the media. About the perverse use of science, Sorokin anticipated Bourdieu (Mangone, 2018) and, in the preface of Fad and Foibles in Modern Sociology and Related Sciences (1956) wrote thus: Any science, at any moment of its historical existence, contains not only truth but also much that is half-truth, sham-truth, and plain error. This has been especially true of the social and psychological disciplines, for the complexity of mental and social phenomena allows many a fallacy to be taken for the last word of science, ‘operationally defined, empirically tested, and precisely measured’. Even the sociology and psychology of today are not exceptions to this rule. They, too, contain verities; they, too, are contaminated by the diseases of sham-truth and error. Some of the ailments are well hidden in the recesses of their valid propositions while others infect their methods, techniques and tests” (Sorokin, 1956, p. v).

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Both Sorokin and Bourdieu pointed out the possibility of perverse effects of science, especially sociology. It follows that the object of study of sociology can only be the individual and collective phenomenal reality in relation to social systems. I agree with Gallino when he states that the “sociology-world” will have to start assessing social representations (Gallino, 2007). Today, it is necessary to combine this with the crisis of the systems and the attempts to define and launch new policies – which, however, have not prevented the unravelling of legal protections or the deterioration of the social fabric. Indeed, the latter needs to be reconstructed through new forms of solidarity. On sociology, therefore, one must return to the question posed by Berger and Luckmann: “The central question for sociological theory can then be put as follows: How is it possible that subjective meanings become objective facticities? Or, in terms appropriate to the aforementioned theoretical positions: How is it possible that human activity (Handeln) should produce a world of things (choses)? In other words, an adequate understanding of the ‘reality sui generis’ of society requires an inquiry into the manner in which this reality is constructed. This inquiry, we maintain, is the task of the sociology of knowledge” (Berger & Luckmann, 1966, p. 30). The conditions and situations for the process of acquiring knowledge must also be analysed considering their influence on people’s construction of reality and, consequently, on their social actions. Realities, or groups of realities, belong to particular social contexts (social relativity), and it is precisely this peculiarity that justifies the sociologist’s curiosity about both reality and knowledge. The work of the social scientist and the resulting knowledge has, therefore, a double configuration. On the one hand, they allow for an institutional accompaniment (public service). It does not mean responding to all the needs of society but formulating scientific answers to real problems. On the other hand, they allow the development of a critical and active citizen very close to Schütz’s (1946) ideal type of the “well-informed citizen”. The latter, revisited according to today’s society, seems to hope for the affirmation of modern citizenship based on a dimension of the individual’s reflexivity that is neither subjective nor structural but related to the order of reality of social relations. And it is precisely on relationships that Bourdieu bases his unitary model. Aiming at the conjugation of the “theory of action” with the “structuralist theory”, he focuses not on individual phenomena but systems of relations between objects and events. Bourdieu declares the supremacy of relationships over all forms of methodological monism that claim to uphold the ontological primacy of either the structure or the agent, the system or the actor, the collective or the individual. In his opinion, these dualistic alternatives reflect a common-sense perception of social reality that sociology must get rid of. In short, for Bourdieu, “relational thinking” is the foundation of the social sciences, and it must lead sociology to become reflexive (Bourdieu & Wacquant, 1992). He means that the discipline must recognise the limits of its scientific status, starting from the distinction between common sense knowledge and scientific knowledge. This introduces the idea of the “epistemological break”, i.e., the clear-cut marking of the boundaries between social science and common sense – while not denying that the persistence of the “spontaneous sociology” of common sense is deeply rooted in society. While all work activities affect both individuals and the economy, some also entail social and cultural implications. The issues related to the role of sociologists cannot be separated from those linked to their commitment and intervention in general. Sociological knowledge, or rather sociology itself, is suspected of “compromising with politics” (Bourdieu, 2013) since it stems from the work of a subject (the researcher) who is himself part of society and who therefore runs the risk of adopting its prejudices. The main cover against this danger is precisely the critical interpretation of socio-cultural phenomena. In an ever-complex scenario, it is necessary to distinguish between the various dimensions of analysis (Collins, 1988). The macro dimension relates to social systems and their forms of organisation; the 14

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micro dimension deals with the relationship between the individual/society and social actions. The meso dimension, an effort to integrate the other two, relates to the relationships between the social system and the lifeworld (the set of meanings and representations of culture). A concrete attempt at its application is Archer’s theory of agency (2003). Social research is, therefore, the tool to expand the capacity to describe a phenomenon, by increasing its knowledge, leading to its explanation and understanding, and then to its prediction. These levels are neither sequential nor separate (Homans, 1967), but a whole that translates into methodological integration between the disciplinary areas of social sciences. Keeping the three levels of analysis together (macro, meso and micro) implies an intellectual activity that goes beyond the “disciplinary” methodologies and points of view. The ever-present controversy between empirical-analytical methods (emphasising quantity and measurement), and hermeneuticinterpretive methods (emphasising subjective meanings and quality) remains the same, but the “debate of sociology around sociology” is no longer about overcoming it as much as about conjugating theory and empirical research.

COMBINING THEORY AND EMPIRICS FOR A NEW “SOCIOLOGICAL IMAGINATION” Regarding the debate on the conjugation of theory and empirical research in contemporary sociology, Goldthorpe (1997) took a very critical stance by speaking of the “scandal of sociology”. He explained his strong label by claiming that sociology had lagged behind other disciplines in its standards for integrating theory and empirical research. The particulars of his critique are as follows: contemporary sociologists split sharply on the relationship between their two main activities – empirical research and theory. Moreover, they also disagree on what kind of academic or scientific enterprise sociology is or should be. Finally, there is discord on how to interpret and respond to this intellectual division – or disciplinary fragmentation (Goldthorpe, 2000). The element that holds it all together is the explanation and understanding of the construction of reality through interactions between individuals, and between personality, society, and culture – Sorokin’s (1962/1947) indivisible sociocultural trinity. As individuals interact (in the world of everyday life and institutions) all these aspects must be read as correlated interpretations and not only the mere response to a trigger. About this aspect, recalling Cipolla’s (1998) definitions, it can be observed that theory, empirics, and operativity must be functionally integrated so that the researcher’s activity can be projected towards positive social change – instead of being trapped within watertight compartments. The first dimension represents the partial knowledge tool that provides the conceptual guidelines and the hypothesis to be verified empirically. The second represents the research feedback, with a twofold output. On the one hand, it offers to the theoretical area the problematic priorities to answer. On the other hand, it gives the general clues to the operational area (practice, third dimension), which it replicates through criteria of applicability and non-academic impact. One can no longer speak, therefore, of the opposition between theory and empirical research, but rather of a continuum of interdependencies (Cipolla, 2004)that goes from theory to operativity (practice) and usefulness (non-academic impact), passing through action research (McNiff, 2013; Chevalier & Buckles, 2019). Research designs, therefore, must rely on the intersection of methods and tools (mixed research and e-methods) and become laboratories of methodological experimentation. Research activities cannot merely contribute to scientific research on the topics in question – sometimes even as an end in itself. They should, instead, constitute a mechanism 15

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for facilitating and accompanying social innovation in terms of tools, organisation, and governance of social processes. Social science methods do not simply reproduce the phenomena they study, but contribute, to a greater or lesser extent, to their construction. Following this logic, research activities should be developed through methodologies that collect and analyse data and information, seeking to produce usable knowledge (from theory to usability) for the support, activation, reflection, and consolidation of processes of institutional innovation and individual and collective empowerment. The debate on the usefulness of sociology or sociological knowledge has never ceased since the 1970s, especially in the Anglo-American world – starting with Gouldnerʼs The Coming Crisis of Western Sociology (1970) – and in France (Boudon, 1971). Sociology hardly ever takes a transdisciplinary approach (Piaget, 1972) and above all does not have a holistic view of society. It certainly does not tend towards the valorisation of theoretical innovations, but rather towards the preservation of so-called traditional and mainstream approaches. It remains closed within its boundaries, resulting in self-referentiality and partial or total absence of a redefinition of paradigms, methodologies, and methods. Hence why we need to return to that “intellectual passion” defined by Polanyi as the active cognitive process that connects beauty, reality, responsibility, and science, because “Any process of enquiry unguided by intellectual passions would inevitably spread out into a desert of trivialities.” (Polanyi, 1958, p. 143) and what one does not want is to be trivial. Several elements, therefore, are intertwined: a) the actual skills acquired within a discipline; b) the curiosity and intellectual freedom that drive to overstepping the pre-established disciplinary frontiers; c) the ability – not to say the humility – to develop points of view that have no other ambition than to show what would otherwise remain unknown. Interdisciplinarity may be what shapes new intellectual passions that go beyond the models that conceive science through disciplinary, sub-disciplinary, and speciality conceptual declinations. The debates around these themes have known ups and downs. In America, after Burawoyʼs presidential address (2005) at the annual meeting of the American Sociological Association in 2004, they gained new vigour around “public sociology” – that form of sociology identified by Burawoy3 himself that establishes an open two-way confrontation with all the interlocutors it manages to approach. However, this discussion was born even earlier. One could consider, for example, Sorokin’s presidential address Sociology of Yesterday, Today and Tomorrow (1965), which gave an accurate prognosis for sociology and the hope that, in the future, the discipline will turn towards creative growth to enter its new period of great synthesis. Or the debate around Charles Wright Mills’ text, The Sociological Imagination (1959), which confirmed that one cannot understand people’s lives without understanding society and vice versa. Furthermore, it argued that individuals need a quality of mind to help them use the information for a lucid synthesis of what is happening and what can happen to the individual and the world. This quality, called “sociological imagination”, allows for reading biographies and history in reciprocal relation to society. In other words, the sociological imagination allows scholars to move from one perspective to another. In doing so, they grasp what is happening in the world and, at the same time, understand what is happening to themselves and other individuals as points of intersection of the biography and history of society – i.e., at those intersections that Bourdieu would consider relations. It is to be hoped that the knowledge of the social sciences – first and foremost sociology – will abandon the excessive self-referentiality that pens all knowledge within its frames of reference and paradigms, without denying the autonomy of the individual disciplines. It should become reflexive knowledge, capable of promoting the construction of connections in the living environments of subjects and between subjects. It should overcome Comte’s “social physics” to lay the (theoretical/empirical) foundations for

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interventions that can bring about positive transformations at both an individual and a social level that in turn translate into “knowing how to live”. Society is ever-changing and moving more and more towards globalisation. According to some scholars, the challenge posed by these transformations brings two orders of questions for sociology (Ossewaarde, 2007). On the one hand, worldwide integration represents a threat to both citizenship and the new sociology; on the other hand, we can glimpse new possibilities for returning sociology to the “public” of world citizenship, calling for its “reinvention” in the form of a “new sociological imagination” (Fuller, 2006; Solis-Gadea, 2005). In this logic, sociology (in particular) and the other social and human sciences (in general) must play a pivotal role in establishing (first) and maintaining (later) the integration between these aspects. To study socio-cultural phenomena, it is necessary to consider an integrated interweaving of factors, disciplines, and methodologies. Sociological knowledge and that of the other social sciences must come together in a single integrated system of knowledge (integral social sciences). This new system must focus on all aspects of the transformation of society (in a holistic sense: aspects of personality, society, and culture) without neglecting the reflexivity on the activities of the researchers themselves. These reflections on the activities of social researchers lead to speculating the need for an increase in knowledge, particularly in a scenario in which complexity increases and the demarcation of the territory to which to address actions is less and less precise, also due to the pervasiveness in the lives of individuals of the mass media and the globalisation processes. The ongoing crisis (economic, social, and also cultural) has disintegrated the social fabric, which needs to be reconstructed for the citizens’ well-being. It imposes a rethinking of the role of sociology and that of the social scientist – which can be understood as intermediate between the civil role and political role of the individual (Arendt, 1958). Sociology scholars must pay close attention to all aspects of the transformation of society, not only to specific sectors. Actions cannot be exclusively technical. They cannot consider the understanding of reality as a given and thus exercise control over it. Instead, they must contemplate reflexivity also on one’s own activities. Knowledge helps to break through the wall of the complexity of problems and situations and allows for a better combination of the objective and subjective dimensions.

FROM INTEGRATED METHODOLOGY TO DIGITAL RESEARCH METHODS As mentioned, we are dealing with a continuum of interdependencies that spans from theory to practice, to impact and action research. Such a continuum is indispensable for acquiring knowledge by reading social phenomena with the participation of the actors themselves. In turn, this helps to translate theoretical premises into concrete actions. The general issue of the relationship between theory and praxis has accompanied sociology since its positivist phase (Comte and Durkheim). However, the prevalent empirical content of sociological knowledge does not solve the problem of its practical translatability, nor does it clarify the ambivalent role of the researcher in being both an actor and an observer of the phenomenon under investigation. These aspects push researchers to ask themselves what is the most suitable methodology that bridges theory and action, considering research as a tool to expand the capacity to describe the phenomenon on the base of three levels indicated from Homans (1967) - explanation, understanding and prediction. To study social phenomena, the approaches and relative methodologies should integrate the subjective and objective dimensions, where the binding element is the interpretation and construction of reality through the relations between individuals, and between individuals, society, 17

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and culture. Social phenomena must be related to individuals and the latter must be seen as agents of interaction; their treatment must be a joint and interactive action between the world of everyday life and institutions – hence the need for studies that take into account both qualitative and quantitative aspects. The need to move towards an integrated system of knowledge in the study of social and cultural phenomena is inherent in the very complexity of the phenomena themselves. It follows the need for an appropriate methodology for the study of social change, the consequences and causes of which cannot be attributed to a single variable but multiple interconnected factors (individual, social, and cultural). The complexity of the object of study “forces” the researcher to deal with different methods and techniques of investigation. These can be traced back to different approaches which, integrated, allow for a complete analysis. The peculiarities of the phenomena push researchers to use certain methods and research tools rather than others. Following this logic, research activities are developed mainly through two different methods. On the one hand, methodologies that collect and analyse qualitative data: in-depth interviews, narratives, network analysis, focus groups, interviews with “key informants”, ethnographic observation (Dumez, 2016). On the other hand, methodologies that collect and analyse quantitative data (e.g., official sources on the demographic size of the population, crimes, surveys, etc.). What often becomes distinctive is the type of processing and treatment carried out on the information. The results of these methodologies, taken together, produce applicable knowledge for the support, activation, reflection, and consolidation of innovation processes that affect the whole of society. Following this logic of qualitative/quantitative integration (Branner, 2016) means that the methods are not on opposite poles. Instead, they allow researcher to observe from different angles aspects of the phenomenon investigated, granting a more effective reading of its complexity. The use of diverse methods is upheld not only by the need to integrate dimensions but also by the impossibility of using a single method to study complex social and cultural phenomena. True, the complexity of social and cultural phenomena is not new – it has been known since the very birth of sociology (studying the whole and not the parts). Since the motivations behind the use of an integrated methodology for the study of social phenomena are now clear, it remains to be specified that the integration of methods also implies the integration of the instruments used for the investigation. To study a social phenomenon, it is necessary to consider an integrated mix of factors, disciplines, and methods of investigation. Following the logic of integration not only does not exclude different methods but also manages to keep together theory, empirics, and practice. It means granting sociology a prominent role as a science capable of providing not the solution, but the possible paths to take to ensure that the socio-cultural phenomenon studied has minimal negative effects on people’s lives. The methodology, today, goes far beyond the mere integration of methods – or mixed methods (Watkins & Gioia, 2015). The contingent hyper complexity of contemporary society cannot disregard a critical approach to the phenomena. Part of this hyper complexity is because contemporary society is configured as a platform society (van Dijck, Poell, & De Waal, 2018). Scholars must address these aspects, which relate to the study of socio-cultural phenomena, but also the methods and tools to be used given the vast amount of information available in the endless Web. The advent of the platform society has entailed not only the transformation of one-to-one, or oneto-many, or many-to-many relations and interactions. Consequently, it also prompted the redefinition of paradigms more suited to the study of a society in constant change. The pervasiveness of the mass media – particularly of the computer that, from mainframe has become first personal, then portable, and then smart – has transformed from object of study to a communication tool. Such a tool inspires social research through a variety of techniques gathered under the umbrella term digital research methods (Rog18

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ers, 2013). This terminology refers to the set of techniques and methodologies using the web as a channel for acquiring information, i.e., all those tools that exploit the mediation of the Internet for data collection. In the platform society, the use of digital methods has grown massively, extending to every kind of investigation. A pointedly qualitative method such as ethnography becomes netnography (Kozinets, 2010, 2015) intending to open new perspectives for studying the interaction and relationships between certain variables (e.g., gender, sexuality, ethnicity, religion, etc.) and cultural changes (Masullo, Addeo & Delli Paoli, 2020). This type of study can include the use of digital research methods to observe, for example, the social relations of parts of populations within different cultures or subcultures within the same social system. Or they could employ them to carry out studies in restricted social contexts (for example, some closed social communities – see the media ecology mentioned in the introduction). The choice is, of course, linked to the discipline of the social researchers, their research hypothesis – and, sadly, also to the available funds. These methods allow for new perspectives while also having inherent weaknesses, as is the case with all so-called traditional methods and techniques.

STRENGTHS AND WEAKNESSES OF DIGITAL RESEARCH METHODS The new complex social reality stemming from the great transformations of the 18th century led to the inclusion of ever-larger categories of phenomena as the object of study of the social sciences – new autonomous sets of knowledge. Sociology was among these. It broke with tradition by bringing the analysis of phenomena back to experience. Man became the homo sociologicus, i.e., a subject acting within a dense network of social relations. However, the development of sociology, like that of many other disciplines, has been far from linear. It has known two opposing currents of thought: one advocated a discipline closely related to the natural sciences and embracing their research methods (quantitative methods); the other supported its full autonomy and independence from the natural sciences (qualitativeinterpretative methods). The terms of this querelle between positivist empirical methods and interpretive methods are still the same (see previous sections). However, today we must face a new debate centred on digital research methods. It can (perhaps trivially) be summarized as follows: some researchers are fully in favour of such methods while others remain very sceptical about their use. I will try to point out the strengths and weaknesses that the use of these methods may entail – keeping in mind that, today, researchers cannot avoid re-configuring or re-designing their methods and tools according to the continuous technological changes of contemporary society. This constant evolution in research is necessary for two reasons: 1) for an adequate reading of the same changes; and 2) to refine methods and tools for the research activities themselves. To help identify the limits and opportunities of digital research methods, I will use the SWOT analysis, usually employed to build a hierarchy of interventions in territorial development by analysing single strategic areas. In this case, the strategic area considered is precisely that of digital research methods and the territory is the Web. Before proceeding, it is necessary to briefly describe the functioning logic of the SWOT analysis, the acronym of Strengths, Weaknesses, Opportunities and Threats. Strengths identify in-Web positive aspects and Weaknesses the negative ones. Conversely, Opportunities and Threats concern elements external to the web – positive and negative, respectively. Planning a research design involving digital research methods must focus on the strengths (enhancing and multiplying them) and weaknesses (counteracting and remedying them). The opportunities and threats, instead, cannot be controlled or influenced by the techniques that the researcher will use, but can contribute negatively 19

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or positively to the collection of data and information. The application of the SWOT analysis to digital research methods is presented in Table 1. Table 1. SWOT analysis for digital research methods STRENGTHS

WEAKNESSES

Geographical unit extension Big data Comparative research

Duplication of data Limited to digital users Lack of symbolic clues

OPPORTUNITIES

THREATS

Narrowing the digital divide Cost containment

Ethical and privacy issues Object of study

Source: Author’s Elaboration

The construction of a research design is made up of the set of basic choices one wishes to make. It must, therefore, be carefully spelt out, essentially taking the form of a “declaration of intent” on these choices. The design of the research will see the illustration of the global objectives which, in turn, will be broken down into several specific objectives. Achieving the latter as a whole allows for the attainment of the former. While every researcher who employs these methods well understands the strengths and opportunities, perhaps a few more words are required for the weaknesses and threats. I discussed the object of study at length in the previous pages, so I will simply point out that it must be extremely clear in its aims, trying not to make research an end in itself, but with positive effects on the transformations of society. One of the major risks of the use of digital methods is closely linked to one of its strengths: data duplication. Digital methods entail using a vast amount of data (big data) that cannot be controlled both for ethics and privacy and simply because there are so many of them; this can lead to using the same “profiling” within a research or for more than one. For example, how many individuals have two or more profiles on each social platform? It is an uncontrollable element which, however, if multiplied by large numbers, makes one question the validity of the research findings. Another weakness is that the use of digital methods reaches only digital users, leaving behind those segments of the population unable to access these means (e.g., indigent, elderly). Finally, as Thompson (1995) said about communication, the use of technologies mediates the interaction by removing all the symbolic clues found in face-to-face interaction. I will now try to reach a final synthesis. I am neither apocalyptic nor integral. I do not aim to solve the debate on digital methods but shift its focus from a mere yes/no binary polarity to the appropriate use of these methods depending on the object of the research and its global and specific objectives. The rapid social changes inevitably lead to considerations about the role of sociology and social scientists in interpreting social transformations and global society in general. Along with the development of a new way of thinking that involves the organisational structures of the world’s great institutions, sociology seems to face difficulties in interpreting these transformations. It is perhaps “perched” on positions of excessive self-referentiality – so-called “sociologism”, corralling all knowledge within its own paradigms. And yet, in this ever-changing context, sociology can play a pivotal role as the science of society. Whether order

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was the prevailing study variable in traditional societies, disorder marks contemporary societies, forcing a redefinition of paradigms and methods - and, therefore, of the tools to be used. If digital methods allow for an in-depth study of the interaction between certain variables (gender, sexuality, ethnicity, religion, etc.) and cultural changes that would not be possible in other ways, they are welcome. The choice is, of course, up to the researchers, who remain bound by the elements highlighted above without losing sight of the original status of the social sciences.

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Goldthorpe, J. H. (1997). The Integration of Sociological Research and Theory. Rationality and Society, 9(4), 405–426. doi:10.1177/104346397009004002 Goldthorpe, J. H. (2000). On Sociology: Numbers, Narratives, and the Integration of Research and Theory. Oxford University Press. Gouldner, A. W. (1970). The Coming Crisis of Western Sociology. Basic Books. Homans, G. C. (1967). The nature of Social Science. Hartcourt. Kozinets, R. (2010). Netnography: Doing ethnographic research online. Sage (Atlanta, Ga.). Kozinets, R. (2015). Netnography: Redefined. Sage (Atlanta, Ga.). Mangone, E. (2009). Il “lavoro sociale” del sociologo tra dimensione oggettiva e dimensione soggettiva. Salute e Società, viii(3, suppl. n. 3), 155–160. doi:10.3280/SES2009-SU3012 Mangone, E. (2018). Social and Cultural Dynamics. Revisiting the Work of Pitirim A. Sorokin. Springer. Masullo, G., Addeo, F., & Delli Paoli, A. (Eds.). Etnografia e Netnografia. Riflessioni teoriche, sfide metodologiche ed esperienze di ricerca. Paolo Loffredo Editore. McNiff, J. (2013). Action Research: Principles and Practice. Routledge. doi:10.4324/9780203112755 Mills, C. W. (1959). The Sociological Imagination. Oxford University Press. Ossewaarde, M. (2007). Sociology Back to the Publics. Sociology, 41(5), 799–812. doi:10.1177/0038038507080437 Piaget, J. (1972). L’épistémologie des relations interdisciplinaires. In L’interdisciplinarité: problèmes d’enseignement et de recherche dans les universités. OCDE. Accessed from, http://www.fondationjeanpiaget.ch/fjp/site/textes/VE/jp72_epist_relat_interdis.pdf Polanyi, M. (1958). Personal Knowledge. Towards a Post-Critical Philosophy. Routledge. Rogers, R. (2013). Digital methods. MIT Press. doi:10.7551/mitpress/8718.001.0001 Schütz, A. (1946). The Well-informed Citizen. An Essay on the Social Distribution of Knowledge. Social Research, 14(4), 463–478. PMID:20285192 Solis-Gadea, H. R. (2005). The New Sociological Imagination: Facing the Challenges of a New Millennium. International Journal of Politics Culture and Society, 18(3-4), 113–122. doi:10.100710767-006-9008-7 Sorokin, P. A. (1955). Testomania. Harvard Educational Review, 25(4), 199–213. Sorokin, P. A. (1956). Fads and Foibles in modern sociology and related sciences. Henry Regnery Company. Sorokin, P. A. (1962). Society, culture, and personality: Their structure and dynamics, a system of general sociology. Cooper Square. (Original work published 1947) Sorokin, P. A. (1965). Sociology of Yesterday, Today and Tomorrow. American Sociological Review, 30(6), 833–843. doi:10.2307/2090963 PMID:5846305

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Sorokin, P. A. (1998). The Boundaries and Subject Matter of Sociology. In B. V. Johnston (Ed.), Ptirim A. Sorokin. On the Practice of Sociology (pp. 59–70). University of Chicago Press. Thomas, W. I., & Znaniecki, F. (1918-1920). The Polish peasant in Europe and America: A Classic Work in Immigration History. The Gorham Press. Thompson, J. B. (1995). The Media and Modernity. A Social Theory of the Media. Polity Press. van Dijck, J., Poell, T., & de Waal, M. (2018). The Platform Society: Public Values in a Connective World. Oxford University Press. doi:10.1093/oso/9780190889760.001.0001 Watkins, ‎D., & Gioia, D. (2015). Mixed Methods Research. Oxford University Press.

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The main platforms are the following: Facebook, Apple, Microsoft, Alphabet-Google, and Amazon, hence the acronym FAMGA). In order to understand this aspect, it is sufficient to take an overview of sociological research from the beginning of the last century to the present day to see how the object of study has changed. To this form, Burawoy adds three others: the professional form refers to academic sociology, articulated in theoretical speculations and empirical research; the critical form refers to the study of the trajectories of scientific knowledge, which also verifies its effects on society; and, finally, the policy form refers to the answers given by third parties who commission empirical research to guide new actions or projects.

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Chapter 3

Going Digital:

Challenges and Opportunities for Social Research Methodology Felice Addeo University of Salerno, Italy Valentina D’Auria University of Salerno, Italy

ABSTRACT The digital society is a research object that still lacks a clear and shared definition, as it is always in progressive and whirling transformation. From a methodological point of view, digital society is then a fruitful ground for experimentation and innovation. However, the unceasing flourishing of online social practices and the innovative ways to frame into data the online activities of individuals make the knowledge drawn from the web always uncertain, revisable, and at high risk of obsolescence. Social research tried to face the challenges posed by the digital society first by adapting the established social research methods to the new digital environments and then creating new ones. Neither approach has been able to define which are the most valid and reliable methodological tools to study the digital society, nor to draw a shared vision that would allow social research to advance. This chapter discusses the challenges and opportunities that the digital society poses to social research methodology and reflects on the need for new epistemological and methodological positions.

AN INTRODUCTION TO SOCIAL RESEARCH IN DIGITAL CONTEXTS The society in which we live is permeated by technology, which mediates most interpersonal relationships, exchanges of ideas, official or personal communication. Thanks to the fast development of technological devices and to the spread of the Internet, huge amounts of data, content and information broke through the barriers of the private sphere to become increasingly available, shareable and public. Nowadays, online contexts have become increasingly prolific with data and information accessible to (almost) everyone, so interest in them from a social science research point of view can be said to be DOI: 10.4018/978-1-7998-8473-6.ch003

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growing exponentially; but it was not always so. In fact, it is possible to trace two phases that describe the relationship between social sciences and the study of the Internet. In the beginning, social sciences looked with suspicion at everything related to the Internet, which was considered as a subordinate context and not worthy of study. The main reasons for this attitude were mainly linked to the way people were used to stay on the Internet at the beginning of the digital era: very little information was shared, even less was socialized, the identities of users were hidden behind nicknames and avatars. Thus, social spaces were considered, both by ordinary people and by social researchers, as contexts detached from reality, lacking any link with it. This period is referred to by Rogers (2013) as the period of cyberspace, where cyberspace was defined as a “space without place”, where everything that happened had nothing to do with the real world. Thanks to the changes in the use of new technologies, the enormous diffusion of smartphones and PCs to stay “always connected”, the emergence of the Social network with the need to create “real accounts” on social media (such as Facebook, Instagram, Twitter, and so on) with public identities and no longer hidden by nicknames, the creation of ad hoc platforms dedicated to buying and exchanging review and opinions on the items or the services bought (e.g. Amazon, Booking, TripAdvisor, and so on), there has been a change of direction whereby the social sciences have started to take an increasing interest in these contexts. These developments have also made it possible to go beyond the real-virtual dichotomy to embrace a broader and more elastic concept of reality: an environment where the real and the virtual are closely interconnected through a flow of events that call on each other. Rogers notes: “virtual interactions supplement rather than substitute for the real and stimulate more real interaction, as opposed to isolation and desolation” (2013: 20). We are well aware that social actions are linked and contaminated, in many ways, by the new digital devices, their infrastructures and their respective uses, so social research is called upon to consider, both epistemologically and methodologically, digital environments as real socialization contexts, within which it is possible to measure, analyze and study social dynamics (Marres, Gerlitz, 2016; Marres, Weltevrede, 2013, Amaturo, Aragona, 2016). In fact, it is precisely since the emergence and spread of social media that online and offline realities have stopped being two separate contexts and have become a single context with increasingly blurred contours. Today, moreover, social media are the greatest example of a participatory web (O’Reilly, 2005), where users stop hiding behind fictitious identities to share ideas and produce content in broad daylight. hence the expression “prosumers” coined by Toffler (1980). So, the social research began to be very interested in such contexts, which were first considered as socialization contexts in their own right, almost like a separate “virtual society”, and then as extensions of real contexts where users engage in amateur content production (Rogers, 2015). The Web began to be a fertile ground for the social sciences, which sought from the outset to study its contents, the dynamics and the contexts of exchange, the functioning of online infrastructures, and to use it as a tool for data collection. In other words, social research can benefit twice from the study of digital networks: they can be an interesting research object and, at the same time, a good way to collect information about subjects and practices thanks to the use of computational tools and software. Studying digital society has no longer meant moving away from reality, as it was once thought by the majority of scholars. On the contrary, the new digital contexts have given researchers the possibility to study both old and new topics that have always been addressed by social research, such as personal identity, power relations, inequalities, social dynamics and much more. Social sciences have made an effort to adapt their offline analytical tools to purely online contexts and, subsequently, this effort has been funneled into the possibility of building ad hoc tools to study the web. However, neither the adaptation of traditional techniques nor the creation of new ad hoc tools for 25

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the study of new digital contexts seem to be the only valid and reliable way for social science practice. In the first case, traditional techniques are often considered rigid and unsuitable for the processing of such complex information in digital contexts; on the other hand, new tools for studying digital societies are often extemporaneous attempts, likely to become obsolete in a very short period of time. With the expression “Digital Methods” we attempt to contain in a single label the changes that social research has implemented to study the data created and spread on the web. As a matter of fact, to date there is still no shared definition that can well encapsulate the expression Digital Methods within delimited boundaries, nor can the concept of digital society ever be clearly delineated. Again, the label “digital methods” stems from the challenge of overcoming the dichotomy “studying technology” and “studying society” (Marres, 2017), with the hope of being able to integrate it into a single and coherent framework, where one no longer has to choose to analyze one or the other, but to converge the analysis towards a single perspective: studying society within the dense network of online and offline contexts, where exchanges that take place in one continue in the other; where events that happen in one have consequences in the other. In the light of the above, one can clearly imagine how valuable these new digital contexts are as a resource for the social sciences and, at the same time, as multifaceted and heterogeneous environments that are extremely complex to frame. Indeed, Addeo and Masullo (2021) agree on the twofold awareness of the digital society: on the one hand, it is certainly fertile ground for new methodological experiments; on the other hand, the perpetual birth and development of new socialization and communication practices within digital contexts make web knowledge increasingly uncertain and revisable, at high risk of obsolescence. However, digital data are complex and problematic, they imply a deep knowledge not only of the Web and its infrastructures, but also of the tools useful to study them. Indeed, Rogers (2019: 3) defines digital methods as “research strategies for dealing with ephemeral and unstable nature on online data”. It is not only the different use of the web and the increase in online data sharing that has brought about a deep change in the social sciences, but it is the social, cultural, historical and economic dynamics, in a broader sense, that have contributed strongly to the creation of a new paradigm in social studies. It is, in fact the succession of events happening around us that caused fractures, sudden changes, accelerations, both in our daily lives and in the world of science. For example, the pandemic event that unexpectedly struck the entire globe in 2020, COVID-19, that has disrupted our routines, bringing with it uncertainty and bewilderment for our health and our future. The COVID-19 pandemic has brought about numerous changes that will continue over time both in our daily lives and in other contexts and fields of human activity. One thinks of the measures that will be taken to deal with the economic and financial crisis caused by the pandemic, of the cuts in public spending that will create new gaps in access to education and research (IIEP & UNESCO, 2020; Bania & Dubey, 2020; Schleicher, 2020). Indeed, we are sure that social research will not be spared by these changes. To deal with such a changing research object, social research on COVID-19 is certainly not standing still in its attempt to understand the pandemic as a social phenomenon, and consequently to generate knowledge-based responses that can be useful to institutions and citizens. Another Pandemic “factor” that has impacted on social research is the shift of all research activities (and meetings related to it) from the offline to the online context, for reasons related to the initial lockdown and subsequently the rules of social distancing. In fact, since the advent of the pandemic, meetings and activities essential for research have almost always taken place online, due to mobility restrictions. This condition is certainly compromising the implementation of existing studies, especially qualitative research work such as interviews, focus groups and other techniques based on the co-presence of researcher and respondent. On the other hand, the web was an invaluable tool during the crucial months 26

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of the pandemic, as social research could not have gone ahead without it. For this reason, quantitative research has largely benefited from the changes caused by the COVID-19 pandemic. Important topics to be studied and explored during the pandemic would have been missed opportunities for the social sciences. Moreover, those involved in Internet studies during the pandemic were much more likely to participate and contribute to knowledge creation than at any other time. The interruption of many activities, the obligation to stay at home, and smart working activities meant that people spent much more time online and, together with the uncertainty of the moment, allowed them to share their thoughts. In this way, research in general, and social research in particular, has benefited from the contribution of people who have volunteered to participate in web-based surveys. In addition, using the web as a communication vehicle not only generated shared knowledge useful for research, but also allowed users to feel less alone, sharing their fears and thoughts during a difficult and uncertain period such as Covid-19. Several research (such as Addeo, Punziano, Padricelli, 2021), in fact, demonstrated the enthusiasm with which people agreed to participate in COVID-19 investigations, sharing their habits during the lockdown, their thoughts and feelings, just as if it were a therapeutic act. In this case, the web has been a means of sharing and richness for both researchers and ordinary people. Widening this point of view, it is necessary for social research in general to take the opportunity to always put itself at stake with new strategies of analysis, and not to fossilize in prejudices that make it sterile and far from reality. After all, being able to appreciate the fertility of new social contexts, to exploit them to one’s advantage no longer means moving away from reality, but means being part of it. In this respect, it is useful to recall the ontological distinction between digitised and natively digital data. The latter are ‘born’ spontaneously in the digital context (think of social networks, where natively digital data are likes, hashtags, views, stories, etc.), while the latter generate from the offline context and subsequently ‘migrated’ to the online context. This distinction opens a reflection on the method for internet-related research (Rogers, 2013). This distinction opens up a brief reflection on the method for internet research (Rogers, 2013). Some native digital data (engine query logs) imply a deeper knowledge of digital contexts and ad hoc tools to analyze them. For instance, if a researcher is interested in analyzing huge amounts of data, it is necessary to first have an automated system to extract them from a specific platform, and knowledge of a specific programming language to process them. The main solutions are: making use of already extracted data (in existing databases), using free software (e.g. Netlytic), scraping techniques to collect data directly from the HTML code of the platform, or through API (Application Programming Interface) querying techniques (See Caliandro, Gandini, 2018; Bennato, 2015). Following this, paragraphs will be devoted to reflections on research approaches linked to the Internet and digital contexts, with the aim of highlighting the peculiarity of the new challenges they pose to social research. In particular, the first section will deal with the topic of Big Data, a topic that is actually in vogue in the sciences (including social science). In this regard, the major limitations, both epistemological and methodological, that characterize this approach will be discussed. Secondly, a brief description of the approach known as “Small Data” will be addressed, focusing on two methods: the web survey and netnography. The Small Data should be considered as an approach embedded in the digital context like the Big Data as well. Because, by analyzing strengths and weaknesses of both type of data, they could be considered as complementary research tools rather than opposing frameworks.

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SOCIAL RESEARCH AND BIG DATA: OPPORTUNITIES AND LIMITATIONS As already partially anticipated in the previous paragraph, the use of the Internet and the development of new technologies has changed over the last decades: we have gone from sharing a few data in a hidden and occasional way to sharing huge amounts of data on a daily basis by individuals who are identifiable and traceable at any given time. When we talk about data that are immediately available and usable in the digital context, we are referring to all the information collected online on a daily basis: these are our traces that we leave on the web, the footprints of our passage, starting from the information shared on social platforms (likes, comments, tweets, shares, etc.), to the searches on search engines, our GPS-located movements, the websites we visit, the products we view and buy, and much more. From this perspective, social media themselves are nothing more than spaces producing metrics (Caliandro, Gandini, 2019). Social platforms, together with the algorithm that defines their functioning, produce numerical indicators for the analysis of social interactions and other socially relevant issues. Likes, comments, shares, reactions, network of followers or friendships, reviews, use of hashtags are considered natural indicators in virtual contexts such as social platforms. These just described are only a small part of the data collected online on a daily basis. These types of information are recognized by the community under the name of Big Data, i.e., the set of data in digital format that are collected, stored and managed through large datasets and that can only be processed through specific software and hardware systems (Lombi, 2015). In addition to the main characteristics (volume, velocity, variety and comprehensiveness) that are used to describe Big Data (Kitchin, 2014), there is a further characteristic that makes them much more interesting: their relationality, i.e., the fact that they do not constitute isolated or isolatable data but are in effect a huge ecosystem of interrelated data. The goal of analysis using Big Data is to grab and understand the complexity and vastness of information without having to give up even the smallest part of it. In fact, they are not created from a specific question, but can be considered in effect as a by-product of our online activities. Their ambition is to achieve exhaustiveness, to be continuously updated and timely, and therefore always in the process of being defined. However, the dynamism due to the continuous updating activity, which on the one hand is the strength of Big Data, is at the same time its Achilles’ heel. In fact, this dynamic can create confusion and uncertainty in the field of research, when data is extracted for analysis. This huge amount of data circulating on the Web and the way it can be accessed give research a valuable opportunity that was not possible before: analyzing and understanding reality through a completely new lens. Therefore, the data arising from to the digital context are considered for social research as a new type of empirical data: they are no longer information collected through the active collaboration of the subject (as happens in interviews or questionnaires); rather, they are collected in a non-intrusive manner and without the slightest participation of the subjects, other than to produce online activities themselves (Molteni, Airoldi 2018). However, it is crucial to ask whether social research is prepared to face these changes and to adapt itself and its knowledge tools to new research scenarios. Indeed, many scholars (Daboll, 2013) are highlighting the big issues that social research are facing when dealing with new aspects of the empirical reality offered by Big Data: first of all, the complexity of managing such a large amount of data, and then the urgency of reviewing traditional ways of doing research; then, the need to develop specific skills in data management and analysis. One of the most relevant aspects in this respect is the relevance of knowing very well the digital environment in order to obtain a deep and accurate knowledge of it. In this sense, when studying a virtual context, it is necessary to take into ac28

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count the role of affordances (infrastructural features of digital media) in the observed digital context, because they condition the digital environment in which individuals behave and interact. In other words, the structure of a website will allow the user to perform certain actions rather than others, this will help to collect a certain type of information at the expense of others. In addition, Big Data has had an enormous impact on the methodological and epistemological aspects of social research, so much so that at first it was feared that there would be a radical change in research practices with a consequential doom to obsolescence of the entire traditional methodological apparatus of the social sciences (Savage, Burrows, 2007). Although predictions of a paradigm shift and disruption of social research practices have not come true, the social sciences have not remained immune to the changes brought about by Big Data. While on the one hand the enthusiasm of possessing huge amounts of data has helped social research not to fossilize on the most common methodological approaches and to experiment with new approaches or to rediscover marginal ones (for example, network analysis, content analysis, sentiment analysis, digital ethnography, and so on); on the other hand, one must consider that in the case of Big Data not all that glitters is gold. In fact, several scholars have put forward a number of interesting reflections that reveal the precarious and controversial nature of Big Data in social research. Running the risk of oversimplifying the issue, two problematic dimensions arise with regard to Big Data: one relating to its nature, and the other relating to its use. Firstly, with regard to the nature of Big Data, it must be said that its complexity is difficult to manage, mainly due to the limited tools available and the lack of expertise in the field, so there is often a tendency to oversimplify complex models. Moreover, the vast amount of information available online risks making any research conducted on a large scale (i.e., with a relevant sample) too small in any case (Daboll, 2013). Second, another controversial aspect regarding the use of big data is related to ethical issues. As we have already partially anticipated, data are collected continuously and without the deliberate authorization of users. This action is legitimized by the fact that big data are huge amounts of mostly technical and anonymous data that can be analyzed in aggregated form. However, personal and sensitive data of users would also end up in the Big Data cauldron, so that it would be necessary to deal with these data with the authorization of the persons concerned. In addition, the data collected online are (partly) public and accessible, and thus available to be used for research purposes. As Boyd and Crawford state: “it is problematic for researchers to justify their actions as ethical simply because the data are available to the public.” (2012: 672). In other words, how could users be guaranteed the right to privacy? How can it be considered ethical to secretly collect users’ data and leave them at the mercy of commercial or other potentially harmful research (e.g., research aimed at influencing users’ political and electoral opinion)? The proposal is to design personal data systems that give everyone the possibility to manage their personal information and interchange with public and private entities, promoting greater awareness among users. According to Stefanizzi (2016), it would be necessary to make a change starting from the design of new personal data systems, so that they give everyone the right to manage their personal data in the best possible way. The proposal in particular touches on four key points from which change should start: as a first aspect, what is today referred to as “informed consent” should become real “selfawareness”, i.e. full control and full awareness of the sharing of one’s personal data; the second aspect is “data liberation” or data portability, i.e. the right of every user to do what he or she wants with his or her own data; “oblivio” on the other hand refers to the fact that users should have the right to request and obtain the deletion of their personal data shared on the web, i.e. that they be forgotten by the network; and finally “public good” is the right to benefit knowledge shared by the network. Indeed, the use of 29

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big data has produced an alternative epistemological view in the social sciences, which Kitchin (2014) labels ‘data-driven knowledge making’, i.e. new research practices are increasingly oriented towards a data empiricism. Theory, on the other hand, is increasingly losing its relevance, becoming almost an obstacle to research implementation. This reversal of plan is likely to be a consequence of both an oversimplification of what is complex and a disproportionate reliance on data. Anderson (2008), in a completely provocative way, reflects on the fact that today it is widely believed that data, correlations between variables and quantitative analysis in general outweighs in importance theory, models and hypotheses, which are considered as an obstacle to the production of knowledge. Similarly, several other scholars (Prensky, 2009; Dyche, 2012; Steadman, 2013) have been somewhat deluded by the endless research possibilities promised by the world of Big Data. Not only was research based on theories considered outdated, but so was the formulation of hypotheses on which develop a reliable and valid research design. The social research approach to big data, in this regard, has led to the conviction that it is possible to analyze reality through a boundless point of view, avoiding the loss of information. As Steadman (2012) points out, the Big Data analysis allows an analyst to have a comprehensive view on the issue addressed while making an analysis from a very broad perspective. Thanks to computational tools and large-scale data collection, Big Data analyses provide interesting results without the need to formulate hypotheses or research questions. The solution is to be guided by the data and let it answer. It is enough to interrogate the data and find relationships between them, without the need to formulate starting hypotheses. This would even make it possible to find relationships that would not have been observed through a theory-driven approach. However, as Kitchin (2014) points out, these positions have mostly been expressed to help the dissemination of the Big Data commercial philosophy. Pushing the accelerator on this totally inductivist idea is not to take a different epistemological stance but rather to spread the commercial idea that Big Data can provide solutions even to those who only possess purely technological knowledge. It is precisely this idea that generates the distorting mechanism according to which knowledge of a theoretical and cognitive nature is outdated, as it is no longer necessary, since it is replaced by the technological skills of the researcher and the calculation capabilities of the machine. In spite of the usefulness of Big Data in looking at reality with absolutely new points of view, it should be clear that one should not confuse the volume of data available online with the completeness of the information obtained. Data itself says nothing, but it is the researcher who has to interpret the results. The interpretation is never free from theory, since it is the result of previous theoretical and empirical knowledge that allows the researcher to interpret it as closely as possible to reality, but still influenced by his own view. On the contrary, the enthusiasm created around Big Data has given rise to several beliefs favored by the empiricist position, two of which are: the first according to which Big Data would avoid the use of sampling techniques; the second relating to overcoming the study of causation between several variables in favour of studying their correlation. This distorted view of doing research is mainly attributable to the illusion of having overcome the limits with which social research has always tried to come to terms. In the first case, the vision of the new way of doing research with Big Data appears distorted because the enormous and immediate availability of these makes one think of research without the need to sample data, with the advantage of focusing more on the object of study. This perception could not be further from the truth, first of all because the total and immediate availability of data on the net is a great illusion: they are continuously produced in an endless process, which generates confusion and precariousness, hindering the complete vision of the phenomenon that one hoped to obtain. Moreover, the complete database is partly inaccessible to the researcher, as some data are hidden and not obtainable for ethical, legal, and economic motives (many data are generated by private interests, so 30

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you have to pay for them). As Leonelli notes: “having a lot of data does not mean having all of them; and cultivating such a vision of completeness is a very risky and potentially misleading strategy” (2014: 7). For this reason, the data constitute an undefined set that necessarily requires selection, a sampling that draws boundaries to include a defined and delimited set of data for analysis. Moreover, the untruthful completeness of Big Data is compounded by its deceitful representativeness: the vastness of the data collected creates the expectation that we have obtained data that is representative of the population, but this is not the case. The representativeness of the data collected does not depend exclusively on their size (large does not mean representative), but rather depends on a series of other factors such as: the distribution of the devices used; who accesses them; which platforms they use; the legislative system of that State that regulates the right to privacy; and much more (Kitchin, 2013, 2014). Secondly, the possibility of touching such a vast amount of data created the illusion of managing and analyzing self-evident data, i.e. capable of returning obvious correlations between analyzed variables which necessarily imply causal relationships. However, when the data analysis spots a significant correlation, it does not mean that it is a certain evidence of a causation (as scientists usually say: correlation is not causation) or, worse still, one could run into spurious (i.e. two variables could be linked to each other by a third one of which we are unaware) or meaningless relationships (i.e. with high significance values but no logic). This risk can arise as much in the approach to Big Data as in the research approach to so-called Small Data. For this reason, the contribution of the researcher is a fundamental part in the interpretation phase of the results of a research. Two or more variables may be associated with each other, but this association does not necessarily imply a causal relationship between them. Believing that a correlation between variables implies necessarily also a causal relationship is serious and dangerous, as one risks committing several logical fallacies. Similarly, believing that data processing software can replace the interpretive work of the researcher is both pretentious and reductive for the research itself. From epistemologically naive attitude of empiricists and theorists it seems that they have not learned the lesson of Ronald H. Coase that in 1994, when Big Data was still a faraway reality, in Essays on Economics and Economists stated that “If you torture the data long enough, it will confess to anything” (p. 27). This sums up well the great limitation implicit in the nature of Big Data: huge amounts of data that are highly manipulable, as they lack semantic autonomy. In addition, it is useful to remember the admonition “garbage in, garbage out”, whereby if an analysis is carried out on dirty, incomplete data, it will not only risk not being able to be exploited as intended but will also lead to distorted and unrealistic conclusions.

ONLINE DIGITAL METHODS: FROM QUANTITATIVE TO QUALITATIVE APPROACH Although Big Data is now a new and widespread reality within the social sciences, it must be considered that it is not the only way to do research on the web. In fact, there are several other approaches to the digital that allow the vast and in-depth study of what happens and is shared in digital contexts. Caliandro and Gandini (2017) talk about the Small Data approach, which does not resort to the totality of the data as happens with the big data approach, but on the contrary works in depth, trying to focus on the quality that the digital data allows us to explore. While the former is an approach mainly based on the processing of large datasets collected from the activities of online users, the latter can be considered an approach based on the processing of smaller amounts of data, with the possibility of also moving the analysis to a deeper and more qualitative level, in order to capture the links and relationships between 31

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the dynamics of the observed contexts. The Small Data approach works on specific topics of interest (a specific event such as political elections, or a socially relevant issue such as gender equality, and so on), through targeted research questions or hypotheses. In general, digital contexts are a huge source of information, both quantitatively and qualitatively, allowing the collection and extraction of new types of information for building empirical bases (big data, open data, linked data, etc.). Indeed, the Small Data approach stems from the need to select and manage smaller amounts of data in order to reduce the margin of error caused by overly complex and superficial analyses of the Big Data approach (returning irrelevant or wrong data). In a nutshell, the Small Data does not possess the characteristics of volume, velocity, variety and comprehensiveness inherent in the nature of the Big Data, but on the other hand, it has the qualities of the solid and strong scientific tradition behind it (Kitchin, Lauriault, 2015), along with the elasticity to adapt to specific objects of study (thanks to the formulation of research questions aimed at detecting specific information). With the colonization of part of social research by Big Data, a dark future was forecast for traditional research (such as the interview, the survey), with the conviction that it would soon be completely supplanted by Big Data analysis (Savage, Burrows, 2007). Indeed, from a methodological and epistemological point of view, it was believed that “the intrusiveness of its data collection method, and the non-genuineness of the empirical material collected” (Biolcati, Martire, 2018: 17) would be the main reason for their inadequacy. Such ways of doing research are downgraded to outdated methods and techniques, in favor of more genuine and less intrusive analysis such as Big Data. Two of the suitable approaches in the Small Data paradigm that will be discussed here, one belonging to the quantitative approach and the other to the qualitative approach, are the web survey and netnography respectively. The progressive digitization of institutions, processes, human relationships, and society in general has led to a continuous and incessant collection of data shared online. According to Groves (2011), data collected online constitute a new ecosystem that should be considered as a by-product of the digital society. Moreover, this huge ecosystem is made up of ‘organic’ data, i.e. data born spontaneously on the web, and are considered as well as “now-natural feature of this ecosystem” (ivi: 868). The organic nature of this data contrasts with the nature of the survey data: data designed ad hoc to detect the specific problem being researched. The research design of a survey, through an exquisitely quantitative approach, aims to investigate the existence and intensity of relationships between variables using the collection of specific data structured in a matrix. Indeed, with the advent of digitalization and the spread of the internet, against all odds that other research approaches not related to Big Data were doomed (see previous section), social research has responded with the web survey, an effective approach that involves the self-completion of a questionnaire by selected subjects via the web. In this sense, there are two interesting aspects related to the web survey: The Internet as a vehicle for spreading the survey; and the sample involved in the compilation. In the first case, the web survey has brought the self-report questionnaire back into vogue, which has both advantages (reduced expenditure of economic, organizational and time resources, greater confidentiality reserved for the respondent) and disadvantages (the lack of co-presence among respondents could generate biases such as response sets, as acquiescence, semantic reformulation of a questions, and so on). In the second case, instead, the main problem embedded in the operation of the web survey (which, by the way, it shares with the Big Data approach) is the sampling coverage. The diffusion of an online survey does not imply the creation of a well-inclusive frame: while on the one hand, the large amount of users 32

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present online allows a rapid collection of data, on the other hand the sample obtained will not guarantee the representativeness necessary for inferential purposes. The problems related to the sampling frame can be different from country to country (e.g. countries where the use of the Internet is hindered by law) and from culture to culture (e.g. the phenomenon of digital divide). Thus, there may be areas where there is a poor internet coverage, or inadequate cultural and social predisposition to use technological devices; or there may be whole families and communities that for economic and cultural reasons are cut off from the digital society. This means that a part of the population cannot be included in digital environments. So, all these people are excluded a priori from participating in web surveys which will instead rely mainly on self-selected samples. From this perspective, how can one think of obtaining a representative sample simply by spreading an online survey? Moreover, it is often considered extremely superficial to generalize the results collected online on a convenience sample, just because it includes a large number of respondents. Technically, a sample is representative, and therefore allows the generalization of results from the sample to the population, only if it reproduces the reference population to scale in its selected characteristics (i.e. from certain variables taken into account) (Marradi, 1997, 2007). Related to this issue, another confusing element is often the online panel, i.e. a sample provided by research companies and institutions that remains unchanged over the period of a survey. Indeed, when the web survey is closed (i.e., when it is targeted to a special, pre-selected population), research companies can provide to the researchers an online panel (non-probabilistic sample). While on the one hand, the online panel has many advantages such as saving time resources to achieve a certain number of participants, the possibility of re-contacting the participants several times (to deepen some important issues, or to report any problems), the possibility of doing research through a longitudinal perspective; on the other hand, the sample involved cannot be considered representative just because it is provided by specialized institutions (Biolcati, Martire, 2018). The qualitative approach can be described as an “umbrella approach”, as it contains several other approaches, each of which has a different way of proceeding. This feature represents in part an epistemological limitation that, together with the difficulty (if not impossibility) of replicating empirical studies, have weakened, especially in the past, the role of the qualitative approach in the world of research. Returning to the heterogeneity of the qualitative approach, also defined by Marradi (Ibidem) as a “nonstandard group”, it is characterized by several approaches and techniques that constitute both a weakness (for the reasons listed above) and an epistemological and methodological richness (as it allows to the researcher a high degree of creativity and freedom). These include hermeneutic interviewing (Addeo, Montesperelli, 2007; Montesperelli, 1998), observation, biographical and discursive approaches such as life stories, focus groups and other group research techniques such as Delphi (Bezzi, 2013), anthropology, ethnography and its online counterpart, netnography (Addeo et al., 2020). Here we will address in particular a reflection on the limits and possibilities of netnography. Of all the others listed, the netnographic approach is the most coherent with the content of this chapter, since it must be read as one of the efforts that social research has made to adapt one of its canonical methods (the ethnographic method) to the new online spaces. The netnographic research method is a purely qualitative method that adapts traditional ethnographic techniques to the study of the ‘net’, i.e. online communities, practices and cultures formed through computer-mediated communications. In line with the non-standard set, it does not have specific research procedures, but this approach is in constant flux towards digital environments, their affordances and dynamics within them. This “empirical vagueness” is also reflected in the nebulousness of terminology and in the fact that there is still no unambiguous direction to recognize this approach. Among the most common labels are: Netnography (Kozinets, 2002, 33

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2010, 2015); Cyber Ethnography (Morton, 2001), Ethnography of Virtual Spaces (Burrell, 2009), Internet ethnography (Boyd, 2008), Ethnography on the Internet (Beaulieu, 2004), Internet related ethnography (Postill, Pink, 2012); Digital Ethnography (Murthy, 2008), Webnography (Puri, 2007). Both ethnography and netnography are naturalistic and non-intrusive approaches interested in studying social practices in their everyday context (Kozinets, 2010). Their object of study is human interactions, the behaviour of subjects within specific contexts, such as habitual and/or familiar environments. The study takes place through engaged observation of social dynamics with the aim of understanding and interpreting different cultures, points of view and ways of being in the world and obtaining a deep and rich description of them. Furthermore, netnography is characterized as a flexible and elastic approach, whose exploratory nature allows one to remain open-minded about issues that emerge in the field, rather than being guided by specific research questions (Varis, 2016). The peculiarity of the ethnographic method is the non-intrusive analysis of social dynamics in online spaces. The digital contexts observed (Facebook groups, Instagram pages, Twitter profiles, Blogs, Forums, etc.) offer specific opportunities to the researcher: first of all, immediate access to “natural” environments where groups of people share the same ideas, or the same topics of interest, where they interact and exchange opinions. These online communities offer the researcher the possibility to analyze the salient contents of these conversations through the analysis of posts, comments, hashtags, shares, and so on. Far from wanting to provide an extreme simplification of the topic, here we will only focus on a few aspects related to netnography, in particular the most controversial aspects of a netnographic research design, namely the type of access to the field and the level of participation of the researcher. The researcher can access the field by making an application for closed communities or by joining open communities independently. Access to the field can be overt or covert, at the discretion of the researcher, the type of community and the phenomenon studied. However, this choice can be controversial. Covert access may have benefits in terms of obtrusiveness but has ethical and privacy implications. Instead, overt access is ethical correct but obtrusive. On the one hand, there are those who support the choice of overt access for reasons of research ethics: a researcher should always disclose his presence and study intentions to the online community (Kozinets, 2010; Hine, 2005); on the other hand, there are those who forgo ethics for less intrusiveness and more genuine research (Hewer, Browmlie, 2007; Beaven, Laws, 2007; Langer, Beckmann, 2005), as they believe that informing community members about the identity of the researcher would compromise the main advantage of the approach, namely its discretion. The other unsolved issue of the netnographic approach is that related to the participation of the researcher in the vital dynamics of the communities. On the one hand, there are scholars who argue that the researcher should limit himself to hidden observation (technically called “lurking”) in order to avoid polluting the studied context (Beaven, Laws, 2007; Hewer, Browmlie, 2007; Puri, 2007; Langer, Beckmann, 2005). Lurking, in fact, offer a unique opportunity for collecting “natural” data, as the members are not aware of their informant status and do not modify their behaviour due to the researcher’s presence (Addeo et al., 2020). On the other hand, there are those who devalue the practice of lurking as a passive and superficial practice that does not give the researcher the real possibility to integrate and study in depth the community of interest, besides being ethically harmful (Beneito-Montagut, 2011; Hine, 2005; Bell, 2001; Heath et al., 1999). As we could see also for the web survey, no technique or approach is free from limitations or threats. The netnographic approach is valuable to study closely the new online communities, considered as extensions of our offline socializing activities. Often online communities are even given added value, as they give users the possibility to share interests with geographically distant people, to increase their knowledge, to form relationships, and much more that would not have 34

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been possible without the web. Thanks to netnography, the researcher can analyze information on the web through a purely qualitative, often theory-driven approach. The web survey, on the other hand, by means of an openly quantitative approach, aims at collecting a large amount of information in order to process it in a database, in the form of variables, and extract any relations between them. They are different ways of doing research and analyzing reality, each of which is adapted to a specific research objective, not without managing the problems and limitations that each approach possesses. In conclusion, the qualitative approach partly overcomes the limitations inherent in the quantitative approach, while not prefiguring it as the absolute best approach. In fact, the qualitative-quantitative dichotomy is now considered by many scholars as outdated, so currently there are no longer any distinctions of merit between the two approaches (Marradi, 2007; Cresswell, Tashakkori, 2007; Amaturo, Punziano, 2016). The qualitative and quantitative approaches (also known as the “non-standard approach” and “standard approach,” respectively) are now considered different and complementary. In fact, the quantitative approach proceeds through the decomposition of individuals into distinct properties (i.e. the characteristics of interest to the study) that will be inserted into the data matrix in the form of variables (through the operational definition). This way of proceeding is this identified by Marradi (Ibidem) as the “assunto atomista”, i-e atomistic assumption. It is also useful to objectively quantify the data collected and look for possible relationships between the variables analyzed. In other words, the quantitative approach is first and foremost a “variable-oriented” approach, meaning that the majority of quantitative studies follow this approach. The strong focus on numbers, together with the rigid structuring of processes, does not allow for the possibility of delving into contextual elements of the research, nor of going into detail on cross-cutting information (which could be fundamental for research dealing with complex or sensitive issues) (Amaturo, Punziano, 2018). On the other hand, these epistemological limitations seem to be overcome by the qualitative approach: its attention to micro problems, its sensitivity to touchy issues, allows the researcher to walk in the shoes of the interviewee. The qualitative approach therefore does not involve any kind of decomposition of the object of study, but rather individuals are considered in their totality, that is, from a holistic point of view. Among the winning characteristics of the qualitative approach we mention in particular: the attention to the sociocultural context that is investigated, the idiographic orientation and predilection for micro problems, the desire to minimize the distance between researcher and interviewee, the preference for the global understanding of provinces weakened in meaning (Montesperelli, 1998). However, the epistemological and methodological limitations of both approaches, quantitative and qualitative, are not entirely irremediable. Fortunately, today social research has overcome this stumbling block (as already partially anticipated). Rather than thinking about a definitive crisis of either approach, or which approach is definitely better, it was decided to enhance research strategies through the safest route: that of mixed methods. Mixed methods are a relatively new reality of doing research, because of their inclusive and elastic nature, which allows the use of several methods and techniques in the same research project, overcomes the implicit limits of each technique and method selected. In other words, the perspective of mixed methods allows the researcher to start wide and deep researches, integrating qualitative and quantitative approach, with the possibility to deepen some topics, to combine standardized techniques (such as web survey, but also the same approach to Big Data) with non-standardized techniques (such as qualitative interview, or focus group, and netnography). The possibility of integrating several techniques is fundamental to the path taken by social research. Integrating different techniques and approaches means integrating sources and information through 35

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interviews, observation, questionnaires. It also means understanding the importance of knowing a phenomenon in its entirety but at the same time deepening certain aspects of it. The web survey approach could be integrated with the qualitative approach (e.g. with face-to-face interviews) in order to deepen some studied aspects but also to reduce the error of sample coverage by including people who are not part of the Internet network. Similarly, the qualitative netnographic approach is usually integrated not only with offline qualitative techniques (qualitative interviews, or field observation), but also with online approaches such as the web survey. It is not by chance that the label ‘blended netnography’ is distinguished precisely in this regard, to distinguish it from ‘pure netnography’ conducted purely online. The same approach to Big Data should be considered as integrable with the approaches described here.

CONCLUSION Today, the Web has become a space that says much more about us than we can imagine. Digital contexts have become virtual extensions of our face-to-face interactions and, more broadly, of our lives. Internet algorithms collect and process information about us on a daily basis, creating an endless stream. Our transactions, our searches, our purchases, our movements from one place to another, our public and private conversations end up in a huge database that tells the story of our online lives. The advent of COVID-19 and the subsequent shift of our activities from online to offline have further exacerbated our presence in these digital contexts. Social research, even if at a slow pace, in recent years has begun to take an interest in digital contexts and dynamics, because it has been realized that they are anything but distant from reality. Digital environments say everything about us that we do online. This is the case of Big Data, data sets in which everything is stored, so that the internet has no short memory, does not forget anything. Similarly, the Internet is used by research as a dissemination channel, thanks to which it is possible to reach a large number of people ready to participate in a quantitative survey in a very short time. This is the case with the web survey, which allows large amounts of data to be collected and processed by statistical analysis software. Both approaches, Big Data and web survey, pay meticulous attention to the quantitative and numerical aspect of the data collected, although with due differences: the former collects “organic data” born spontaneously on the web, the latter directly asks users to provide it with the information it needs by filling in a questionnaire. Both approaches still have a lot to offer to social researchers, as they are pools of information for the study of society. On the other hand, from a qualitative point of view, there are approaches such as netnography, which look carefully and in depth at the new digital contexts, to collect and analyze information while paying attention to micro problems. All the approaches described here, together with the other approaches not included, are blighted by flaws and limitations that make them imperfect. Limitations that partly share the approaches explored here, and which need to be worked on more, relate to the inclusiveness of the samples, and the ethics of data collection. The ‘real’ world is changing, increasingly encompassing the digital world. However, not all countries are included in the same way in virtual contexts, so it is necessary to pay attention to the segment of the population that is cut off from studies, in order for social research to be inclusive. On the other hand, from an epistemological and methodological point of view, the inclusion of currently marginalized populations would be useful to overcome the inferential limitations of these digital approaches. Secondly, it would be necessary to establish regulations in each country that would make 36

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users aware of and, above all, able to choose what data to share online. From an ethical point of view, this would be a great step forward both for research and for humanity and its rights. The specific limitation to the Big Data approach is certainly the superficiality with which one looks at the data collected, and the naivety of relying on an entirely data-oriented approach. Though, this does not mean that the Big Data approach is completely unusable in the context of social research, or that those who use it always fall into error. A possible solution to this bias would be to introduce this dataoriented approach into a new epistemological framework that includes a theoretical contextualization of the data collected and a shared reflection. Reflecting on the limitations of digital approaches should lead us to think first of all that they are far from perfect, and that they do not always (or hardly ever) guarantee the return of faithful data regardless, especially without the contribution of the researcher and his skills. The only weapon that social research currently has at its disposal is the combination of several approaches and techniques, i.e. the path of mixed methods. Combinations of different types of data (digital and non-digital) collected within a mixed-methods research design aim to combine strategies that have, from a methodological point of view, certain potentials but also intrinsic limitations. For example, digital data research might perform an initial Big Data analysis as an initial filter of the information collected, and then drill down and through the typical Small Data modalities. Digital approaches such as Big Data, online survey and netnography represent a new and important way of analyzing society, but they need to be complemented and supported by existing approaches. Only in this way will it be possible to retrieve information from multiple sources, confirm what is collected online, and, finally, broaden the horizons of knowledge.

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Chapter 4

The Future of “Digital Research” Costantino Cipolla University of Bologna, Italy

ABSTRACT Sociology is a discipline inevitably based on interpretative categories of social reality derived from a specific historical phase. In a period that is increasingly defined as a new era or digital society, can sociological knowledge not be upset by this overload of changes of every kind and nature? And can these changes not involve all identity components of sociology, namely theory, research, and the usability of its knowledge? Given this, it seems rather evident that this volume is the sign of the times and testify the variety and flexibility of digital methods. The author limits to dealing schematically with two methodological components that are constitutive of the digital revolution: the shift from the traditional and glorious ethnography to the new and emerging netnography, especially as regards the qualitative side, and, on the more properly quantitative side, the overwhelming and boundless spread of big data. A brief and selective description of these “transitions” will be complemented by a thoughtful evaluation of their potential for the future in the peculiar field of inquiry.

INTRODUCTION The web society (Cipolla 2015), without calling into question other more challenging terms, requires, in various ways, the deployment of techniques and methodologies of social research that can only be digital in nature. This volume is a good and documented demonstration of this. This chapter is aimed to draw various hints towards the future of digital research. We will do this in an extremely synthetic way, using a necessarily schematic form of exposition, aimed, however, at giving an account of the radical change, if not an authentic revolution, taking place around us and, as sociologists, inevitably also within us (Cipolla 2021). Since we are faced with a challenge without any exemption either of merit or of method, it is not so easy to understand the future that awaits us in the face of ever new tools and knowledge1. From an epistemological point of view, we can call for the emergence of a “fourth paradigm” (Lombi, 2020) after those historically established, or simply take the path of mixed methods2 or, again, rely on a process of integration between technique and content3 that overcomes or recomposes the information overload produced by technology which fractionalizes us and attack us from all sides (Cipolla, 2019) DOI: 10.4018/978-1-7998-8473-6.ch004

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 The Future of “Digital Research”

almost forcing us to be eclectic4, to put aside or put a little in the shadow our human dimension, in favor of artificial intelligence, algorithms and various bots5. Along this empirical perspective, which cannot but follow its society, what do we mean by “digital research”? It is obvious that with this question we are entering a theoretical-practical labyrinth that is not easy to untangle and that can be unraveled in many ways. Here, in this sense, we can do very little and limit ourselves to a definition, just beyond, of what can be meant by this type of digital investigation. Thus, in the following paragraphs, we will deepen, but only in outline and for guiding labels, two basic paths that the sociological research has recently undertaken and is developing with particular momentum and attached complexity, both on the old footsteps of quantity and quality, and on the current hybrid, eclectic, difficult, if not improperly, dichotomized ones. On the qualitative side I am referring, as we will see later on, to the shift from classic ethnography (or qualitative research?) to the current and rampant Netnography or digital ethnography and on the quantitative side, to the arrival of big data, alongside the traditional and common small data - and this, I want to specify right away, with all the many and profound methodological implications and, therefore, of merit (results) of the case. A first framing question we need to ask ourselves is that concerning digital methods in, for or of social research (Caliandro, Grandini 2019). Setting aside the hypothesis of virtual methods (Hine 2005), assuming the principle that digital inquiry is not a work confined to communicative processes (Caliandro, Grandini 2019) and involves new epistemological orientations, acquired that the old social relations cannot be confused or assimilated to the new technologically mediated connections6, the sociological research that today compete necessarily can be traced, in my opinion, to four well distinguishable (although interconnected) areas. A first type of research in this vein can be defined as traditional, but with technical contributions of a digital nature. By now, sociological research conceived in the classical sense that does not include computer-assisted tools or digital techniques, I do not believe that it is even conceivable anymore. During its necessary evolutionary and longitudinal developmental cycle7, this cannot but be intersected by many contributes of a digital nature. Both in the overall research design, with its basic research questions, in the co-institution of the elementary information (data collection) and their consequent processing and analysis, and their inescapable interpretation and dissemination or, better, their expendability of the whole, the facilitating and supporting intervention of the digital cannot be denied, without however posing itself in this sense as aseptic or neutral. No technique, in fact, is ever really neutral (Bordoni 2020: pp. 9 and following). More pervasive appears to be a second type of investigation that substantially transforms, thanks to the web, a classic traditional research in a sociological research carried out through digital techniques and methodological procedures. This transfiguration goes beyond the concept of change and for many reasons presents us with a new kind of investigation that, while still following the necessary methodological cycle mentioned above, is actually very different from the original historical model, for example in sampling, information gathering and so on. Another evolution derived from the changing times consists in setting up research designs that, with respect to a given topic, contemplate at the same time thematic and technical excursions in both online and offline practices for a final integrated outcome that is certainly enriched by the confluence in a single explanatory stream of the two different streams just mentioned. As far as I can understand it, this is a horizon that sociological research will pursue for the force of social happenings more and more towards the future, drawing from it certain and overall heuristic advantages.

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Finally, the type of research, entirely new, that studies practices on the web and follows its media logic cannot be abandoned to its fate (Rogers 2013). This cannot be ignored and cannot be carried out without considering the specific affordances8 of the digital devices. In fact, the affordances can never be completely avoided and force us into frames, which will affect the research process as a whole. Moreover, the transfer and applicability of the research results from restricted areas and topics to more general and current practice is a crucial and controversial issue, which we cannot now adequately address. In any case, it is obvious that in this area, digital native techniques and data come to clearly prevail over those derived offline, that is, from the digitization of tools and information deduced from palpable life.

NETNOGRAPHY BEYOND ETHNOGRAPHY In the cognitive storm that characterizes this historical period, this small essay cannot but live on its modesty, even though it is on the way out of a volume that moves in the sign of the times and along the always crossable border of sociological knowledge. It should be noted that this is especially true with regard to the many, new and always mobile digital methods and methodologies. Digital methods and methodologies present a wide range of components which in this paragraph will be limited to qualitative sociological knowledge seen under the historical lens of ethnography. Ethnography has changed its skin over the years (Denzin, Lincoln 2000) and, from cultural anthropology, has extended its methods to the social sciences, multiplying its theoretical and methodological orientations, going into crisis, resurrecting, taking new and unprecedented cognitive paths (Rinaldi 2020). Remaining within the framework of a social and analytical ethnography (Masullo 2020), we will not deal here with the transition from traditional ethnography to digital ethnography (Delli Paoli, D’Auria 2021)9, without completely excluding one from the other. We will dichotomize this perspective, well aware of the radicality of such an assumption, but following the logic of an abstract typology, not far from reality, able to give an account, albeit through the identification of simple labels, or rather to designate in summary and by simple cross-reference, some specific and basic thematic area (see the scheme attached to this paragraph). Generally speaking, digital ethnographic research can (today) be divided into four areas10 that can be described as follows: social media ethnography (deep, limited to a few accounts, without a specific field of reference); contextual digital ethnography (limited to an investigative field, semantically reduced, dedicated to often private relationships developed online); meta-digital ethnography in this case, information gathering is siphoned off or leaked into big data. Content is dispersed and requires or starts from a collection of information with global value. In cross-media ethnography, the investigative fields cover both the online and the offline, relying on platforms and concrete life, without setting defined information boundaries of whatever nature they are. I believe that the title we have explicitly given to this paragraph can give a good idea of how the diffusion of digital ethnography in our heuristic framework profoundly changes the overall scenario. Among the many roads and directions that we could have highlighted, we have synthetically preferred that of the “beyond”. By this we meant a sort of methodological overcoming that does not cancel progress but reduces it to a part of itself. Regardless of what one wants to term the newcomer on the scene of social research, whether it be traced back to more or less literary self-production11 online or imposed in a materialistic key (Marcotte 2020) or virtual appearance (Ferraro 2014: 237 and following), with relative advantages and disadvantages (Corposanto, Molinari 2014: 17 and following), it is proposed as a further, another contribution to be integrated into the previous one. In this way, Netnography would incorporate

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ethnography in itself, leading it back to a partial contribution within a more general contribution. This kind of incorporation can be interpreted in various ways, not all of them obviously positive (Masullo, Addeo, Delli Paoli 2020) and cannot be limited merely to informatic and technological dimensions (Masullo 2020). As can be seen in table 1, through multiple conceptual and methodological contributions, it changes the playing roles and even the game itself, beyond the contents being examined12. If we retrace the labeling components that contribute to compose our scheme, we realize rather quickly that they, even in their deliberate dispersion and in their not recomposed disorder, give us some indication of evolution towards the future. Without deepening any traditional footprints or digital traces, we can at least extrapolate the following. Taking for granted that what has been said takes shape in the web society, we begin by noting how classical ethnography is based on close, present, warm, direct relationships that have little to do with the all-pervasive digital connections13, which are mediated, colder, softer, more detached, and extrinsic. Another aspect of dissimilarity between the two research perspectives in question concerns the fact that in the traditional ethnography, the natives of physical cultural context are studied and accompanied in their current life, while in the online context, you need to follow the medium where interactions take place, with all the implications that are anything but marginal. Again, in traditional ethnography, the information collected are typical of a qualitative approach in a sufficiently historicized perspective. In digital ethnography, the data are abstracted, they become meta-data, powerful, extensible, but also less meaningful and authentic, a little more distant from current life, wherever it develops. Another rather relevant difference, although perhaps not entirely understandable in dichotomic terms, concerns the empathetic relationships with the natives made it possible in direct and participant observations, as opposed to more self-referential, more hermeneutic relationships, based on objective meanings (seen however with one’s own eyes) in the case of computer-mediated observations defined and calibrated on the web. A further component that seems to me to separate in various ways our two ethnographic approaches relates to the possibility of excavation and conceptual deepening granted in traditional ethnography in the face of a more appropriate possibility of comparison made possible by digital ethnography. In conclusion, we can underline how the traditional ethnographic approach activates a present that moves from the past, while the netnographic one activates a present that sees almost only the future. And this, I believe, is increasingly the proper, the specific, the core of digital or digitally oriented ethnographic research.

BIG DATA WITH SMALL DATA The topic of big data14 as such has only recently entered sociological debate, but its impact on our way of knowing seems to me rather important, calling into question consolidated methods and almost automatically proposing new heuristic perspectives. In the table associated with this paragraph (table 2) we will obviously enter into the merit and method of what significantly differentiates, albeit in dichotomic terms, big data from the old small data, typical of the previous traditional statistics and the contained database on which it worked. The large number of data, possible with the computerization and datification of our lives, have their own reason for being in their natural, enormous, overwhelming and uncontrollable dimension. And this “reason” leads us, if not even transforms us, into hyperobjects (Latour 2005), for analyses that can also be defined as “postdemographic” (Rogers 2013). The ways in which big data are built within the web society are very different and only partly produced within the web, many of them being digitized for different needs of society. In any case, they will be more and more around us

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 The Future of “Digital Research”

Table 1. Qualified and ambivalent scheme and classification Ethnography (traditional)

Netnography (digital)

Relational co-involvement

Connective co-involvement

Direct observation

Indirect observation

Narrow and contextualized field

Dilated and de-contextualized field

Temporally episodic relationship

Temporally continuous relationship

Presence (recordable)

Archive (analyzable)

Following the natives

Following the medium

Near

Remote

Offline community

Online community

People

Bot (information programmers)

Individual writing

Intersubjective writing

Auto-ethnography

Hetero-ethnography

Overt observation

Covert observation

Daily and natural sociality

Artificial sociality

Text

Hypertext

Spatial and temporal data

Meta-data

Limited information

Information Overload

All senses

Limited perception

Synchronic perceptive

Longitudinal perspective

Phenomenology (empathy)

Hermeneutics (meaning)

Concrete reality

Potential reality

Cultural (societal) constraints

Digital frames

Interpretive advocacy

Descriptive analytics

Mono-situated

Pluri-situated

Narrow reflexivity

Wide reflexivity

Constrained flexibility

Suggested flexibility

Self-representation

Co-representation

Deepening

Generalization

Difference

Comparison

Chosen empirical basis

Evolutive empirical basis

Upstream option on the reference set

Convenience sampling

Self-representation

Co-inter-representation

Social A-symmetry

Communicational horizontality

Modern Society

Web society

Present from the past

Present towards the future

(I think there are few doubts about this) and will hover over and inside our research, whether it is more or less localized. As a necessary consequence, they will be associated with other technical-operational perspectives, such as a sort of algorithmic sociology15 or the increasingly pervasive use of digital meth-

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 The Future of “Digital Research”

ods. This is not to say that classical statistics is destined to succumb or to disappear or to be exhausted by big data. Someone like me, given my age and my degree, who historically moved in that field and then found himself in front of or immersed in a mass of data without borders, understands quite quickly some basic things that also concern the title that we wanted to give to this paragraph. The title we have given to this paragraph in fact wants to signify or designate the fact that big and small data for many reasons co-implicate, one refers to the other, if not in need of the other. Precisely because they are placed at different descriptive levels, they can mutually enrich each other, reducing each other’s shortcomings, if not actually filling them, at least in the area of mutual relevance. This is especially true considering the fact that they are inherently pluralistic in their contents and technical directions (Agnoli, 2016: 7 and following.; Aragona 2016: 42 and following) and the integration between their different assumptions and their different outcomes is almost suggested by things or facts. In other words, it seems to me that big and small data co-imply, understand each other better in their mutual intersection, make us understand more and better about social facts if taken as a whole and compared. Today, and more and more towards the future, we can come to the conclusion that one cannot exist without the other, otherwise we will lose significant explanatory potential. Obviously, we are referring to cognitive procedures of a quantitative nature, which, although are expanding their scope, are never able to fully overlap with those more intrinsically qualitative16. Once again, however, we take it for granted that our regulatory ideal remains that of integration between quality and quantity in the broadest sense, especially in the web society17. As in the previous discussion on digital ethnography, also in this case we have articulated and led our comparison between big and small data to an expositive scheme structured in a forcedly dichotomous way18 (table 2) along multiple definitions that can outline, in our opinion, the methodological and general framework of reference in question. From this scattered list, we deduce in a very synthetic way (as always) some basic interpretative lines with a wider value or overlying the single labels in the field. The first basic line of interpretation may concern the arrival in our field of knowledge of a mass of digital archives, which, on the whole, are immense and capable of giving the world its own form. The shift from the old paper archives can only be radical, beyond its statistical19 and democratic20 meaning. Consequently, I think it is appropriate to share the idea of many scholars who think that in this way, theoretical perspectives that are almost imposed from the outside reach our knowledge. With this, I do not want to support the thesis that in this way we are returning to the old inductivism. The old inductivism was based on directly observed reality, while in digital archives social phenomena are already mediated with presumptions that, although pluralistic, technically direct us towards their more or less implicit orientations. This shift, then, cannot be ignored during our research work, otherwise our theoretical propositions will be relegated to an undefined, albeit verifiable, dependence on third parties or on the unnamed path that preceded us21. A second underlying component of this shift concerns what has been identified as dataphrenia (Bennato 2016) 22. Very often, although not always, big data are derived from what can be defined as digital traces left by actors during their social life (Stefanizzi 2016: 172). Personal wills do not enter into these collection mechanisms that tend by contrast to reproduce themselves infinitely. This easy, accessible, docile and practically limitless information availability can only encourage different cognitive appetites. However, due to big data features, this can easy turn into acritical findings. It derives a more or less latent dataphrenia, however incipient, which is dangerously invading our sociological knowledge. Because of their constant self-production, their exorbitant plurality, and their connective flexibility, almost involuntarily, big data tend to exhaust our heuristic world. In other words, they tend to unload our

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cognitive horizon and present it to us unraveled and ready to be enjoyed. In other words, what was once considered a search for causes becomes a search for correlations. The consequences of this evolution do not seem to be immediately understandable, even if I think we can say that they weaken sociological research in their own way, because to cause is to generate, while to connect is a less demanding way of associating. Is this the social destiny, on an explanatory level, of big data? We have already mentioned that big data are very different entities, especially in terms of their origin and consistency (Russo 2016; Grimaldi, Cavagnero, Gallina 2016). They can be generated in various ways, voluntarily or not, for structural or instrumental needs, by conscious acceptance of the subject involved or not. This multiplicity, always partial in its own way, however, leads the researcher to treat these data in a perspective of methodological integration (Mauceri 2016)23, whether it is between big and small data, or within aggregations of large mass of data. Within this perspective, they certainly represent a resource that cannot be easily set aside or simply left to its fate. Another aspect that big data reveals and proposes to us, almost on its own account, concerns a topic that is not insignificant, but indeed crucial, in epistemological terms. Although it cannot be dealt with deeply here, it concerns, in some ways incredibly, the need in our society not to limit ourselves to language, considering it our boundary of meaning24, but to overcome it and relativize it by going towards things in their extensive materiality. Big data, in their palpable materiality25, would go in this direction, they would be objects placed above our minds and in front of our eyes as such. Their machinic production would accentuate all of this and would physically transcend language, communication itself, to accentuate perhaps the very different communication. This new, unprecedented relationship between subject and object, full of technical consequences, would for many reasons derive from big data, able to break their own denomination, according to different paths26. A further component that emerges from our scheme is what we can define as structural in nature, and which refers to the many digital frames that invade the web both inside and outside communication processes. They are located both upstream (phase of information processing) and downstream (findings’ presentation, selection, visibility), but the affordances27 that support them also concern the type of medium (Severo 2016) that makes them possible and the many information technologies that make them up. In every human-machine relationship (including the digital ones) there is inherent some tightness that contributes to shaping the final outcome of the path taken. I conclude with a consideration, endowed with a natural uncertainty of its own, regarding a sort of ideology of power (De Stefano 2016) that can be hidden or be proper to this historical passage. In the meantime, it should be noted that since big data are so many different things and that they are never alone, since small data accompany them anyway, this ideology cannot in itself be reduced to a single dimension, homogeneous within itself and ready to challenge the course of history. It is clear that if the big data become more and more big, they bring with them some form of manipulative power inside and outside themselves, but it seems to me equally evident that the direction of this cannot be completely well defined a priori. Knowledge, in fact, can always be interpreted and provided in multiple ways. From this I can deduce the consideration that the ideologies of power (in the plural) cannot disappear with the arrival of big data28, but I do not know to what extent this can aggravate the previous situation or just present it to us in other forms. With respect to what we have just seen in this paragraph and in the previous one, it does not seem that we can reverse these processes, but that we can only go forward, albeit along roads not predetermined and different from each other. Given this, the conclusion of this contribution can only focus on some aspects of the unavoidable ethics of research that inevitably will accompany the development of digital methods.

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Table 2. Designated and dual scheme of classification Small data

Big data

Paper archives

Digital archives

Classical science

Computational science

Measurability

Beyond measure

Monodisciplinary

Pluridisciplinary

Primary collection

Secondary collection

Quantophrenia

Dataphrenia

Data (empirics)

Self-sufficient data

Sampling

Global set

Separated

Linked

Fractional

Exhaustive

Low definition

High definition

Intrusive

Non-intrusive

Voluntary

Unvoluntary

Operational definition

General guidelines

Quality/quantity dichotomy

Mixed

Presence of metadata

Random Metadata

Use value (not only)

Exchange value (not only)

Symbolic meanings

Digital meanings

Regular

Arbitrary

Ideological pluralism

Ideology of power (?)

Focused cores

Shape of the world

Classical Paradigms (inductivism/deductivism)

Emergent Paradigms (addutivism/pluralism,etc.)

Self-construction (production)

Hetero-construction (capture)

Value hierarchies

Algorithmic (mathematical hierarchies

Scientific affiliation

Public/private affiliation

Research designed in itself

Research inferred from other than itself

Subjective expressions

Digital traces

Content generated by research goals

Content produced by social life

Slowness

Speed

Homogeneity

Heterogeneity

Micro-macro

Macro-micro

Depth

Modelling

Interface

Programmed intermediaries

Native co-authors

Quasi-subjects (non-human actors)

Social contexts

Digital Affordances

Causes

Correlations

Expendability over time

Real-time virality

Individual (social actor)

Network (bot)

Modern society

Web society

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The first consideration that can be made is that the digital ethics develop from relational-psychological (Venneri 2014) to more properly connective in accordance with the spread of the web and the medium or media that cross and connote it29. The implications of this shift cannot be considered minor, dealing with the normative values inherent in the new public rituals (before often private), and the difficulty of placing and including ethics in the robots’ capacities (Fabris 2014). But this same consideration cannot fail to expand to the ongoing democratization of big data, with the potential, huge errors that may result from this (Parra Saiani 2016; Gini 1941) and with the possible extension of fake news (including scientific fake news) in every space and time30, with virtually no boundaries of any kind. From the risks inherent in a situation of this kind one can try to get out in various ways according to what we can assume as digital techno-humanism. On the one hand, in fact, we can count, even through education (Mannese 2019), on our capacities and expertise in order to enter the web without being overwhelmed by the network. On the other hand, we can exploit the virtues of the digital (Rivoltella 2015), trying to leverage on our contribution in the course of its inexorable development. This way of dealing with and challenging computer frames in various ways at a value level can only concern the web conceived in a broad sense and our digital humanities methods. No techno-methodological “revolution” can claim to be or be free from new ethical challenges that must be kept under close control and never abandoned to their anonymous delirium, especially in the new “digital age” (Schmidt, Cohen 2013)31.

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Corposanto, C., & Molinari, B. (2014). Survey e questionari online. In C. Corposanto & A. Valastro (Eds.), Blog, Fb & Tw. Fare ricerca quali-quantitativa online (pp. 17–42). Giuffrè. De Stefano, A. (2016). La profondità dei big data. Datacentrismo, ragnatela di significati digitali e cultura algoritmica. Sociologia e ricerca sociale, 37(109), 54-69. Delli Paoli, A., & D’Auria V. (2021). Digital ethnography. A systematic literature review, Italian Sociological Review. Denzin, N. K., & Lincoln, Y. S. (2000). Handbook of qualitative research (2nd ed.). Sage. Fabris, A. (2014). L’etica dei robot. In C. Bordoni (Ed.), Il perimetro delle tecnologie. Guida per una nuova iperumanità (pp. 85-94), Mimesis. Ferraro, V. (2014). Osservazione etnografica e comunità virtuali: una comunità di Export italiana. In C. Corposanto & A. Valastro (Eds.), Blog, Fb & Tw. Fare ricerca quali-quantitativa online. Giuffrè. Fraire, M., Spagnuolo, S., & Stasi, S. (2016). L’utilizzo dei big social data per la ricerca sociale: il caso della cittadinanza attiva in difesa del territorio. Sociologia e ricerca sociale, 37(109), 174-187. Gini, C. (1941). Alle basi del metodo statistico. Il principio della compensazione degli errori accidentali e la legge dei grandi numeri. Metron, 14(2-4), 173–240. Giuffrida, G., Mazzeo Rinaldi, F., & Zarba, C. (2016). Big data e news online: possibilità e limiti per la ricerca sociale. Sociologia e ricerca sociale, 37(109), 159-173. Goffman, E. (1967). Interaction ritual. Doubleday. Grimaldi, R., Cavagnero, S.M., & Gallina, M.A. (2016). Uso della rete e consapevolezza delle tracce digitali. Una ricerca tra studenti universitari. Sociologia e ricerca sociale, 37(109), 204-218. Latour, B. (2005). Reassembling the social. An introduction to actor-network theory. Oxford University Press. Lombi, L. (2020). Tecnologie digitali e ricerca sociale nella web society. In R. Cipriani & R. Memoli (Eds.), La sociologia eclettica di Costantino Cipolla. FrancoAngeli. Mannese, E. (2019). L’orientamento efficace. Per una pedagogia del lavoro e delle organizzazioni. FrancoAngeli. Marcotte, A. S. (2020). Umani Non-Umani: Etnografia digitale, Nuova teoria materialista e i Bot della chat di Ashley Madison. In G. Masullo, F. Addeo, & A Delli Paoli (Ed.), Etnografia e netnografia Riflessioni teoriche, sfide metodologiche ed esperienze di ricerca (pp. 193-202), Loffredo. Masullo, G. (2020). Introduzione. L’approccio etno-netnografico: uno sguardo d’insieme. In G. Masullo, F. Addeo, A Delli Paoli (Ed.), Etnografia e netnografia Riflessioni teoriche, sfide metodologiche ed esperienze di ricerca (pp. 19-25), Loffredo. Masullo, G., Addeo, F., & Delli Paoli, A. (2020). L’approccio etnografico e netnografico nelle scienze sociali: definizioni, strumenti, prospettive future. In G. Masullo, F. Addeo, A Delli Paoli (Ed.), Etnografia e netnografia Riflessioni teoriche, sfide metodologiche ed esperienze di ricerca (pp. 25-58), Loffredo.

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Mauceri, S. (2016). Contro la deriva scissoria. Le funzioni dei big data nelle strategie integrate di ricerca sociale. Sociologia e ricerca sociale, 37(109), 70-82. Parra Saiani, P. (2016). Le risorse e il controllo. I big data oltre il mito. Sociologia e ricerca sociale, 37(109), 28-41. Rinaldi, C. (2020). Inquietudini metodologiche. Riflessività, maschilità e sessualità nel fieldwork. In G. Masullo, F. Addeo, & A Delli Paoli (Eds.), Etnografia e netnografia Riflessioni teoriche, sfide metodologiche ed esperienze di ricerca (pp. 109-127). Loffredo. Rivoltella, P. C. (2015). Le virtù del digitale. Per un’etica dei media. Morcelliana. Rogers, R. (2013). Digital methods. MIT Press. doi:10.7551/mitpress/8718.001.0001 Russo, V. (2016). Un’applicazione empirica dei big data allo studio del mediattivismo civico. Sociologia e ricerca sociale, 37(109), 188-203. Schmidt, E., & Cohen, J. (2013). La nuova era digitale. Rizzoli. Stefanizzi, S. (2016). Small, open, big: I dati e la conoscenza scientifica. Sociologia e ricerca sociale, 37(109), 117-126. Venneri, E. (2014). Prospettive etiche della ricerca online. In C. Corposanto & A. Valastro (Eds.), Blog, Fb & Tw. Fare ricerca quali-quantitativa online (pp. 273–292). Giuffrè.

ENDNOTES 3 4 1 2

5 6



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8



9



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See in this book the chapter by A. Delli Paoli. See in this book the chapter by V. Luise and P. Lodetti. See in this book the chapter by G. Punziano. See the first large-scale reflection from which started a decade ago my analytical investigation into the incumbent web society (Cipolla 2013). See in this book the chapter by E. Grassi and that by Camargo Molano. See also Bordoni (2020). See Cipolla (2015: 156, 177). Not so Caliandro, Grandini (2019: 97 ss.). See also in this book the chapter by F. Corbisiero. I refer to my old, Il ciclo metodologico della ricerca sociale, FrancoAngeli, Milano 1998 (various editions). See Caliandro, Grandini (2019: 140) and in this book the chapter by S. Acampa, G.M. Padricelli, R. Sorrentino. For a review of the traditional and digital ethnography, see in this book the chapter by A. Delli Paoli. For a more detailed description of these types of netnographic research see in this book the chapter by A. Delli Paoli. See in this book the chapter by C. Cantale. This is a topic of considerable interest that we cannot address now. In online contexts, does content lose depth in favor of its expressive form?

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13 14



15



16



17



18



21 19 20

22



23



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Which, I repeat, should never be confused or mistaken for relational or offline connections. See the monographic issue of Rassegna Italiana di Sociologia, IVI, ¾, 2015, dedicated to the claims of large numbers. In it, I indicate the work of M. Mazzotti, Per una Sociologia degli algoritmi. To which we also referred, as cited in footnote 7, but making it converge towards aphoristic perspectives. The meme can never, not even by assonance, be methodologically assimilated to the gene. See in this book, in dissimilar fashion, the work of G. Giorgi. I also refer to Cipolla (2014). This methodological perspective has been the basis from the beginning of my heuristic theory, as can be seen well and in stages in Cipolla (2021). See also Mauceri (2016). Today this “scissorial drift” appears to me to be in marked decline. Sometimes, the alternatives in question should be treated in a more nuanced and co-implicating way. Which is not distorted, see for example Fraire, Spagnuolo, Stasi (2016). Which can never exclude expertise, however. See Stefanizzi (2016). Which is neglected even by those who are thoughtful in the field. See Giuffrida, Mazzeo Rinaldi, Zarba (2016). I note that this aspect, which anticipates the data to the researcher, is almost never reported to the classic Grounded Theory. I do not return to the fact that all my work, for better or worse, goes in this direction. This was true for Wittegenstein, but not for Heiddeger who also attributed a central and possessive role to language (Cipolla 2018). This return of a new materialist perspective (in the wake of Latour?) is also affecting sociology perhaps precisely through the uncertain and vague materiality of the digital. The interplay between objects and people, between man and machine, between thought and things leads us towards a co-understanding (my historical warhorse) that offers us new perspectives in the digital world. A term that is almost never translated, perhaps because of its polyvalence that refers to convenience, opportunity, frame and so on. In short, it refers to the opportunities, with related constraints, offered by the web. To say that big data is in and of itself deeply ideological seems to me to be simply wrong. Knowledge is never just that, not even in dictatorial contexts. In digital society, connection becomes the new generalized medium of symbolic communication. Without understanding this, it can hardly be understood (Cipolla 2015). See in this book the chapter by Trezza. Perhaps this perspective is too bold and pretentious, but I think it represents a challenge that can be accepted, also because otherwise it will override us without even letting us know it.

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Chapter 5

Research Ethics in the Social Sciences Gianluca Attademo University of Naples Federico II, Italy Alessia Maccaro University of Warwick, UK

ABSTRACT The formulation of Charts for research ethics and Codes of conduct has been growing in the last few decades, on the one hand due to a renewed awareness of the ethical dimensions of research governance and the relationship between regulators and researchers, and on the other hand for the expansion of possibilities achieved by innovation in information and communication technologies. The voluntary involvement of research participants, risk management and prevention, data protection, community engagement, reflexivity of researchers are some of the centres of gravity of a debate that involves researchers, institutions, and citizens.

INTRODUCTION At the international level, “Charts” and “Codes of behavior” were published for guiding human and social science research in the last few years. Their aim was both: to define guidelines for the researchers’ behavior, and to provide research projects with features that make them acceptable in terms of ethical clearance, which is becoming a more and more essential prerequisite to access funds. As Sutrop et al. (2020) wrote “The increased interest in writing codes of ethics for scientists may also be somehow related to various growing pressures upon scientists: pressure to publish (“publish or perish”), tight competition for research funds, anxiety about short-term contracts, etc. Also, a lot of scientists have to fulfil different roles […], and the pressure to cope with conflicting duties makes one ask how to share one’s time, attention, and responsibility so that no commitment is neglected. A classic example in academia is finding time for both, teaching and individual research. There may also be DOI: 10.4018/978-1-7998-8473-6.ch005

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conflicting duties within the same role. For example, a researcher may face a conflict between the duty to share data and to protect confidentiality” (p.71). As more and more researchers are involved in interdisciplinary projects and international research teams and publish in international journals, differences in standards (among different disciplines, different countries, and different institutions) become evident. Hence, the need to negotiate and develop a common understanding of the ethical standards of good research, including its planning, design, and conduct, as well as the dissemination of its results. As of today, however, codes of behavior are not many, and internationally recognized ethical frames are missing, despite their relevance. As a group of experts wrote in a Discussion Document for the Academy of Social Science in the UK “while these codes are often treated as if they were universally applicable, social scientists have demonstrated that they have been shaped by the particular circumstances and contingencies of biomedical research on human subjects” (Dingwall et. al, 2017, p. 111). However, there is a widespread agreement in the debate on the fact that it is necessary to reconsider ethics in the research of social sciences. Two main reasons motivate such necessity: firstly, the researchers’ participation in multidisciplinary projects is increasing, not only within social studies but also in partnership with medical sciences. Secondly, today’s technological progress allows us to get easy access to and exchange a large amount of data through the Internet. These Big Data create new and extraordinary possibilities to share and analyze them. Thus, in the last few years, the efforts made at the European level and, in specific contexts at the national level, had the double aim of a more in-depth analysis, highlighting the peculiarities of ethical questions regarding social sciences, and of acting at a systematic level, promoting codes and guidelines shared among all social sciences and the research system. At the European level, a great push undoubtedly comes from the European Commission, whose program Horizon requires to fill in an Ethical Issues Table before submitting projects to apply for funds (EC, 2010). In addition to this, in case the research involves human subjects, personal data, human cells or tissues, or features ethical issues reported in the table, a specific Ethics Self-Assessment is required.

SOME HISTORICAL REFERENCES The project Respect (Professional and Ethical Codes for Technology-related Socio-Economic Research), financed by the European Commission in the frame of program IST (Information Society Technology, priority 2) and carried out between 2001 and 2003, analyzed about 250 ethical codes of professional associations of human and social sciences (Dench, Iphofen & Huws, 2004). A review of these documents underlines how the topic was dealt with from the particular point of view of single disciplines adopting a wide range of approaches. For example, the ethical guidelines adopted in 1999 by the Association of social anthropologists from the UK and the Commonwealth (ASA 1999), pose ethical questions in terms of relations and responsibility of the researchers versus the participants to the research, the funders, the colleagues, the discipline, the governments and the society in general. Instead, EFPA, the European association of psychologists, proposes four interdependent principles: respect for the rights and dignity of the person, competence, responsibility and integrity (EFPA 2005). In the early 1980s, the Social Research Association (SRA) was one of the earliest organizations publishing ethical guidelines with the aim of promoting homogeneous ethical practice of research for its members, research centers and professional associations. The SRA Ethical Guidelines, upgraded in 2003, divide the ethical principles into four groups of obligations: to 55

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society, to funders and clients, to colleagues, to subjects of the research. While the most recent publication, Research Ethics Guidance, issued in 2021 by the same association, is structured around four keywords, i.e., informed consent, confidentiality and anonymity, avoiding harm, questionable research practice (SRA, 2021). A fifth section of the document focus on ethical foundations, not offering “priority lists for making choices, but a framework within which the conscientious social researcher can, for the most part, work comfortably” (SRA, 2021, p.7). Even if with different approaches, the scientific literature underlines the presence of common ethical questions as the informed consent, the duty to privacy, the maximization of the benefits of the research avoiding any harm to the subjects involved, integrity. Such principles confirm the influence of biomedical sciences in the debate on ethics and the evaluation of research in social sciences, which, like biomedical sciences, involve human subjects. The Nuremberg Code (1948), that goes hand in hand with the sentence of the experiments conducted by the Nazi doctors, is the document that represents the turning point in the history of biomedical experimentation (Taylor, 1992). The Code individuates an essential requirement in the participants’ consent to the experiment. With the Helsinki Declaration of 1964, the WMA (World Medical Association) introduces independent ethics committees with the aim of examining research protocols before they get started. The updated version of the Declaration of Helsinki (WMA 2013, article 23) states: “The research protocol must be submitted for consideration, comment, guidance and approval to the concerned research ethics committee before the study begins. This committee must be transparent in its functioning, must be independent of the researcher, the sponsor and any other undue influence and must be duly qualified. It must take into consideration the laws and regulations of the country or countries in which the research is to be performed as well as applicable international norms and standards but these must not be allowed to reduce or eliminate any of the protections for research subjects set forth in this Declaration. The committee must have the right to monitor ongoing studies. The researcher must provide monitoring information to the committee, especially information about any serious adverse events. No amendment to the protocol may be made without consideration and approval by the committee. After the end of the study, the researchers must submit a final report to the committee containing a summary of the study’s findings and conclusions”. The 1960s and 1970s also saw the emergence of ethical considerations in social and behavioral science research in the United States. There were three high-profile studies raising questions concerning the respect for, autonomy of, and harm done to research participants: those of Stanley Milgram in the 1960s on the obedience to authority (Milgram 1974), Philip Zimbardo’s prison simulation study of 1971 (Haney, Banks & Zimbardo, 1974) and Laud Humphreys’ studies in the years 1965–1968 (Humphreys 1970). During the same period, several prominent professional associations of social sciences established their own codes of ethics. The American Psychological Association (APA) published its first researchrelated code in 1966 (AERA, APA, NCME 1966; 2014); the American Sociological Association (ASA) approved its code in 1969; the American Anthropological Association (AAA) adopted an ethics code in 1971 (AAA 1971); and the American Political Science Association (APSA) issued a report on ethical issues in 1968 (APSA 1968). However, in 2010 the European Commission reported that their ethical review process shows “a lack of awareness of how one should deal with the ethical issues in Social Sciences and Humanities (SSH) research proposals. Either the applicants lack the requisite knowledge of ethics, have low awareness of how ethical principles should apply to their research, or they do not know how to demonstrate their awareness of ethical issues. Some common reasons include: failure to show any significant appreciation of the potential ethical issues of the study; failure to notice that voluntary informed consent is the ac56

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cepted norm of conduct for all kinds of research on human subjects; failure to give a detailed description of how the informed consent process will be implemented; failure to provide appropriate documentation supporting the proposed research from the ethics and regulatory side; failure to describe in detail what kind of measures of privacy and data protection will be implemented; failure to assess the potential risks associated with the project and to plan measures to avoid or minimize them” (p. 5). In recent years there have been updates and integrations to these documents, with a growing awareness of the ethical dimensions of research governance and of the relationship between regulatory bodies and researchers. Among these, The New Brunswick Declaration should be considered. Such declaration was signed by an international gathering of researchers committed to enhancing ethical research practice, and supporting innovative alternatives to the regulation of research ethics that might achieve this end. The document lays out some concerns and alternatives to current ethics regimes suggesting, among other things, that they should “encourage regulators and administrators to nurture a regulatory culture that grants researchers the same level of respect that researchers should offer research participants” (SRA, 2012; see also van den Hoonaard, Hamilton, 2016).

THEMES, PROBLEMS, PRINCIPLES The first important remark regards the peculiarity of empirical research. Furthermore, there is “a general consensus in the literature that the ethical problems of qualitative research are greater than those of quantitative research” (Dench, Iphofen & Huws, 2004, p. 13). Indeed, the most relevant ethical questions concern people’s interaction and contact, as for instance in frontal interviews, administration of tests or questionnaires, observation of individual behavior or social interactions within an anthropologicalcultural context (Hammersley, Traianou, 2012). The use itself of previous data, more and more common thanks to the possibility of archiving and electronic elaboration, presents relevant ethical implications. It is the researcher’s primary responsibility to use scientifically accredited databases offering adequate guarantees on the lawfulness of the original collection. The people’s participation has to be voluntary, explicitly declared and documented so as to be removable at any time without any inconvenience. The participants’ consent represents the necessary condition for the lawfulness of their enrollment and the collection of personal data; the consent to the enrollment has to be distinguished from the further consent to the collection and use of personal data, which must feature a specific documentation. Researchers must be very careful in informing the participants through a proper communication process so that the research goals, methods and activity are clearly understood, as well as the potential risks and the commitment demanded. Many highlight the importance of favoring the participants’ active and critical attitude as much as possible. A favorite instrument in this respect is the preliminary contact of representatives of associations and groups of interest, particularly in case of subjects in conditions of social or economic disadvantage. The availability to openly discuss the nature of the research, and what it involves, may contribute to hone its goals and modalities, limiting at the same time the risk of withdrawal or conflicts among the participants. Further critical issues regard the necessity to limit or conceal to participants the information on the real nature of the research goals. In such cases, great attention must be paid to the evaluation of the relevance of expected results versus the harm caused to the participants’ dignity, informing them in any possible way on the predictable risks and inconveniences. At the end of the study, complete information on the research has to be provided, as well as the reasons why real goals had to be concealed in order to obtain a retrospective consent. In case of 57

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research involving the risk of unpredictable acquisition of remarkably sensitive data, participants must be made explicitly aware of such risk in order to protect their personal integrity and autonomy (Calvey, 2017). This type of information may reveal particularly sensitive private aspects for which subjects are vulnerable because of their condition, age or personal story. The selection of participants and the conduction of the research must be carried out so as to avoid any form of discrimination or treatment disparity linked to their age, gender, sexual orientation, handicaps, ethnicity, social-economic status or religion. Researchers have to take into account special needs deriving from specific conditions of fragility; in the collection and classification of personal data regarding lifestyles and behaviors of individuals and groups, they have to avoid definitions possibly leading to unjustified generalizations causing stigmatization of such individuals or groups. As for research involving foreign countries, local needs must be considered carefully; the obtained results have to be disclosed so that they may contribute to capacity building of the local people. As regards the management of risk, researchers need a plan involving: i) a deep analysis of the context; ii) an estimate of the nature, probability and severity of the possible risks; iii) the number of people potentially involved and the short and mid-term consequences; vi) measures of preventions as the creation of a network including local institutions and organisms (such as embassies, forces of public security, non-governmental organizations defending human rights). These risk plans must clearly feature: the responsibility of all the researchers involved (according to their roles), the list of the expenses for the cost of the security, the risks connected to the conduction of interviews, possible sanitary risks, the modalities of management of emergencies. Then, researchers have to be given specific training on the possible risks and the strategies of containment. The disclosure of sensitive personal data may cause relevant risks for the subjects involved in the research. The risk management plan has to include the aspects regarding the prevention of a possible improper disclosure of such data or their illegal use, as well as the procedures for harm restoration. In particular, the adequate protection of the subjects requires: collection of information strictly linked to the research goals and not exceeding them; anonymous materials; dissociation between interviewees’ personal data and the materials deriving from interviews; protection of the files related to the transcription of the interviews through procedures of encryption; generalization in all the uses of transcriptions of the interviews; at the end of the research, destruction of all rough materials not completely anonymous; conduction of online surveys only by employing platforms with appropriate certifications of security; use of only anonymous data in disclosing the results (Elliot et al., 2016). Making the research results public is a duty of institutions and researchers; the disclosure of scientific results is an expression of democratic societies, and contributes to the maintenance and the development of cultural traditions and the formation of aware public opinion. The researchers’ contribution to the public debate is important, as well as the promotion of non-instrumental uses of the research outcomes through the media. The national and international documents above mentioned insist, though with some differences, on a series of reference principles; it is important to underline the necessity to avoid defining a hierarchy, but to interpret them in their framework. •

Benefits for the society

Research is a social activity carried out for the social good and the common interest. A better understanding and description of social phenomena and the enrichment of culture as common heritage are key factors of development in democratic societies. 58

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•

Dignity

Dignity is the theoretical and ethical foundation of the acknowledgement of human rights. Respect for people’s dignity means never mistreating or using them for other purposes that are unknown to them. •

Respect

Research must be carried out respecting the dignity, the autonomy, the personal integrity and the private life of the subjects involved. Respect means acknowledging their needs and interests, evaluated in concrete situations impartially and without any conditioning. Respect means also acknowledging the particular vulnerability of some subjects and valorize the minors’ will and ability to discern. •

Responsibility

Responsibility means to be able to account for one’s own actions; it involves duties of juridical, deontological and ethical nature towards all the subjects who may be influenced, positively or negatively, by the conduction of the research. Moreover, the research activity has to be carried out in a transparent way, so that all its phases may be evaluated by the scientific community and the general system of research. •

Inclusion

An inclusive approach requires the fair consideration and valorization of interests, values and prospective of all the relevant subjects as well as a correct acknowledgement of their role and potential contribution. Their instances must be evaluated impartially and included in the definition of the research goals with a preliminary estimate of possible risks and social consequences. •

Accessibility

The correct communication and public diffusion of results is part of the research integrity. While respecting the clients’ bonds, the research results have to be disclosed with transparency, completeness and objectivity. Their social relevance is linked to the responsibility of making sure that they are disclosed also outside the scientific community in order to be available for a wider public. •

Freedom

It’s diffused responsibility to create the conditions for the protection of the researchers’ independence and freedom. The activity of research must be protected from economic and political pressures. The actual intellectual independence of researchers is based on the assumption of their awareness of possible cultural and ideological pressures deriving from their environment, training and personal experience. •

Reflexivity

Reflexivity has emerged as a current key issue when considering ethics in sociological research. Ethical practices imply that the researcher reflect on their role in the field. Researchers must ponder 59

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not only on their biases, but also on the impact of their presence in the field or the questions they might ask in a survey. Concluding this quick analysis of principles and rules of behavior, we would like to underline the fact that research in the field of human and social sciences involves a process, whose different stages rely on the contribution of several actors. What originates from this is a responsibility necessarily shared among all the subjects involved in the system, primary and secondary, direct and indirect, according to their role in the different stages. Defining ethical principles and the research integrity, thus, means defining the range and the limits of the responsibility of single researchers as well as all the other actors of the research ecosystem.

A FEW OPEN QUESTIONS Definitions of ‘research integrity’ vary and there is no universal consensus on the meaning of the term. For some, ‘research ethics’ can be considered as a sub-set of ‘research integrity’, see for instance the UK’s Concordat to Support Research Integrity (Universities UK, 2012). Others use ‘research ethics’ as a catchall to describe research conduct/integrity, particularly ‘good’ research conduct. “In some usage, the two terms appear to be treated as complementary, so that ‘integrity’ is taken to refer to important aspects of researchers’ behavior that are not always included in, and are certainly not usually central to, discussions about research ethics, such as avoidance of plagiarism, the declaration of conflicts of interest, and a commitment to research rigor […]. However, at other times, the term ‘research integrity’ appears to operate as an overarching category that includes those issues normally discussed under the heading of ‘research ethics’ (Hammersley, 2020, p. 382). Their separation is often due to administrative requirements, which makes their meaning weaker; therefore, they should be seen in their interconnection, as Iphofen briefly put it: “Work that is unethical lacks integrity – and any work that suffers in terms of its integrity cannot be regarded as an example of ethical research practice” (Iphofen 2020, p. 20). It is important to consider that the various terms can have different meanings to different people and organizations. As we have seen, the term “research integrity” (or sometimes “researcher integrity”) has come to be widely used in recent years, especially in official documents relating to the governance of scientific research. “Nevertheless, this popularity of the concept of research integrity has served a useful function in drawing attention to aspects of the role obligations of researchers that have not always received the attention they deserve: those relating to the actual task of producing knowledge, as compared with (equally important) ethical issues to do with the treatment of participants in research” (Hammersley, 2020, p. 381). Hammersley suggests focusing on what he defines as “Epistemic integrity”: “Above all, epistemic integrity requires that a researcher strives to make sound judgments regarding what would be best in pursuing a particular project so as to produce sound knowledge, about the validity of the findings produced in studies, and concerning the current state of knowledge in her or his field” (2020, p. 381). At the same time, “it proscribes researchers pretending to have answered questions that their research did not tackle effectively and perhaps could not have tackled”. Because social science research, “free, based on a plurality of interests, funding, methods and perspectives” is, must be emphasized, “one of the means by which democratic societies learn about themselves, and others, alongside journalism and the creative arts” (Dingwall et al., 2017, p. 114). Therefore, issues about balancing benefits and harm should be addressed in a different way from how it happens in biomedical sciences. The Participant (Patient) Protection Model (PPM), born from the memory of abuse in 60

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clinical trials, coherently prioritizes the rights of participants over all others, including the researcher and society more generally. It “attempts to equalize gross asymmetries of knowledge, risk and power, and the consequent opportunities for abuse, by constraining the legitimate actions of researchers” (Dingwall et al., 2017, p. 114). On the other side social researchers pointed out the duties that those participating in a democratic society have “to contribute to learning from which they and others may benefit, particularly if this is conducted in ways that minimize their personal risks”. The proposal of the document prepared for the UK Academy of Social Sciences is, consequently, that if the “PPM places an onus on researchers to demonstrate that participants in their research are knowingly accepting well-managed risks […], adopting the social science model would place the onus on would-be regulators to show why research consistent with the following principles should not proceed” (Dingwall et al., 2017, p. 114). Moreover, conflicts of value and critical issues emerge in the debate on ethics of research in social sciences. One of these concerns the choice of the best methodologies so that the research is not only scientifically sound but also ethically justified. Some methods are indeed more ethically appropriate to some study topics and target populations rather than others. Thus, method choice, like the perception of ethical risks, is highly context-dependent. Ron Iphofen writes on this topic: “Different methods raise different ethical issues and while combining methods in a form of triangulation is appealing for both methodological and ethical reasons, the multimethod or mixed method approach offers no guarantees for reducing risks of harm. It cannot be overstated that the choice of method remains highly context specific. A full consideration of the population to be researched and the setting for the research should guide the choice of method” (Iphofen, 2020, p. 371). As for the matter of relations between researchers and participants to the research, we have to take into account that today, considerations of ethics in social sciences have expanded dramatically in a turn toward recognizing the agency of those whom we study. Currently, conversations center around mitigating power dynamics between researchers and participants, reflexivity on the part of the researcher, and an engagement with feminist and indigenous methods. In opposition to these trends is the so-called “colonization of social sciences by the biomedical approach”; a phenomenon involving the increasing institutionalization of ethical boards modeled on those of biomedical research (van den Hoonaard, 2011, p. 2016). The reasoning included “a regime of ethics boards established to police medical research”, describing a scenario of “raising a generation of scholars who lack the ability to question ethics review boards and their history” (van den Scott, 2020, p. 776). Then, the reflexivity that the social scientist’s action should feature would be seriously questioned. “In some regions many sociologists are passive with regard to the ethics regime and accept the solidification and permanence of the boards without question. Sociologists concerned with ethics view this development with great concern because the boards affect how we conceptualize our research at its most fundamental levels. Focusing so single-mindedly on the ethics review process, which has little to do with actual social science ethics, turns a sociologist’s thoughts inward. The question shifts from what comprises ethical research to how to get their research project through the ethics boards. Thoughts remain trained on “their” research, process, and career. The ethics review boards establish a new hierarchy of credibility with themselves at the top, then the researcher, as a privileged expert, and then the participants” (van den Scott, 2020, p. 776). This reference to reflexivity allows us to mention one last critical issue long emerged in the research regarding human and social sciences: the fundamental question of power relationships between researchers and participants. Some human subjects or potential participants can find themselves in situations where it may be difficult for them to refuse to participate in research. The debate, from Nuremberg onwards, revolves around the acknowledgement of the principle of autonomy and consequently the freedom 61

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to participate or not. All subjects vary in the degree to which such rights or choices are available to them – thereby restricting their autonomy. An exemplary situation is the question of the enrollment of students; the European Commission, in the above-mentioned Guidance Note, points out that “involvement of students in research experiments is subject to same ethical requirements as the other research participants” (p. 16). Students in many disciplines might find themselves constrained or pressured to participate in research conducted by their educators with little opportunity to object. The Code of Human Research Ethics for the British Psychological Society offers an illustration of how this obligation is “justified.” As Iphofen sums it up “It recognizes that students are in a dependent or unequal relationship with their lecturers and, while undergraduate participation in psychological experiments is not required for Society accreditation, it is argued that most psychological research involves human participants and that courses in psychology need to acquaint students with appropriate methods for carrying out such research. The “direct” experience of being a study subject is perceived as valuable for understanding what their research subjects might feel when the student conducts their own research. Further the Code makes the ethical argument that it could be seen as unethical for psychology students or graduates to carry out research with others unless they have been willing to participate, and have had experience of participation in such research themselves” (2020, p. 555).

CONCLUSION As discussed in this chapter, the growing awareness of the importance of ethics for scientific and sociological research has led to a systematization on an international regulatory level, through increasingly explicit documents, of the relationship between governments, sciences and subjects in trials. Among the scientific community a common understanding of the standards of good research has been developing. This has favored an increase in awareness of the importance of paying attention to ethics. The reported historical reconstruction shows how the responsibility of the researchers towards the participants to the research, the funders, the colleagues, the discipline, the governments and the society in general has progressively shone through the definition of specific documents. The international debate has, in fact, defined a broad interdisciplinary framework, in which ethics has acquired a recognized and irreplaceable role in defining and constantly redefining the balance between the protection of the dignity of research participants and the promotion of research as a mean, by which democratic societies learn about themselves.

REFERENCES All European Academies (ALLEA). (2011). The European code of conduct for research integrity. European Science Foundation. American Anthropological Association (AAA). (1971). Principles of Professional Responsibility. AAA. American Educational Research Association (AERA), American Psychological Association (APA), National Council on Measurement in Education (NCME). (1966). Standards for Educational and Psychological Tests and Manuals. Authors.

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American Educational Research Association (AERA), American Psychological Association (APA), National Council on Measurement in Education (NCME). (2014). Standards for Educational and Psychological Tests and Manuals. Authors. American Political Science Association (APSA). (1968). Ethical Problems of Academic Political Scientists. APSA. American Psychological Association (APA). (2017) Ethical principles of psychologists and code of conduct. APA. Association of Social Anthropologists of the UK and the Commonwealth (ASA). (1999). Ethical Guidelines for good research practice. ASA. Calvey, D. (2017). The art, politics and ethics of undercover fieldwork. Sage (Atlanta, Ga.). Dench, S., Iphofen, R., & Huws, U. (2004). An EU code of ethics for socio-economic research. Institute for Employment Studies (IES). Report 412. Dingwall, R., Iphofen, R., Lewis, J., Oates, J., & Emmerich, N. (2017). Towards Common Principles for Social Science Research Ethics: A Discussion Document for the Academy of Social Sciences. In R. Iphofen (Ed.), Finding common ground: consensus in research ethics across the social sciences (pp. 111–123). Emerald Group Publishing Ltd. doi:10.1108/S2398-601820170000001010 Economic and Social Research Council (ESRC). (2015). ESRC framework for research ethics. ESRC. European Commission. (2010). Guidance note for researchers and evaluators of social sciences and humanities research. Author. European Federation of Psychologists’ Associations (EFPA). (2005). Meta-Code of Ethics (2nd ed.). EFPA. Hammersley, M. (2020). On Epistemic Integrity in Social Research. In R. Iphofen (Ed.), Handbook of Research Ethics and Scientific Integrity (pp. 67–89). Springer. Hammersley, M., & Traianou, A. (2012). Ethics in qualitative research: Controversies and contexts. Sage (Atlanta, Ga.). Advance online publication. doi:10.4135/9781473957619 Haney, C., Banks, C., & Zimbardo, P. (1973). A study of prisoners and guards in a simulated prison. Naval Research Reviews, 9, 1–17. Humphreys, L. (1970). Tearoom trade: impersonal sex in public places. Aldine Publishing Company. Iphofen, R. (Ed.). (2017). Finding common ground: consensus in research ethics across the social sciences. Emerald Group Publishing Ltd. Iphofen, R. (2020). Acting Ethically and with Integrity for Research Subjects and Participants. In R. Iphofen (Ed.), Handbook of Research Ethics and Scientific Integrity (pp. 553–560). Springer. doi:10.1007/9783-030-16759-2_55 Iphofen, R. (2020). Regulating Research. Governance and Ethics Across the Spectrum. In R. Iphofen (Ed.), Handbook of Research Ethics and Scientific Integrity (pp. 17–32). Springer. doi:10.1007/978-3030-16759-2_52

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Iphofen, R. (2020). Ethical Issue in Research Methods. In R. Iphofen (Ed.), Handbook of Research Ethics and Scientific Integrity (pp. 371–379). Springer. doi:10.1007/978-3-030-16759-2_54 Iphofen, R. (Ed.). (2020). Handbook of Research Ethics and Scientific Integrity. Springer. Israel, M., & Hay, I. (2006). Research Ethics for Social Scientists: Between Ethical Conduct and Regulatory Compliance. Sage (Atlanta, Ga.). Milgram, S. (1974). Obedience to authority: an experimental view. Tavistock Publications. Social Research Association (SRA). (2003). Ethical guidelines. SRA. Social Research Association (SRA). (2012) The New Brunswick Declaration: A Declaration on Research Ethics, Integrity and Governance resulting from the 1st Ethics Rupture Summit. SRA. Social Research Association (SRA). (2021) Research Ethics Guidance. SRA. Sutrop, M., Parter, M. L., & Juurik, M. (2020). Research Ethics Codes and Guidelines. In R. Iphofen (Ed.), Handbook of Research Ethics and Scientific Integrity (pp. 67–89). Springer. doi:10.1007/978-3030-16759-2_2 Taylor, T. (1992). Opening statement of the prosecution December 9, 1946. In The Nazi doctors and the Nuremberg Code: human rights in human experimentation (pp. 67–93). Oxford University Press. Universities UK. (2012). The concordat to support research integrity. Author. van den Hoonaard, W. C. (2011). The seduction of ethics: transforming the social sciences. University of Toronto Press. doi:10.3138/9781442694521 van den Hoonaard, W. C., & Hamilton, A. (Eds.). (2016). The Ethics Rupture: Exploring Alternatives to Formal Research Ethics Review. University of Toronto Press. doi:10.3138/9781442616653 van den Scott, L.-J. K. (2020). Sociology and Ethics. Doing the Right Thing. In R. Iphofen (Ed.), Handbook of Research Ethics and Scientific Integrity (pp. 769–782). Springer. doi:10.1007/978-3-030-16759-2_68 World Conference on Research Integrity (WCRIF). (2010). Singapore Statement. WCRIF. World Medical Association (WMA). (2013). World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. Journal of the American Medical Association, 310(20), 2191–2194. doi:10.1001/jama.2013.281053 PMID:24141714

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Chapter 6

Doing Research With Online Platforms:

An Emerging Issue Network Francesco Marrazzo University of Naples Federico II, Italy

ABSTRACT The post-API age in digital research has brought immediate consequences in research activities based on (big) data owned by online platforms. Even some initiatives made by online platforms themselves, mainly based on funding specific research projects, have not found a warm reception in the research community and have been considered not enough to do research on the most relevant phenomena of the digital public sphere. Therefore, since the access-to-data has become a relevant issue even for civil society organizations and public actors dealing with digital ecosystem, a specific brand-new issue network among public institutions, NGOs, and researches has been established. The technical expertise, the shared interests, and the fulfilment of similar goals in shaping public values in the online platforms activities seem to be crucial to the permanence and even to the institutionalization of such an issue network.

INTRODUCTION The collaboration between online platforms (search engines, social networks, etc.) – owning a huge amount of data released by the daily activities and interactions of users (Andrejevic, 2014), the so-called big data (Manovich, 2011; Mayer -Schönberger, Cukier, 2013; Kitchin, 2014; Amaturo, Punziano, 2017) – and the academic world is an increasingly relevant topic for the evolution of (digital) research in the human and social sciences. The Cambridge Analytica scandal, that is the origin of the so-called post-API age (Freelon, 2018) in the digital social research (Veltri, 2020), jointly with the growing interest of policymakers, especially at European level (van Dijck et al., 2018), in governing the platforms and regulating some phenomena such as online disinformation and online hate speech, has gradually led to the establishment of an isDOI: 10.4018/978-1-7998-8473-6.ch006

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sue network, made by policymakers, researchers, NGOs, etc., eager to adopt some solutions aimed at improving the digital research based on online platforms (especially social media) data. Therefore, this chapter aims to look at the digital research with(in) the online platforms from a public policy perspective by (i) tracing the status of the relationships between research community and online platforms (paragraph 1), (ii) mapping the main issues arising from the recent developments of this sensibile relationship and highlighted by a whole variety of actors forming an issue network (paragraph 2), (iii) analyzing some possible solutions the members of such an issue network could propose (paragraph 3).

THE RELATIONSHISP BETWEEN RESEARCH COMMUNITY AND ONLINE PLATFORMS For many years, digital research in social sciences has been based on the analysis of semi-structured and unstructured data (Veltri, 2020) from online platforms collected through the APIs (Application Programming Interfaces), allowing anyone with a few programming skills to gather massive volumes of data about a given platform’s users and content. Since the free collection of data has been simplified thanks to the collaboration between researchers and developers aware of the potential offered by this technology for the knowledge of social phenomena on the web, this opportunity has generated an approach to the computational social sciences based on the extraction of data records made available through the online platforms programming interfaces (Caliandro, Gandini, 2019). Numerous tools, e.g. Netvizz (Rieder, 2013) were born, for example, from the fruitful work of the Digital Methods Initiative (Rogers, 2013). Furthermore, since social scientists doing digital research become part of an assembly, which includes human components, methodological devices and data, and which itself becomes the object of research (Lupton, 2018), access to data and the collection via APIs has allowed social researchers to analyze what kind of web epistemology the platforms convey when they shape the content, types and categories of knowledge they make accessible (Amaturo, Aragona, 2019) 1. Even the lack of transparency about how big data are collected and codified by private companies is a relevant issue digital research scholars should face (Veltri, 2020). Finally, the compliance of such big data to data quality requirements is becoming fundamental in the digital social research (Stefanizzi, 2021). The Cambridge Analytica scandal in 2018, jointly with some restrictions aimed at protecting the users’ privacy, has led to the API shutdown2. As stated by Freelon (2018): When companies can restrict or eliminate API access at any time, for any reason, and without any recourse, computational researchers and students need to seriously consider how to proceed. We find ourselves in a situation where heavy investment in teaching and learning platform-specific methods can be rendered useless overnight: this is what I mean by “the post-API age”. (p. 665) Since that moment, the free access to the online platforms data has been regulated exclusively by the proprietary companies, other data extraction strategies, such as the web scraping (Rogers, 2018; Caliandro, 2021) being more work-intensive and possibily illegal in some cases3, and the academic world, especially in the field of social sciences, has certainly suffered from this closure. The so-called APIcalypse (Bruns, 2019) has had a particularly critical effect on the ability of social media researchers to investigate phenomena such as abuse, hate speech, trolling, and disinformation campaigns, and to hold the platforms accountable for the role their affordances and policies might play in facilitating such 66

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dysfunctions. On the opposite, Tromble (2021) has argued that although the restrictions to academic researchers’ access to the easiest, most reliable means of systematic data collection via online platforms application programming interfaces (APIs) have been decried widely by digital researchers, relatively little has changed, and the underlying relationship between researchers, the platforms, and digital data remains largely the same. According to Trombe herself, the platforms and their APIs have always been proprietary black boxes, never intended for scholarly use, and the largesse of the API era allowed many researchers to conduct their work with little regard for the rigor, ethics, or focus on societal value anyone should expect from scholarly inquiry. However, in the last two years (2019-2020), online platforms have launched relevant programs with academics, more based on funding specific research projects than on giving access to the data (even if in some cases, platforms made available data grants or other competitive schemes that explicitly invited scholars to apply for funding and data access as well), that appear designated more to generate positive media coverage than support meaningful scholarship (Bruns, 2019). Twitter has been one of the few online platforms which has made APIs available to researchers and developers. Twitter’s APIs are a unique data source for academics that is used around the world in a wide range of fields, from disaster management to political science, every day, even if some scholars have underlined that the bulk of the research on Twitter has focused on easily accessible datasets centring on hashtags and keywords (Bruns, Burgess, 2015)4. Facebook has instead adopted a different approach, first implementing a new model of partnership between industry and academia (King, Persily, 2018) and giving access to data to some scholars interested in the relationships between social media and democracy5, then giving access to Crowdtangle (a social media analysis tool owned by Facebook itself, and previously made available to journalists) to accredited academic researchers. The use of this tool exacerbates some limits already inherent in the API – which, as opportunities for access to data regulated by the online platforms themselves and subject to their terms and conditions, set up tight constraints on the type and quantity of data to which research can access (Caliandro, Gandini, 2019) – and in online platform themselves, that have been built to extract data from user for companies (or professionals) to advertise to a specific audience segment, rather than to create community or enhance public debate (Rogers, 2018). Furthermore, such a “partial” access makes the issue of the opacity of digital data more and more evident, especially because these kinds of partnerships will necessarily lead to trade-offs in terms of research objectives and may create conflicts if the results are somehow harmful or simply negative for the company concerned (Veltri, 2020). Researchers eager to ask for more data, or data useful to their research projects, have faced the blackboxing strategies employed by online platforms (Bonini, Gandini 2020), that, through deflection and silence, have constantly avoided their requests6. As noted by Bruns (2019), alternative data access frameworks, such as Facebook’s partnership with the controversial Social Science One initiative, represent an insufficient replacement for fully functional APIs7, and the platform providers’ actions in responding to the Cambridge Analytica scandal raise suspicions that they have instrumentalised it to actively frustrate critical, independent, public interest scrutiny by scholars. (p. 1544) While earlier social media researchers have contributed to debanalise (Rogers, 2014) social media platforms by documenting their increasingly important informational, social, or commercial roles, the

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subject matter of much current research focus on the most prominent contemporary social media phenomena (such as disinformation and hate speech) is already object of public criticism.

Detailed Explanatory Box: Best Practices in Social Research With Online Platforms Data Even if the partnerships between online platforms and certain scholars and the tools made available to a part of the academia have been considered not satisfactory for the growth of the digital social research, some interesting research projects have emerged, especially on Facebook data. Using Crowdtangle data and the dataset made available through a Social Science call for proposal, Giglietto and colleagues (2019) have analyzed the social media shares of some links on both Facebook and Instagram, by looking at the news stories shared by multiple Facebook and Instagram accounts, pages and public groups. The authors have so identified several networks that repeatedly acted coordinated inauthentic behaviour, finding out that news stories shared by these networks of coordinated actors have received a higher volume of Facebook engagement than other ones. Afterwards, the same research team, using the Condor URLs dataset available in FORT – Facebook Open Research and Transparency (see next box), has analyzed a network of 10 Facebook pages that consistently shared the same links at approximately the same time (using the same methodology already developed in the previous research activities to detect coordinated link sharing behaviour), finding out coordinated hateful disinformation strategies on Italian politics and relevant social issues such as immigration (Giglietto et al., 2021) . Using instead Facebook and Google Ad Libraries data, Simon Hegelich and colleagues (2020) have deeply investigates the possibilities and limits presented by the newly created ad libraries to analyze online political campaigns. The authors selected Germany as a case study and focused on the months leading up to the 2019 elections to the European Parliament, identifying the political actors that were active advertisers, compared their spending, and contrasted the number of ad impressions with user engagement on their organic online content. Furthermore, the authors have explored regional and demographic distributions of users reached by the advertisements and used them as a proxy for the advertisers’ targeting strategies. They have also compared the success of the ad campaigns on boosted Facebook posts. At the end of their analyses, specifically directed at investigate the use of microtargeting in online political campaigns, the authors have found that even though all the major German political parties engaged in online ad campaigns, they kept their attempts at microtargeting to a minimum. In parallel, policymakers and regulators from Western countries have encountered similar issues: the US Congress has repeatedly faced the difficulty to receive official and detailed pieces of information during some official hearings with online platforms representatives, even CEOs, both in wider public debates, such as the Senate Committee on Commerce, Science and Transportation on Section 230 of the United States Communications Decency Act, and in specific investigations, such as the Online Platforms and Market Power one carried out by the House Judiciary Subcommittee on Antitrust, Commercial, and Administrative Law. Even the European Commission, which has been able to organize a stable exchange of data and information with online platforms within the frameworks of Code of Conducts on some relevant social media phenomena (such as hate speech and disinformation)8, has repeatedly reported the enormous difficulties in receiving the data necessary for their monitoring activities, even to the point of asking online platforms more accountability and transparency in the recent legislative proposal on Digital Markets and Digital Services Acts (EU Commission 2020a)9.

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In the same months, researchers have started to lobby and create public advocacy campaigns to push platforms for different and better data access frameworks for scholarly research (e.g. Knight First Amendment Institute, 2018), and national policymakers have faced investigations and legislative proposals regarding online content issues (disinformation, hate speech, enhancement of journalistic content, and so on)10. Therefore, these two actors, apparently so far away, have found themselves united against the same counterpart. Among the researchers, a consideration has arisen regarding the need to approach platforms not one researcher (or research team) at a time, but with more coordinated strategy for pressing this issue forward, both with the platforms themselves and with policymakers (Tromble, 2021). At the same time, policymakers have started to actively involve the researchers in their activity, consulting them for reports (see ERGA, 2020a, 2021) and legislative initiatives.

MAIN ISSUES AND ACTORS: THE ESTABLISHMENT OF AN ISSUE NETWORK In light of the important role of social research in tackling online disinformation campaigns and the involvement of social researchers by online platforms to fight disinformation as well, some policymakers giving attention to this issue have started to investigate the relationships between online platforms and researchers. In particular, the European Commission, after launching a Code of conduct on disinformation (2018), has asked other public and private actors to monitor its application by signatories online platforms, such as Facebook, Google, Twitter, Microsoft and, more recently, TikTok, looking even to the research community engagement (ERGA, 2020a; VVA, 2020)11. Beyond the specific problems highlighted regarding the archives of political and issue-based advertisements, it was highlighted, in particular by ERGA and by the experts consulted by the regulatory authorities for audiovisual media services of the European Union member States, that online platforms should commit to provide a large amount and variety of data, since not all research projects need the same data, and therefore accessible data should be defined according to specific research interests and not by the online platforms themselves. In particular, the conclusions of the ERGA Report on disinformation (2020a) have stressed that the contacts made with the universities and the researchers have clearly shown that the platforms provided very little (if any) access to data for independent investigations. The research community has underlined some critical issues such as the lack of useful, measurable and researchable data, including data on ad targeting and user engagement with disinformation, and the inadequateness of the ad libraries provided by online platforms in supporting in-depth systematic research into the spread and impacts of disinformation in Europe. Furthermore, the scholars interviewed by national regulatory authorities (NRAs) have stressed that: (i) not all projects need the same data; (ii) accessible data should be defined by the specific research interest and not by a company granting access on its own terms; (iii) there is no mechanism to effectively assess the quality of the data. To their defence, the platforms have argued that they cannot provide free access to data because of privacy and data security reasons. Therefore, the ERGA Report has stressed the need for a set of recommendations aimed at improving the relationships between online platforms and researchers12. According to the stakeholders involved in VVA independent study (2020), the pillar of the Code aimed at empowering the research community is the one which has proven to be the least advanced. Most of them noted that there is limited engagement with the research community and that the tools 69

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set up by platforms are still too weak. Even though some stakeholders agreed that Crowdtangle is an example of a good research tool, they noted that the tool is also owned by Facebook, hence they see a conflict of interest. The lack of transparency regarding the access to data is a common concern raised by the researchers interviewed. In particular, the Ad Archives of Google and Facebook are not seen as fit for purpose by some stakeholders. The data that could be extracted were deemed unreliable and it was noted that the archives were damaged with bugs, which ultimately made these tools effectively useless as a transparency tool for researchers, journalists, or stakeholders for whom this data was intended. Many stakeholders hence denounced the lack of user-friendliness of the platforms’ databases. Furthermore, many researchers report that access to platforms’ data has not improved after the establishment of the Code. Some academics also raised the issue that the choice of platforms to grant access to researchers sometimes seemed arbitrary and that this has increased distrust between platforms and the research community. Such public and private stakeholders involved in the Code of Practice on Disinformation monitoring have gone beyond the funding of specific research projects and the partnerships build around specific events, and, thanks to the voice of the researchers, have faced issues arising from the research community, that is asking for more data and more people (not only academic, but event independent and NGOs reseachers) being able to access the data. In the meanwhile numerous online media measurement, market research or business consultancy companies have had the opportunity from Facebook to access an important amount of data according to a path that is well differentiated from academic researchers, and based on a measurement partnership model13. Anyway, it should also be noted that access to data has been highlighted as an important issue by the same regulatory authorities involved in online platforms monitoring (EU Commission, 2019) 14, and even some non-governmental organizations have reiterated the important link between data access and platform governance (Ausloss et al., 2020). Therefore, different actors, such as policymakers, NGOs and researchers themselves, started to take an interest in providing a solution, coming from the public sector, but shared with any stakeholder, online platforms included, regarding the research activities to be undertaken in the digital ecosystem. Since any actor involved in this issue, apart some specific fringes, is seriously intentioned in finding out a way to conduct better and high-quality research projects on online platforms data (NGOs and researchers), and to face somehow the negative phenomena associated with the role of online platforms in public discourse, such as disinformation or hate speech (public actors), the involvement of online platforms, owning these data and governing their own policies, remains still relevant. At the same time, a public stakeholder aegis seems to be essential to not get the issue in the hands of online platform themselves self-regulation, which is no longer considered the best solution (Helberger, 2020). Even thanks to some specific initiatives at EU or national level, an issue network – made by policymakers interested in expanding their role in online ecosystem governance (e.g. NRAs in audiovisual media services field)15, researchers, in particular digital sociologists, interested in improving their ability to analyze social media data and eager to be accredited as a point of reference in the study of social media effects on citizens behaviour and democracy functioning both in the research ecosystem and in the public opinion, and NGOs expanding their interest in the digital issues as well16 – is emerging. As pointed out by the main scholar dealing with this notion (Heclo, 1978), this specific issue network is dispersed and composed by numerous and different players (interest groups and individuals as well) moving in and out of transitory sub-networks, and driven not by direct material interests but by intellectual 70

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or emotional commitment, associated with a deep knowledge of the issues at stake. This issue network therefore seems to be particularly relevant to highly intricated and technical policies to be undertaken in the next years in the digital ecosystem governance. Furthermore, since some innovations in specific sector based on information system could be difficult to implement and be accepted (Overman, Simanton, 1986), in this case specially by online platforms themselves, such an issue network can be more effective than traditional ways of shaping the public agenda involving only public stakeholders and representatives of interest groups.

LOOKING FOR THE SOLUTIONS As it has been previously noted, even thanks to the operational aspects of doing research with online platforms called into question by researchers and NGOs, the access-to-data approach has become more and more relevant in the policymakers effort to analyze and manage the issues arising from the relationships between online platforms and researchers. Since, as argued by Walker et al. (2019), growing data access gap not only hinders research of public interest, but may also preclude researchers, NGOs and policymakers from identifying meaningful research questions as activity on social platforms becomes increasingly more inscrutable and unobservable, in a typical way of functioning of these unprecedent coalitions in this field, issue network members have therefore reinforced each other’s sense of issues as their interests, rather than letting interests defining specific positions on some issues, and are making progress by talking, debating and arguing the alternatives day-by-day (Heclo, 1978). Even the online platforms (or at least some of them) have begun to face these issues to seem more accountable to researchers and NGOs’ eyes and look at increasing pressures of public actors. When ERGA has deeply investigated the relationships between research community and online platforms, scheduling some meetings with researchers in the light of the preparation of a specific report (ERGA, 2021), the latter ones have showed to regulators representatives how in the last months there have been some initiatives set forth by the online platforms themselves, establishing partnerships with some researchers or groups of researchers or launching funding schemes aimed to scholars, to improve access to their data sets. However, these initiatives seem to be addressing again only a few researchers, notably the most famous ones, and they do not look like the definitive solutions to the issues the research community is facing. According to ERGA (2021) and to Code of Practice on Disinformation itself, systematic and generalised access to data owned by online platforms is needed, and such type of access is still not available; in this sense, ERGA analysed the problems that young scholars (not academics or independent researchers) and NGOs are facing in accessing the data owned by the online platforms, too. Furthermore, some of the new tools deployed by the online platforms have been specifically addressed to academic researchers, in particular to scholars affiliated with public and private universities, and independent researchers, even working for NGOs, are still facing enormous difficulties in accessing to these tools. Some of the NGOs representatives, who have been consulted by ERGA during webinars and private meetings, have indeed complained about the different treatment carried out by online platforms in this regard.

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Detailed Explanatory Box: Online Platforms Data Access Solutions: The Cases of Facebook and Twitter In the latest months, online platforms have faced the issues regarding the relationship with the research community and the access-to-data for scientific purposes, with different perspectives, solutions and degrees of development. While it is too early to describe and assess the approach and the tools proposed by Google, TikTok, Microsoft and Mozilla, some insights about Facebook and Twitter can be underlined.

Facebook In the last months, Facebook has opened the access to Crowdtangle to scholars and researchers affiliated to public and private universities17. According to the Crowdtangle team, the access is currently prioritizing university researchers (faculty, PhD students, post-docs) focused on misinformation, elections, COVID-19, racial justice, well-being18. Furthermore, Facebook has released different kinds of datasets for research purposes19, including a dataset about US elections held in November 202020, and has deployed a specific platform, accessible only to accredited scholars, intending to provide researchers with the tools and the data they need to study Facebook’s impact on the world, with a focus on elections, democracy and well-being, through specific datasets on users attitudes and behaviour. In particular, through this initiative, called Facebook Open Research and Transparency (FORT), scholars who have applied and have been accredited can access Ad Targeting Transparency Data Sets including targeting information for more than 1.65 million social issues, electoral, and political Facebook ads that ran during the three months prior to Election Day in the United States, from August 3 to November 3, 202021, and to URL Shares Data Set, including differentially private individual-level counts of the number of people who viewed, clicked, liked, commented, shared, or reacted to any URL (for any URL with at least 100 public shares) on Facebook between January 2017 and July 201922. Data for Good Program is another Facebook initiative specifically directed to researchers, which includes tools built from privacy-protected data on the Facebook platform, as well as tools developed using commercially and publicly available sources like satellite imagery and census data. With specific reference to the COVID-19 health emergency, Facebook launched, in partnership with Carnegie Mellon University (CMU) and University of Maryland (UMD), the COVID-19 Symptom Survey, asking their users about how they are feeling, including any symptoms they or members of their household have experienced and their risk factors for contracting COVID-1923. Country and region-level statistics are published daily via public API and dashboards, and microdata are available for researchers via data use agreements. While the CMU and the UMD have made the aggregated data from these surveys publicly available, Facebook and partnering universities created a portal to provide eligible academic and nonprofit institution researchers with information about how to request access to non-aggregated survey data for research purposes. The sharing of non-aggregated data is intended to help facilitate more advanced modelling and forecasting efforts by researchers aiding public health responses around the world. Finally, in December 2020, Facebook created four datasets dedicated to economic recovery during the COVID-19 emergence, to help researchers, nonprofits and local officials identify which areas and businesses may need the most support:

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• • • •

Business Activity Trends – in partnership with the University of Bristol, aggregating information from Facebook Business Pages to estimate the change in activity among local businesses around the world and how they respond and recover from crises over time; Commuting Zones - a dataset aimed at giving visibility to geographical areas in which the commuters spend most of their time between home and work, regardless of administrative boundaries, so becoming a crucial tool for providing input to economic analysis; Economic Insights from the Symptom Survey, including new insights about whether people in different occupations are worried about their household finances, as well as if they have experienced disruptions in employment. New Waves of the Future of Business Survey, including data from monthly surveys on small businesses on Facebook, built with the World Bank and Organisation for Economic Cooperation and Development, to determine the effects of the global pandemic on their operating status, their employees and their business needs24.

Twitter Since 2006, Twitter’s APIs have become a relevant data source for academics and have been used around the world in a wide range of fields. Academic researchers have used data from public conversations to study topics as diverse as the conversation topics on Twitter itself (e.g. state-backed efforts to disrupt the public conversation, floods and climate change, attitudes and perceptions about COVID-19, efforts to promote healthy conversation online and so on). Nowadays, according to Twitter itself, academic researchers are one of the largest groups of people using the Twitter API. Twitter has constantly tried to help academic researchers use Twitter data for their research purposes. For example, in April 2020, the platform launched the COVID-19 stream endpoint, the first free, topicbased stream built solely for researchers to use data from the global conversation for the public good25. At the beginning of 2021, Twitter made available to scholars and academic research community members a new Academic Research product track, allowing qualified researchers to access to all v2 endpoints released to date, with particular reference to: • • • •

free access to the full history of public conversation via the full-archive search endpoint, which was previously limited to paid premium or enterprise customers; higher levels of access to the Twitter developer platform for free, including a significantly higher monthly Tweet volume cap of 10 million (20x higher than what’s available on the Standard product track today); more precise filtering capabilities across all v2 endpoints to limit data collection to what is relevant for your study and minimize data cleaning requirements; new technical and methodological guides.

Access to this new product track is available to researchers by applying for access with the Academic Research application, and using a new developer portal, an additional application step needed to protect the security and privacy of people using Twitter. In particular, a manual review process was provided by Twitter to allow access to the Academic Research Product Track. First, applicants should meet three requirements:

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• • •

be either a master’s student, doctoral candidate, post-doc, faculty, or research-focused employee at an academic institution or university. present a clearly defined research objective, and specific plans for how Twitter data are going to be used, analysed, and shared for research purposes. use the product track only for non-commercial purposes.

Twitter has outlined how this product track is a significant shift in the type of data it makes available for free to third-party academic researchers interested in studying user behaviours and trends related to online discourse26. Therefore, what ERGA (2021) has repeatedly stressed is the importance of laying down the conditions for improving the cooperation between scholars and platforms in a way that does not depend on the various initiatives launched by the different online platforms and addressed only to well-known researchers27. According to the research community representative heard by ERGA, only few of the initiatives set forth by the platforms seem to be going in this direction (giving access to specific data owned by the platforms to vetted researchers). Many more efforts will have to be done by the platforms before the commitments of the Code’s pillar E may be considered complied with. At the same time, according to ERGA itself, researchers are fully convinced that only the access to APIs or raw data could be useful to academic and independent research activities, proprietary datasets being a solution convenient only to online platforms themselves and to the so-called secondary research activity. Researchers need indeed to analyse unstructured data, study the ways these data have been generated, and therefore have access to a vast amount of data to being managed and scrutinised. The role of European Digital Media Observatory (EDMO)28 to that end could be relevant as well. Designing a framework to ensure secure and privacy-protected access to platforms’ data for academic researchers is indeed one of EDMO’s main goals29, and the cooperation between EDMO and ERGA, each of them in respect of their respective roles, will be crucial to strike a balance between the various interests at stake and to provide clear indications on how to properly implement the community research pillar (the E one) of the Code of Practice. Anyway, the analysis carried out by ERGA highlighted that, to comply effectively with the commitments of the Code’s Pillar E, platforms should build a research ecosystem based on: 1) access to application programming interface (APIs) for research purposes, or availability of a tool allowing researchers to access to raw data (even regarding deleted pieces of content), and free access to ad archives (or similar archives) APIs; 2) identification of access requirements not penalising young and independent (NGOs) researchers even not affiliated to universities; 3) institution of a systematic cooperation between relevant stakeholders e.g. involving EDMO representatives, representatives from research community and independent regulatory authorities working on dataset and APIs jointly with online platforms, in charge of addressing specific issues about doing research with platforms and solving eventual disputes between platforms and researchers. According to ERGA again, to support the implementation of these measures, the access to data issues should be promptly covered by the Code of Practice on Disinformation 2.0 and some KPIs regarding this issue should be considered in the monitoring activities carried out by ERGA or other relevant actors. Since access to data seems to be an important way by which NRAs could make the online platforms 74

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more and more accountable, some measures aimed at individuating specific ways by which public sector stakeholders could access data needed for their monitoring and supervising activities (e.g. the provision of a basic set of row data regarding specific issues such as content moderation, partnership with factcheckers, tackling hate speech and cyberbullying) should be taken in account as well. In this regard, a new version of Code of Practice on Disinformation should include some specific KPIs relating to the Pillar E Empowering the research community which should refer to the European Union territory as a whole and to the EU Member States: A) Structural qualitative indicators ◦◦ Availability of policies ensuring the connection between platforms and research community, even trough the provision of specific access-to-data tools (or even allowing the access through APIs). B) Service-level quantitative indicators ◦◦ Amount of raw data made available to academic/research organisations through specific tools (or even allowing the access through APIs); ◦◦ Number of specific requests for data received; ◦◦ Number of specific requests for data followed up. Even some NGOs dealing with platform society issues state that meaningful research access is a precondition for informed and effective platform governance (Ausloss et al. 2020). Looking at civil society organization positions included in an ERGA paper on notions of disinformation (2020b), AlgorithmWatch wishes for binding disclosure and data access obligations based on the technical functionalities of the platform service. Furthermore, looking the potential threats to freedom of expression caused by increased reliance on automated content moderation, AlgorithmWatch has called on tech platforms to provide researchers with data about which takedowns did not receive a human review, whether users tried to appeal the takedown, and reports that were not acted upon. Even Avaaz, in its recent reports on disinformation (2021a, 2021b) has remarked how data points are important as a means of analysing the platform’s effectiveness in combating misinformation and is therefore calling platforms, especially Facebook, to collaborate with researchers by giving access to meaningful data, so much that the legislative recommendations proposed by the organization itself for tackling online disinformation include the online platforms transparency, considered essential since governments, civil society, and the general public should be informed about the nature and scale of the threat, and about the measures being taken to guard against it. The research community, especially in social sciences field, is apparently the most engaged issue network actor in this issue and some of the most recurrent considerations made by its representatives have been already remarked in this paragraph. Anyway, researchers should take in account some critical remarks on doing research with (online) platforms (data). According to some scholars (such as Tromble, 2021) indeed, the digital research in the API age has not always been adequately rigorous, has failed to examine and acknowledge the limitations of digital data, and has too often been motivated by expedience, rather than societal value. Since scholars of the digital are particularly well-positioned to analyse and unpack some of the most pressing social, cultural, economic, and political questions of our time30, rather than focusing on getting more data from the platforms, they should focus on getting high-quality data31. Starting from this point, as researchers:

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if we hope to convince platforms, policymakers, and the public that further academic data access is warranted, we will have to offer concrete proposals for specific protocols and safeguards. (It will certainly be better if our community identifies and proposes such safeguards, rather than having them dictated to us by the platforms.) We also need to think more carefully about how we manage data risks that vary over time (Tromble, 2021, p. 7) Otherwise, according to the digital methods advocates, “apart from calls to drop the API and return to digital ethnography, user studies and other small data research practices, reactions to such obstacles erected by social media companies more in line with digital methods include continuing technical fieldwork as well as API critique” (Rogers, 2018, p. 565). After the APIcalypse (Bruns, 2019), digital methods scholars have indeed stressed the need to return to digital fieldwork (Venturini, Rogers, 2019), adopting a reflexive posture useful to understand and support the work of the social and political actors who strive to repurpose online platforms for a healthy public debate as well. Among digital methods scholars, a methodological approach called “following the natives” (Caliandro, 2021) is emerging. This approach means taking advantage of the natively digital methods through which social media users manage their own data and paying attention to how users use digital devices, i.e. observing the strategies through which users use those devices that structure streams of online communication. Since this approach, which is somehow proving to be appropriate in studying specific issues and specific platforms (Semenzin, Bainotti, 2020), remains useful only in qualitative studies and in case study analysis, and since other bottom-up solution, such as adopting a pragmatic partnering with the users to do research, rather than with the platform owners (Halavais, 2019), seem to be quite unpromising in expanding the borders of digital social research, researchers should adjust their mind and start thinking that the ideal goal of the brand-new issue network focused on (online platforms) data-access issues could be finding a market solution, even adapting some operative aspects from other industries (Ausloos et al., 2020). In this sense, the permanence and, in certain ways, the institutionalization of the brand-new issue network deployed on the data-access issues could be really effective in promoting stable and mutual means of collaboration between research community (not only academic one) and online platforms, even with enormous advantages for public actors trying to deal with phenomena so difficult to analyse and tackle, who could benefit from constantly updated research notes coming from NGOs and academia and an online platforms’ greater predisposition in sharing unstructured data or pieces of information. While the cooperative responsibility, shared by platforms themselves, users and public institutions seems to be the best solution aimed at fulfilling public values in the digital ecosystem (Helberger et al., 2018), the permanence of an issue network made by platforms, researchers and public actors could be the best way to make the access-to-data framework useful to realize public values in platform-based public activities, even through new forms of data-driven regulation. Finally, as regards the institutionalization, the establishment of specific committees and roundtables coordinated by public actors dealing with legislative and regulatory issues in the online platforms domain, such as the regulatory authorities in audiovisual media service, in application of the new Audiovisual Media Services Directive or acting as national Digital Service Coordinators in the forthcoming Digital Services Act framework, could certainly be a flexible but decisive way to face an issue (the access to data owned by online platforms for research purposes) so changeable and involving technical aspects.

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REFERENCES AGCOM. (2018). Elezioni Europee, per contrastare fake news, OTT svolgano ruolo più proattivo e forniscano all’Autorità le informazioni tecniche necessarie. Press release. Retrieved from: https://www. agcom.it/documents/10179/12455065/Comunicato+stampa+25-10-2018+1540483289664/f2faf5811bfd-4c63-bde3-8b320b4ce29e?version=1.0 Amaturo, E., & Aragona, A. (2019). Per un’epistemologia del digitale: Note sull’uso di big data e computazione nella ricerca sociale. Quaderni di Sociologia, 81(63), 71–90. doi:10.4000/qds.3508 Amaturo, E., & Punziano, G. (2017). Blurry Boundaries: Internet, Big-New Data, and Mixed-Method Approach. In N. C. Lauro, E. Amaturo, M. G. Grassia, B. Aragona, & M. Marino (Eds.), Data Science and Social Research (pp. 35–55). Springer. doi:10.1007/978-3-319-55477-8_5 Andrejevic, M. (2009). Exploiting YouTube: contradictions of user-generated labour. In P. Snickars & P. Vonderau (Eds.), The YouTube Reader (pp. 406–423). National Library of Sweden. Andrejevic, M. (2013). Infoglut: How too much information is changing the way we think and know. Routledge. doi:10.4324/9780203075319 Andrejevic, M. (2014). Big data, big questions: The big data divide. International Journal of Communication, 8, 17. Ausloos, J., Leerssen, P., & ten Thije, P. (2020). Operationalizing Research Access in Platform Governance. Algorithm Watch. Retrieved from: https://algorithmwatch.org/wp-content/uploads/2020/06/ GoverningPlatforms_IViR_study_June2020-AlgorithmWatch-2020-06-24.pdf Avaaz. (2021a). Left Behind: How Facebook is neglecting Europe’s infodemic. Retrieved from: https:// secure.avaaz.org/campaign/en/facebook_neglect_europe_infodemic/ Avaaz. (2021b). Facebook’s Climate of Deception: How Viral Misinformation Fuels the Climate Emergency. Retrieved from: https://secure.avaaz.org/campaign/en/facebook_climate_misinformation/ Bastos, M. T., & Mercea, D. (2019). The Brexit Botnet and user-Generated Hyperpartisan news. Social Science Computer Review, 37(1), 38–54. doi:10.1177/0894439317734157 Bonini, T., & Gandini, A. (2020). The Field as a Black Box: Ethnographic Research in the Age of Platforms. Social Media + Society, 6(4), 2056305120984477. doi:10.1177/2056305120984477 boyd, d., Crawford, K. (2012). Critical questions for Big data. Information, Communication & Society, 15(5), 662–679. Bruns, A. (2019). After the ‘APIcalypse’: Social media platforms and their fight against critical scholarly research. Information Communication and Society, 22(11), 1544–1566. Bruns, A., & Burgess, J. (2015). Twitter hashtags from ad hoc to calculated publics. In N. Rambukkana (Ed.), Hashtag publics: The power and politics of discursive networks (pp. 13–28). Peter Lang. Caliandro, A. (2021). Repurposing Digital Methods in a Post-API Research Environment: Methodological and Ethical Implications. Italian Sociological Review, 11(4S), 225–242.

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Caliandro, A., & Gandini, A. (2019). I metodi digitali nella ricerca sociale. Carocci. Commission, E. U. (2018). Code of Practice on Disinformation. Retrieved from: https://ec.europa.eu/ digital-single-market/en/code-practice-disinformation Commission, E. U. (2019). Joint Communication to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions “Tackling covid-19 disinformation - getting the facts right”. Retrieved from: https://eur-lex.europa.eu/legal-content/ EN/TXT/?uri=CELEX%3A52020JC0008 Commission, E. U. (2020a). Proposal for a Regulation of the European Parliament and of the Council on a Single Market For Digital Services (Digital Services Act) and amending Directive 2000/31/EC. Retrieved from: https://ec.europa.eu/info/sites/default/files/proposal_for_a_regulation_on_a_single_market_for_digital_services.pdf Commission, E. U. (2020b). Staff Working Document: Assessment of the Code of Practice on Disinformation - Achievements and areas for further improvement. Retrieved from: https://digital-strategy.ec.europa. eu/en/library/assessment-code-practice-disinformation-achievements-and-areas-further-improvement Di Mascio, F., Natalini, A., Barbieri, M., & Selva, D. (2021). The Role of Regulatory Agencies in Agenda‐Setting Processes: Insights from the Italian Response to the COVID‐19 Infodemic. Swiss Political Science Review. ERGA. (2020a). Report on disinformation: Assessment of the implementation of the Code of Practice. Retrieved from: https://erga-online.eu/wp-content/uploads/2020/05/ERGA-2019-report-published-2020LQ.pdf ERGA. (2020b). Notions of Disinformation and Related Concepts. Retrieved from: https://erga-online. eu/wp-content/uploads/2021/03/ERGA-SG2-Report-2020-Notions-of-disinformation-and-relatedconcepts-final.pdf ERGA. (2021). Report on the relationships between researchers and online platforms. ERGA. Freelon, D. (2018). Computational research in the post-API age. Political Communication, 35(4), 665–668. Fuchs, C., & Dyer-Witheford, N. (2013). Karl Marx@ internet studies. New Media & Society, 15(5), 782–796. Giglietto, F., Marino, G., Terenzi, M., Righetti, N., & Rossi, L. (2021). Coordinated Hateful Disinformation on Italian Politics and Social Issues, since 2017. Available at SSRN: https://ssrn.com/abstract=3777263 Giglietto, F., Righetti, N., & Marino, G. (2019). Understanding Coordinated and Inauthentic Link Sharing Behavior on Facebook in the Run-up to 2018 General Election and 2019 European Election in Italy. Available at: https://osf.io/preprints/socarxiv/3jteh/ Halavais, A. (2019). Overcoming terms of service: A proposal for ethical distributed research. Information Communication and Society, 22(11), 1567–1581. Heclo, H. (1978). Issue networks and the executive establishment. In A. King (Ed.), The New American Political System (pp. 87–124). American Enterprise Institute for Public Policy Research.

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Hegelich, S., Medina Serrano, J. C., & Papakyriakopoulos, O. (2020). Exploring political ad libraries for online advertising transparency: lessons from Germany and the 2019 European elections. International conference on social media and society, 111-121. Helberger, N. (2020). The political power of platforms: How current attempts to regulate misinformation amplify opinion power. Digital Journalism, 8(6), 842–854. Helberger, N., Pierson, J., & Poell, T. (2018). Governing online platforms: From contested to cooperative responsibility. The Information Society, 34(1), 1–14. Hemsley, J. (2019). Social media giants are restricting research vital to journalism. Columbia Journalism Review. Retrieved from: https://www.cjr.org/tow_center/facebook-twitter-api-restrictions.php?fbclid=I wAR1uULkcGqcOrQYSagYkfSaKciKGK5t2x_Q5hnoOd38CGs02ND_oVULdpns King, G., & Persily, N. (2018). A New Model for Industry–Academic Partnerships. PS, Political Science & Politics, 1–7. Kitchin, R. (2014). Big Data, New Epistemologies and Paradigm Shifts. Big Data & Society, 1, 1–12. Knight First Amendment Institute at Columbia University. (2018). Knight Institute Calls on Facebook to Lift Restrictions on Digital Journalism and Research [open letter]. Retrieved from https://knightcolumbia.org/sites/default/files/content/Facebook_Letter.pdf Lupton, D. (2018). Sociologia digitale. Pearson Italia. Manovich, L. (2011). Trending: The promises and the challenges of big social data. Debates in the Digital Humanities, 2(1), 460-475. Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt. Overman, E. S., & Simanton, D. F. (1986). Iron triangles and issue networks of information policy. Public Administration Review, 584–589. Rieder, B. (2013). Studying Facebook via data extraction: the Netvizz application. WebSci ‘13 Proceedings of the 5th Annual ACM Web Science Conference, 346-355. Rieder, B. (2018). Facebook’s app review and how independent research just got a lot harder. Retrieved from http://thepoliticsofsystems.net/2018/08/facebooks-app-review-andhow-independent-research-justgot-a-lot-harder/ Rogers, R. (2013). Digital methods. MIT Press. Rogers, R. (2014). Foreword: Debanalising Twitter: The transformation of an object of study. In Twitter and society. New York: Peter Lang. Rogers, R. (2018). Social media research after the fake news debacle. Partecipazione e Conflitto, 11(2), 557–570. Schroepfer, M. (2018). An update on our plans to restrict data access on Facebook. Retrieved from: https://newsroom.fb.com/news/2018/04/restricting-data-access/

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Semenzin, S., & Bainotti, L. (2020). The use of Telegram for the non-consensual dissemination of intimate images: gendered affordances and the construction of masculinities. SocArXiv Papers. Retrieved from: https://osf.io/preprints/socarxiv/v4f63/ Srnicek, N. (2018). The Platform Capitalism. Polity Press. Stefanizzi, S. (2021). The Use of Big Data: Some Epistemological and Methodological Considerations. Italian Sociological Review, 11(4S), 193–205. Tromble, R. (2021). Where Have All the Data Gone? A Critical Reflection on Academic Digital Research in the Post-API Age. Social Media+ Society, 7(1). Tromble, R., Storz, A., & Stockmann, D. (2017). We don’t know what we don’t know: When and how the use of Twitter’s public APIs biases scientific inference. Social Science Research Network. Retrieved from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3079927 Van Dijck, J., Poell, T., & De Waal, M. (2018). The platform society: Public values in a connective world. Oxford University Press. Veltri, G. A. (2020). Digital social research. Polity Press. Venturini, T., Rogers, R. (2019). “API-based research” or how can digital sociology and journalism studies learn from the Facebook and Cambridge analytica data breach. Digital Journalism, 1–9. VVA – Valdani Vicari & Associati. (2020). Study for the assessment of the implementation of the Code of Practice on Disinformation. Available at https://ec.europa.eu/digital-single-market/en/news/studyassessment-implementation-code-practice-disinformation Walker, S., Mercea, D., & Bastos, M. (2019). The disinformation landscape and the lockdown of social platforms. Information Communication and Society, 22(11), 1531–1543.

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Looking at this issue from a different point of view, some scholars have harshly criticized the link between digital (neo-)capitalism (Srnicek, 2018) and the ownership of data by online platforms, highlighting how big data have been long and wrongly considered as independent from the context in which they are created (Andrejevic, 2013), mainly characterized by the unpaid work of the platforms users (Andrejevic, 2009; Fuchs, Dyer-Witheford, 2013). The Cambridge Analytica scandal has been primarly caused by the publication of Carole Cadwalladr’s investigative article in The Guardian on 17 March 2018. In the wake of the immediate scandal, Facebook soon announced further changes to the data access regimes for its public API services, providing substantial reductions to the functionality of Facebook’s Events, Groups, and Pages APIs (Schroepfer, 2018). The announcement also foreshadowed further API adjustments as well as reviews of data access permissions for existing apps ‘over the coming months’. Please note that these changes have been made without consulting third-party developers, researchers and even journalists (Hemsley, 2019), and that the speed of implementation of these adjustments has

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led a considerable number of third-party apps and services, including key research tools such as Netvizz (Rieder, 2018), to fail to operate correctly and to substantially die. Walker et al. (2019) point out that “high-volume data retrieved from APIs cannot directly replace low-volume web scraping data. Even if the volume of data collected using web scraping or APIs were identical, the metadata available via API requests is considerably different from metadata that is visible on the user-facing portions of a social media platform’s website used for web scraping” (p. 1536). According to Caliandro (2021), web scraping techniques have a controversial (ethical) status in social research methods: “in fact, although scraping is not illegal per se, nevertheless it is a practice opposed by social media platforms for a variety of reasons. First, this is because scraping carries the risk of crashing the website under study. (…) Second, this is due to the fact that scraping allows to collect data from users with private profiles – thus, to obtain information that users are not necessarily happy to share. Last, a similar issue concerns the willingness of social media companies to share data with developers and researchers (…) specifically, through scraping it is possible to access also those data that social media companies do not intend to share” (pp. 229-230). Finally, according to Venturini and Rogers (2019), some forms of scraping need to be considered a ‘necessary evil’ for social research, if performed conscientiously. Furthermore, according to Tromble et al. (2017), conclusions based on data from the public Twitter APIs are likely to be biased. This is particularly true for analysis of tweet content or interactions between Twitter users, as tweets with hashtags are over-represented in both Streaming and Search APIs, while user mentions are overrepresented in Streaming API samples and under-represented in Search API results. In particular, in April 2018, Facebook launched Social Science One, a very ambitious programme involving a commission of 83 academic researchers and a group of funders, with the goal of building a fair and transparent procedure to share the platform’s data with academic research community. One year later, in April 2019, Facebook announced a new set of research projects that will look into social media’s impact on democracy. The projects provided access to “privacy-protected Facebook data” to more than 60 researchers from 30 academic institutions across 11 Countries, in an attempt to help conduct research into a range of topics related to election campaign in Europe. To support these projects, Facebook built a first-of-its-kind data sharing infrastructure to provide researchers access to Facebook data in a secure manner that protects people’s privacy. On December 11, 2019, the members of the European Advisory Committee of Social Science One issued a public statement complaining about the lack of an adequate data access from Facebook. Surprisingly, on February 2020, Facebook has provided Social Science One with a large dataset, resulting from processing approximately an exabyte of raw data from the platform. According to Social Science One itself, this dataset will enable social scientists to study some of the most important questions of our time about the effects of social media on democracy and elections with information to which they have never before had access. To be fair, Rogers (2018) had altready noted that social media APIs did not differentiate between academic researchers and marketing companies or potential data resellers, so that, if one strives to configure a system for more comprehensive data collection (using multi-ple accounts, funnelling all data collected into one repository), one is treated as a spammer or reseller, blocked and actively worked against. In that sense, researchers had already been not regarded as a ‘good partner’. According to Bruns himself (2019), these schemes for scholarly data access, establishing a news paradigm for data access called corporate data philantropy, have been conceived partly in an effort 81

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to address the significant data access gaps caused by such shutdowns, and partly also to ward off the negative publicity resulting from the growing scholarly criticism of their heavy-handed new enforcement tactics. Anyway, they seem to be constructed primarily to generate positive publicity for the companies and provide access to data only for a vanishingly small minority of research projects that are carefully and explicitly selected to be ‘orthogonal’ to the platform’s corporate interests. On the opposite, Tromble (2021) states that initiatives such as Social Science Ones have brought some Facebook executives to genuinely support efforts to bring more rigorous social scientific and humanistic approaches into the heart of their companies’ work, as well as to find ways to support outside research, even producing new state-of-the-art system that uses differential privacy to mitigate data abuses and is likely to serve as a model for future platform–academic data sharing endeavours. We are referring to the Code of conduct on countering illegal hate speech online, released on 2016, and currently agreed by Facebook, Microsoft, Twitter, YouTube, Instagram, Snapchat, Dailymotion, Jeuxvideo.com and TikTok, and to the Code of practice on Disinformation, released on 2018, and currently agreed by several online platforms such as Facebook, Google, Twitter, Microsoft, Mozilla and TikTok (for more information about this Code of Practice, see the footnote n. 10). At national level, please note that the Italian Communications Regulatory Authority, AGCOM, dealing with audiovisual media services, radio, press, postal services, telecommunications sectors, and (on certain aspects) online platforms themselves, has publicly disclosed the lack of co-operation shown by online platforms, even by writing formal letters to platforms’ CEOs (AGCOM, 2018). In particular, Germany has already approved in 2017 a law on hate speech, obliging social network managers to remove obviously illegal content within 24 hours, and the most controversial ones within a week. In 2018, France has approved a specific law on information disorders, disinformation and elections period. The Australian “News Media and Digital Platforms Mandatory Bargaining Code”, approved on February 2021 after a huge discussion with online platforms, and after Facebook had blocked journalistic content on its platform for a week, has instead faced the asymmetric relationships between platforms and publishers, pushing an international debate on the enhancement of journalistic content on online platforms recently placed again at the heart of EU policy agenda by the Copyright Directive (that some countries such as France have immediately transposed into national law, other ones having instead adopted a wait-and-see approach), some years after the controversial Spanish Copyright Law approved in 2014, that has brought to the closure of Google News in that country. Following the Report of the High Level Expert Group on fake news and the EU Communication on fake news and disinformation, EU Commission has signed a Code of Practice on Disinformation with online platforms (see footnote n. 7) and representatives from advertising industry. The EU Action Plan on Disinformation (2019) has entrusted ERGA, the European Regulators Group for Audiovisual Media Services, with monitoring the Code of Practice implementation by online platforms by verifying the compliance with the committments taken by online platforms themselves along five pillars: • Positioning of advertisements (e.g. number of accounts removed due to violations of advertising policies); • Political and issue-based advertising (e.g. percentages of advertisements correctly classified as political-electoral on the total number of advertisements of this type distributed by the platforms); • Service integrity (e.g. number of bots or fake accounts removed);

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• Empowerment of consumers (e.g. use of specific tools by users); • Enhancement of collaborations with the research community (e.g. collaborations with factcheckers). ERGA has released an interim monitoring report in 2019 and final report in 2020. After that report, ERGA has continued its monitoring activity by releasing some guidelines on the relationships between online platforms and, respectively, fact-checkers, users and media literacy campaigns, and is going to release an additional report on relationshisps between online platforms and researchers, that will be discussed in the paragragh 3 of this chapter. During 2021, ERGA will carry out a specific monitoring activity on the compliance of online platforms with some specific commitments regarding tackling COVID-19 disinformation. Besides ERGA, EU Commission has assigned Valdani, Vicari & Associati to carry out an independent study on Code of Pratice on Disinformation implementation by online platforms (VVA, 2020) and has furthermore released a specific Staff Working Document (EU Commission, 2020b). In a report drafted for an NRA joining ERGA, Prof. Rebekah Tromble examined recent scholarly research on two issues at the heart of the Code of Practice – online political advertising, microtargeting, and disinformation – and sought to assess the extent to which this research has been enabled and supported by Google, Facebook, and Twitter. The report stated that very little scholarly research on online political advertising, micro-targeting, and disinformation has been based on data found in Facebook’s, Google’s, and Twitter’s respective ad archives and that even the more advanced academic-platform partnership, Facebook’s Social Science One, has been not so decisive in improving research on disinformation. Prof. Tromble provided several recommendations: • As part of their public ad archives, the platforms should provide more precise data on ad spending and impressions. • The platforms should also provide more precise targeting data in the ad archives. This should include direct targeting data, as well as information about categories targeted indirectly through the custom audience and lookalike features. • For sensitive categories (e. g., race or political ideology), audience reach data might be substituted for targeting data. Alternatively, sensitive targeting data could be reported to regulatory authorities, with researchers given the opportunity to access the data under controlled conditions. • The platforms should preserve deleted ad content, including content removed for violation of ad policies, for analysis by researchers. The platforms should provide formal analyses identifying their specific concerns regarding data sharing for independent academic research under General Data Protection Regulation (GDPR). Such analysis will provide a starting point for resolving areas of ambiguity and uncertainty. • In turn, Data Protection Authorities should offer formal guidance on permissible data sharing practices under GDPR. • Regulatory authorities should begin to require that the platforms share data for research purposes. The types and amounts of data should remain flexible, with priorities set based on public interest as defined by the regulatory authorities, in consultation with both the platforms and scholars. The platforms’ proprietary interests should not be neglected, but these should be balanced against the public’s interest in platform transparency. • The establishment of “safe harbours” should be promoted, to the aim of supporting independent scholarly research carried out on platform data. Models from the health and medical sectors, as well as the government statistics offices, could be consulted. 83

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Anyway, as regards commercial social media analytics services, Bruns (2019) had already noted that their price points, data offers, and analytics services are generally ill-suited to typical scholarly research needs, and that, in particular, data access – especially with respect to export for analysis in scholarly content or network analysis software – is often shallow and limited. In some cases, the issues researchers and policymakers are facing in collecting and analyzing data from online plaftorms are quite different, but they can converge on specific issues such as the need for analyzing posts that have been deleted by users or platforms (Bastos, Mercea, 2019). During the COVID-19 pandemic, some regulatory authorities, such as the Italian one, AGCOM, have acted as first mover at national level in the disinformation field, even thanks to some partnerships with data scientists (Di Mascio et al. 2021). Among the others, we can mention three NGOs dealing with digital platforms governance, and EU-level based: Avaaz, Access Now and AlgorithmWatch. Even the Social Science One team has worked closely with the CrowdTangle team over the past few months with the aim of making its data widely available to university researchers (more information at https://www.facebook.com/formedia/blog/crowdtangle-for-academics-and-researchers). See https://help.crowdtangle.com/en/articles/4302208-crowdtangle-for-academics-andresearchers#:~:text=CrowdTangle%20started%20a%20pilot%20program,and%20abuse%20of%20 social%20platforms Here we can find an official update: https://research.fb.com/data/. More information at https://about.fb.com/news/2020/08/research-impact-of-facebook-and-instagramon-us-election/. Specifically, this includes an Ad Targeting data set (the targeting information selected by advertisers running social issues, election, and political ads but as a privacy protective measure, excluding ads with fewer than 100 impressions) and an Ad Library data set (social issues, election, and political ads that are part of the Ad Library product) so that researchers can analyze the ads and targeting information in the same environment (more information at https://research.fb.com/blog/2021/02/ introducing-new-election-related-ad-data-sets-for-researchers/). The URL Shares Data Set has been the first dataset construced in the scope of the partnership between Facebook and Social Science One, and made accessibile to any researcher through a specific request for proposal process (more information about the launch of the URL Shares Data Set at https://socialscience.one/blog/unprecedented-facebook-urls-dataset-now-available-researchthrough-social-science-one). The survey is available in 56 languages. A representative sample of Facebook users is invited daily to report on symptoms, social distancing behaviour, mental health issues, and financial constraints. Sampled users receive the invitation at the top of their News Feed, but the surveys are conducted and collected off the Facebook app by our partners. Facebook does not collect, store, or receive survey responses, and university partners do not know who took the survey. The surveys may be used to generate new insights on how to respond to the crisis, including forecasting and modelling efforts. Facebook provides weights to reduce nonresponse and coverage bias. More information at https://research.fb.com/blog/2020/05/expanding-support-for-covid-19-research-through-thesymptom-surveys/ More information on the economic recovery datasets included in Facebook Data for Good Program at https://about.fb.com/news/2020/12/data-for-good-new-tools-to-help-small-businesses-andcommunities-during-the-covid-19-pandemic/

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The COVID-19 stream endpoint is a unique dataset that covers many tens of millions of tweets daily and offers insight into the evolving global public conversation surrounding the health emergency. The dataset has been made available for free from 29 April to 15 October, 2020. According to Twitter itself, this dataset should help academic researchers in analysing the spread of the disease, understanding the spread of misinformation, helping crisis management, emergency response, and communication within communities, and developing machine learning and data tools that can help the scientific community answer key questions about COVID-19. The COVID-19 stream endpoint provides access to COVID-19 and Coronavirus related public Tweets in real-time as defined by the criteria used to power this topic on Twitter. More information at https://blog.twitter.com/developer/ en_us/topics/tools/2020/covid19_public_conversation_data.html More information at https://blog.twitter.com/developer/en_us/topics/tools/2021/enabling-the-futureof-academic-research-with-the-twitter-api.html As regards the different chances of (online platforms) data-access for scholars coming from leading research or academic institutions and scholars coming from less-equipped universities, Veltri (2020) speaks expressly of academic inequality risk. Looking to a broad range of researchers and scholars, Walker et al. (2019) have argued: “More immediately, the creation of Social Science One as a gatekeeping body governing the relationship between Facebook and academics exemplifies a governance model that may widen the gap between data-rich industry researchers with connections to social platforms and independent researchers working outside corporations. This divide has been characterized as the gap between ‘big data rich researchers,’ who have access to proprietary data and might be working in the interests of the company employing them, and the ‘big data poor’, or the broad universe of academic researchers whose findings may be of public interest but may ultimately be critical of social media platforms (boyd & Crawford, 2012)”. The European Digital Media Observatory (EDMO) brings together fact-checkers, media literacy experts, and academic researchers to understand and analyse disinformation, in collaboration with media organisations, online platforms and media literacy practitioners. Under the leadership of the European University Institute in Florence (Italy), which relies on the expertise of its School of Transnational Governance and Centre for Media Pluralism and Media Freedom, EDMO is a partnership that also includes Datalab at Aarhus University, Athens Technology Center, which provides the technological support and is also coordinating the Social Observatory for Disinformation and Social Media Analysis (SOMA), and Pagella Politica. The consortium has been chosen following an EU Commission public call for tenders in 2020. In particular, at the end of 2020, EDMO launched a Working Group on ‘Access to Data Held by Digital Platforms for the Purposes of Social Scientific Research.’ The working group’s specific task will be to develop a Code of Conduct under Article 40 of the General Data Protection Regulation (GDPR), regarding the sharing of digital platform data for independent research purposes. In this sense, the EDMO initiative would reduce any potential legal uncertainties and risks for the platforms, and offer researchers a clearer route to data access, including sensitive data. Paradoxically, according to Tromble herself (2021), Cambridge Analytica —as well as myriad controversies concerning misinformation, bots, abuse and harassment, hate speech, and so on— could have opened “some actors’ eyes to the value of external academic research. Before these scandals, platforms rarely engaged with or supported outside, independent scholarship” (p. 2). Venturini and Rogers (2019) instead point out that “API restrictions may end up being a good thing if they encourage researchers to return to fieldwork. […] API querying itself can be a form 85

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of digital fieldwork when it is not a wholesale accumulation of big-for-thesake-of-big data, but a careful work of extraction carried out in collaboration with platforms and users” (p. 6). Tromble (2021) has underlined how in the API age digital scholars were exploiting bugs in platforms’ code and breakingterms of service to gather far more data than was technically permissible.

Section 2

Digital Data Collection and Capture This section will discuss issues related to data collection and capture by opening the discussion to elements such as primary and secondary data, scraping or APIs, data access, sampling biases, ethics, sociotechnical mediation, post demography, and representativeness.

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Chapter 7

Online Research Practice and Integrated Perspectives of Inquiry:

Dis(advantages) of Web and E-Mail Surveys Sergio Mauceri Sapienza University of Rome, Italy Maria Paola Faggiano Sapienza University of Rome, Italy Luca Di Censi Human Foundation Do, Italy & Think tank per l’Innovazione sociale, Italy

ABSTRACT The authors reconstruct the system of advantages and limits of e-mail data collection and web survey technique in social research; for this purpose, they examine in detail a set of studies that stimulate multiple reflections, both with reference to the overall value of survey research and on the role of the web for social sciences. The subject of all selected research designs is a complex social problem that involves the internet, both focus for observation and tool for research: voting intentions, social effects of the pandemic, the quality of university life, technology addiction. In each research experience, for different reasons—above all due to the lack of a single, self-sufficient data collection mode—, the authors favor the integration of research strategies: 1) mixed-modes of data collection, 2) follow-up panel web survey, 3) mixed methods research, 4) introduction of a preliminary pilot study, 5) multilevel survey.

INTRODUCTION The origin of the term mode of data collection cannot be traced to a single source and has become part of the lexicon of research since the 1970s (Groves and Kahn, 1979). Proposing a brief excursus on DOI: 10.4018/978-1-7998-8473-6.ch007

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modes of data collection, we have gone from post interviews to face-to-face interviews, which were the main modes of data collection from the 1940s (Lyberg and Kasprzyk, 1991), to the development and widespread adoption of telephone surveys, first in the United States and later in Europe and elsewhere since the 1970s, up to the web surveys of the early 1990s. Since the 1990s the use of the Internet has been rapid and widely recognised; just four years after its introduction, 50 million people worldwide were using the web. The internet usage rate has been impressive and has eclipsed all other previous technologies; just think that to reach this saturation level radio took almost 30 years and television 13 years. In 2018, users connected to the Internet in the world surpassed the 4 billion people threshold: a historical datum that shows how today more than half of the world population is online (Report Global Digital, 2018). The impact of the web was immediate and, to a certain extent, researchers were caught unprepared, even though in recent years we have witnessed an explosion in the use of the web to collect information previously collected in other ways. The turning point came in the mid-1990s, with the introduction of Hyper Text Mark-up Language (HTML): the web became an interactive medium and participation in computer-based surveys was perceived as easy and non-intrusive, respecting the anonymity of the interviewees. Moreover, with the spread of electronic mail, web surveys have been recognised for their potential to reach a very large public, guaranteeing a higher response rate in considerably less time/costs compared to paper or postal surveys (Ebert et al., 2018, Kehoe and Pitkow, 1996; Schmidt, 1997). Why should a researcher prefer to write an online questionnaire instead of using the proven pen and paper system or the different telephone interview methods? As mentioned, the use of online data collection is cheaper in terms of time and costs, as well as being more efficient in the data encoding phase. Indeed, using a computerised support database, the transcription time of the data coincides with the time that the participant takes to answer the questionnaire, since the operation of inserting the answer is automatic: in this way the errors of manual transcription of data are also reduced (Reis and Gosling, 2010; Vicente and Reis, 2010). Web surveys are now a practical and valuable resource for social scientists. The possibility of selecting populations that are connected and technologically expert, the low cost, the speed of delivery and response, the ease of compilation, in addition to the practical management of data cleaning, are all elements in favour of the Internet as a tool for research. Many authors (Boyle et al., 2016; Lindhjem and Navrud, 2011; Sakshaug, Yan, Tourangeau, 2010; Sills and Song, 2002; Watts, 1999; Smith, 1997) are in agreement in positively evaluating web surveys with respect to the following additional aspects: design flexibility, geographic scope, anonymity and contained error with respect to other methods of surveying such as postal, telephone or face to face. Another important advantage of web surveys is that through virtual communities a researcher can gain access in a short space of time to groups of people who share specific interests, attitudes, beliefs and values ​​about a problem or an activity, even where they are separated by large geographical distances (Garton et al. 2003; Taylor 2000). A researcher interested in probing hard-to-reach populations can quickly gain access to a large number of such individuals by posting invitations to participate in newsgroups, chat rooms and blogs. With the face-to-face interviews the process would expand and it would be more complicated to find an equivalent number of people with specific attributes, interests and attitudes in a single physical place. For example, researchers can find a concentrated number of individuals who use streaming platforms or online or console gaming communities, or find populations that are difficult to reach, such as patients suffering from a rare disease by using forums or hashtags.

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Furthermore, a web survey can include the presentation of texts, audio, video or other stimulus materials to integrate the questionnaire and support the compilation of the questions (Couper, 2005). Over the years, the modes of data collection have followed and been compared to one another, and have eventually becoming complementary due to three related trends: 1) the proliferation of data collection modes; 2) the greater complexity of the modes and 3) the rise of the mixed methods approaches (see sections 2 and 5). As Couper (2011) argues, these three trends redefine how data is collected as a multidimensional construct. In this perspective, a web survey appears to be a highly flexible data collection method that can be integrated with others (Patrick et al., 2021). The validity of the use of digital questionnaires has been repeatedly questioned because it presents the problem of sampling bias (see section 2), which constitutes the nucleus of the heaviest criticisms related to the web survey (Andrade, 2020; Ball, 2019; Van Selm and Jankowski, 2006). Moreover, the absence of the interviewer has important repercussions on data quality, since it is not possible to support the compilation of the questionnaire with interventions: that help to interpret the questions and the alternatives of response according to the intentions of the researcher; which incentivise the deepening of answers to open questions; that can reduce the risk of distortion like the response set1. Nevertheless, several studies show that online questionnaires, if used wisely, can replace, in specific research opportunities, the traditional modes of data collection (Wieters, 2016; Shih and Fan, 2008). The following paragraphs will illustrate three virtuous cases of web surveys, highlighting the potential and the limits of this approach of surveying, as well as possible forms of combining information gathering strategies in the same research design. The aim is to underline that in Social Sciences this approach can also be used to answer complex questions. The studies presented also contribute to defining some strategies aimed at containing the risks of bias, with a view to responding to the main limits highlighted in the literature.

STRENGTHS AND WEAKNESSES OF AN OPEN WEB SURVEY Among the forms taken by survey research, following the profound digitalisation process that has affected our society, there is the Internet-based survey, whose two maximum expressions are the e-mail survey or closed web survey (linked to limited populations, with respect to which a mailing list is available, used to define the target to be filled in; see section 3) and the open web survey, in which the link to the questionnaire appears to be published on one or more web pages accessible to all (or, if nothing else, to the ever-growing Internet audience – Couper and Miller, 2009; Gabbiadini, Mari and Volpato, 2011; Lombi, 2015; Callegaro, Lozar Manfreda and Vehovar, 2015); the latter, which shall the subject of the reflections that follow in this section, is aimed more often at large and heterogeneous populations, with whom it is not simple, sometimes even impossible, to establish very precise boundaries. The aim of this section is to use some virtuous research cases in order to highlight how it is possible to maximise the advantages of a technique, which is certainly not without its flaws, by appropriately combining a series of measures and setting up ad hoc corrections. Before moving on to the case studies, it may be useful to summarise the main advantages and limits that are most commonly associated with the open web survey, so as to better understand, subsequently, what can be remedied and how. At the top of the list of advantages is the conspicuous reduction in research costs, with the loss of a series of substantial items of expenditure (including those for sampling, for training the interviewers, for the conduction of the interviews), resulting in comparatively nominal in comparison to those typi-

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cally associated with online surveys, including for example those for sponsorship and highlighting the link to a questionnaire2. Let us come to another point of crucial interest. As is known, online questionnaires include a compilation in the absence of an interviewer; in this regard, many studies show that the characteristic of selfadministration considerably reduces the social desirability of responses (Kreuter, Presser and Tourangeau, 2009), one of the most insidious distortions for empirical studies in the sociological field. As with all self-compiled questionnaires, the absence of an interviewer makes it possible to answer the most intrusive questions without triggering the risk of giving one’s interlocutor the best image of oneself. To this undoubted benefit, from the broad repercussions on the overall fidelity of the data collected, others are added: a) automatic registration of data in the matrix (any tool for the design and online management of questionnaires is the automatic and real-time storage of the information gathered in the data matrix is ​​obtained); b) the possibility of supporting the compilation (and also enriching of content-stimuli) with aids-elements connected with the advancement of technology (tutorial, video, audio, avatar); c) the monitoring of the digital tracks in progress/observation of the process of composition of the data matrix (and the consequent possibility of partial analysis of the data in the different moments of the surveying/coverage checks in progress); d) the possibility of interacting with the respondents in order to solve problems of understanding the questions/the response modalities, to carry out improvement interventions on the survey instrument through this feedback and to grasp the most common reactions in relation to the subject under investigation (think of the importance of the wide range of comments and reactions by respondents); e) the containment of compilation errors/partial compilations (through the introduction of indications for the correct compilation supported by the technology/setting of more or less restrictive constraints on the lack of answers)3; f) the possibility of designing panels for the investigation of some analysed aspects/of the continuation of a study over time on the basis of mailing lists created through the voluntary contacts collected when completing the online questionnaire; g) the possibility of reaching a vast network of contacts in the territory - also due to the system of multi-platform shares implemented by the same respondents - in the case of surveys designed with reference to large populations and geographically located; h) the opportunity to launch pilot studies that prepare the ground for broader investigations involving a greater investment of resources; i) the opportunity to study - in some cases addressing the entire reference population - specific online practices/specific virtual communities. The “cons” of a web survey are neither few nor of little relevance, among which the non-representativeness of the sample4, connected to the self-selection of cases (Bethlehem, 2010; Di Franco, 2010), stands out in first place. The subjects who choose to complete the questionnaire do, in fact, often refer to a population with undefined contours, with respect to which a known list is not available, from which to plan the extraction of the sample. In the absence of this list, unlike in the case of random samples, the probability of each case being sampled cannot be calculated for a web survey. The cases are self-selected because the possibility of completing the questionnaire is entrusted entirely to the choice of subjects to connect to the web page where the questionnaire is published online and to proceed to provide the requested information, with the non-trivial risk of strong unbalance of the sample reached. It is evident that, where the distributions of some significant variables in the reference population are known, it is appropriate to carry out a comparison a posteriori or in progress between the latter and the data obtained on the sample (also for the purpose of developing balancing strategies or simply to set adjustments and weightings during data analysis). The lack of statistical representativeness is certainly a striking limit of open web surveys5 but, as we will be able to take up again in the conclusions, it is a disadvantage that has more negative repercussions on the job of the pollster than on that of the social researcher, intent 91

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on identifying trend uniformity in terms of relationships between variables. Among the most conspicuous disadvantages we must also remember: a) the impossibility of setting up very extensive or complex questionnaires6; b) the problem of coverage with reference to specific social categories7; c) the impossibility, given the interviewer’s absence, of motivating the interviewee, establishing an atmosphere of trust, supporting the interpretative processes of the questions, grasping the extra-verbal aspects of the interview, all elements with undoubted positive implications in terms of quality of the data; d) the limited number of cases generally achievable8.

Voting Intentions and Political Participation: The Survey on Italian Voters on the Eve of 2018 Political Elections in a Mixed-Mode Perspective As previously mentioned, we now proceed with the presentation of some case studies with the aim of highlighting the measures adopted to maximise the advantages of the web survey technique and the methodological/corrective choices developed to minimise the disadvantages. The research9 (Lombardo and Faggiano, 2019), focused on voting intentions and, more extensively, on the issue of political participation (Atkeson et al., 2011), provided for two moments of survey at a distance of six months, in which 850 and 1,371 cases were respectively achieved. In particular, the second survey (which can be considered the “official” research experience compared to the first one, which has the character of simulation) unfolded over the four weeks of electoral campaign for the Italian general elections of 2018. The questionnaire used is composed of 35 mainly structured questions; in addition to a graphical interface that facilitates the correct compilation of the questions, divided into thematic modules in succession, a compilation guide and numerous notes/aids for each of the questions contained therein are available online (Peytchev et al., 2006). The aspects investigated include trust in institutions, social resentment, problems perceived as urgent, interest in politics, political orientation, political information/media diet, voting intentions and related motivations, electoral behaviour over time, associationism, political and social commitment in the dual dimension online and offline. The main objectives of the survey are to detail the sociological profile of the various electoral targets and the one, closely connected with the development over time of a tradition of research and its set of techniques and tools, to identify explanatory factors connected with the choice of vote (of individual and contextual nature). For the purposes of the online survey, numerous social channels (Twitter, WhatsApp, Messenger, Telegram) were used, among which Facebook played a prominent role10. In order to conduct an optimal online survey, within which the advantages exceeded the disadvantages, various measures were adopted. Among the latter, testing of the entire research facility proved to be essential, managed both in terms of a meticulous pre-test (useful to establish the average time required for compilation, to refine the wording, to establish the most suitable ordering of questions/modalities to remedy redundancies/gaps) - carried out both online11 (on all the platforms involved) and offline - and, as mentioned, in terms of the full simulation of the survey a few months before the official study12. The ongoing monitoring of digital tracks was also of great importance (consisting, on the one hand, of the constant observation of the identikit under construction of the respondents within the data matrix - fundamental in order to make assessments in terms of coverage of the different electoral targets, on the other hand, of the enhancement of online interactions with respondents: comments on the post contain-

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ing the link to the questionnaire, private messages on social media: suggestions, criticisms, requests for help in completing, requests for clarification, etc.). The survey, despite involving different social channels, took place mainly on Facebook (on which, according to the latest updates, over 30 million Italians are registered, most differentiated with respect to the classic basic variables), a platform on which almost all leaders politicians/political forces boast an official page, on which different forms of online political participation by users are registered (searches for information on current political issues, monitoring of specific pages of interest, sharing of political contents, forms of online mobilisation, etc.). A portion of the survey was carried out on the Facebook page of the Department of Communication and Social Research (CoRiS – Sapienza University of Rome), a promoter of research activity, whose students were asked to share - whether on the same platform or not - the post containing the link to the questionnaire; on this occasion, 300 interviews were completed. The use of sponsorship of the post - which took place on the basis of a minimum economic investment that allowed it to be “highlighted” for the entire time span considered on the platform - guaranteed its most widespread distribution throughout the country and the achievement of maximally differentiated profiles. The research team, in addition to focusing on classic socio-demographic variables in order to randomly reach a vast and heterogeneous sample of Facebook users, identified numerous additional selection criteria (political orientation, interests, hobbies, etc.). In this case, despite having obtained a total volume of about 40,000 contacts (post views), the number of completed questionnaires was equal to 800 units. The online survey, both in the first and in the second round (with almost identical percentages) showed, in addition to the advantages described above, its flaws, including, first of all, the limited number of questionnaires filled out against the contacts made and the problem of coverage with respect to certain social categories. The online questionnaire, showing that it encompasses a double distortive effect represented by a «technical factor» and a «theme factor» (whose the most problematic aspect lies, as is known, in expressing voting intentions) reached some targets with difficulty - the elderly, subjects with a low level of education, right-wing voters and it did not, for obvious reasons, reach non-internet users at all. In the face of over-represented categories in the online sample (left-wing voters, the undecided, subjects with a high level of education) and, at the same time, of targets that are difficult or impossible to reach and, given the indispensable goal of analysing all the electoral targets, the solution to the main critical issues emerged was identified in a specific mixed-mode strategy. In particular, supported in this choice by the experience of the first survey, it was opted, while considering the online nature of the administration of the questionnaire, for the use of a concurrent offline survey with a paper questionnaire administered face-to-face to selected targets (those with, in the hypothesis, little or no representation at all), with an intent to compensate13. The strategy adopted proved to be fruitful from several points of view, intervening in the direction of correcting the most glaring defects of the web survey, without excessive investment of additional resources, obviously acting in the same time unit and with the same questionnaire: a) greater numerical consistency of the total sample achieved (1,371 cases in all, of which slightly less than 300 reached offline); b) greater balancing of the socio-demographic variables (for the purposes of managing the bi- and multivariate level of analysis)14; c) finding a reasonable number of cases with respect to each electoral target (despite the under-sizing of the right-wing voters, compared to the other electoral targets: left-wing voters, Five Star Movement voters, undecided, non-voting oriented) and the possibility of proceeding to appropriate characterisation/identification of predictive variables in relation to voting intentions; d) high quality of the data (the presence of the interviewer during the offline survey, connected largely to 93

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complex social profiles difficult to manage - elderly, subjects with low levels of education, etc. - ensured an adequate understanding of questions/answers, facilitated the establishment of a climate of trust, as well as a complete compilation of the questionnaire) 15; e) transition from a study on voters present on the Net to a study focused on all types of voters (one should think in particular of non-Internet users); f) possibility of carrying out in-depth analyses on some aspects that emerged - also using survey instruments other than the questionnaire - drawing on the contacts contained in the mailing list composed on a voluntary basis during the survey (an example is represented by the in-depth analysis of the “reasons for resentment”, which is the most widespread data in the sample); g) possibility of continuing the investigation over time, always based on the contacts made, mixing the online and offline dimensions of the survey, as well as qualitative and quantitative collection techniques. In the empirical case presented the mixed-mode strategy (Revilla, 2010) adopted is concretely represented by the effective and concomitant combination of the administration of an online questionnaire - as the prevailing technique - and a paper face to face questionnaire - as a residual compensation technique. As is known, many further paths can be taken in the mixed perspective, appropriately choosing - depending on the topic under investigation, the objectives of analysis and the resources available - which ones and how many techniques to combine, which of them should potentially have pre-eminence/residual character, according to which temporal perspective the different elements must be combined (What to start first? What to combine later? Which elements to make interact in conjunction?) 16.

Life in the Time of Coronavirus: A Panel Web Survey in a Mixed Methods Perspective It is well known that due to the rapid and dangerous spread of Coronavirus throughout the country, Italy experienced a painful lockdown beginning March 11th and lasting until May 3rd. What in fact turned into a devastating pandemic on a world scale deeply altered the living habits of millions of people, who not only endured serious health risks but also absorbed a heavy blow at a psychological, social and economic level. Starting in early April, a group of researchers from the CoRiS Department launched an open web survey ̶ utilizing the open source software server LimeSurvey ̶ with the aim of studying the effects of the lockdown on the daily lives and social relations of Italians (Lombardo and Mauceri, 2020). In greater detail, the online questionnaire, again in this case extensively tested before the extended survey, was designed to gather a large basket of information (in addition to many socio-demographic variables), all hypothetically interconnected, in the following areas: Apprehension, perception of risk and state of mind; Lifestyles and family relations; Employment situation, professional profile and smart working; Distance education; Information, faith in institutions, opinion of measures taken against the spread of the virus; Use of technology; View of the future. Altogether, over four weeks of data collection, the number of cases reached by the survey totaled 13,473 (more precisely, the questionnaire was opened by 24,144 potential respondents, of whom around 60% filled it out in full). The link to the questionnaire was widely shared online, appearing on many of the web pages and social channels commonly visited by the vast and heterogeneous audience of Internet users. The critical issues with this detection technique have been discussed above. Without repeating ourselves, it is worthwhile to look at the methodological and strategic choices that constituted the strong points of this experience and made it a success, in the hope that they can serve as a valid “toolkit” for similar experiences involving other researchers in the future. 94

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First of all, considering that data collection was conducted during a national lockdown, the present survey undoubtedly stands out as exceptional. Upon reflection, in such a situation the web survey was the only way of conducting a questionnaire survey that would involve the Italian population as a whole and could be done quickly and at low cost. The overall volume of responses obtained, for a questionnaire that was more complex and detailed than the type generally found online, is plainly extraordinary. The sudden disruption of the daily life of an entire population, the close connection between the topics of the investigation and current events, the intense social concern and people’s desire to “tell their stories”, sharing and expressing their testimony, have resulted in a massive willingness to participate in the compilation of a “collective diary of the pandemic”17. We shall now move schematically to the methods of dissemination of the questionnaire and the contacts established for its optimal promotion, and to the construction of a “high reputation” profile for the survey. • •

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Placement of the survey within a “virtual institutional context”, represented by the site and social channels of the CoRiS Department that hosted the survey, the web pages of the substructures attached to it (laboratories, libraries), the site of the University itself. Creation of the study’s own Facebook page (Life in the time of Coronavirus – https://www.facebook.com/progettocovid.coris/), a space designed to establish direct contact (when not actual interaction) with respondents and other subjects interested in the topics covered, as well as a locus for the daily publication of the “in progress” results of the research. Contact with the scholastic institutions operating throughout the country, aimed at promoting participation in the survey by young people. An email invitation to fill out the questionnaire was sent to the entire database of Italian high schools. Publication of the research initiative on numerous Facebook Groups active on the local information front (above all for areas hit especially hard by the virus), associated with specific employment profiles (especially targets less easily accessible online or typically more reluctant to be interviewed), in the world of education (universities, schools), and on the theme of the emergency. Support from extra-academic institutions of strategic value in the pandemic phase. The link to the questionnaire appeared on the Facebook page of the Civil Protection Department (obtaining hundreds of likes, dozens of comments and over 300 shares). It was also published on the portal of the Ministry of Health and on the site of scientific and geo-vulcanology Meteo-Web. Sponsorship and highlighting of the Facebook post containing the link to the questionnaire. This economic investment in the social platform ensured maximum dissemination of the link throughout the country (the accounts, for subjects aged 18 and over, were reached randomly, based on maximizing heterogeneity with respect to age and gender).

If, on the one hand, the methods illustrated for distributing the questionnaire made it possible to quickly reach a vast and, above all, motivated audience, they also had the effect of over-representing certain profiles - in particular, young people, people of middle age and women. With no intention to generalize, but in order to mitigate these disproportions and more adequately carry out the analysis of the data, it was decided to weight the data according to three key variables (gender, age and level of education), setting as the reference population Internet users over the age of 15, as defined by ISTAT (2019). Probably the most interesting feature of the survey is that it was set up right from the start as a panel web survey, aimed at capturing, in a diachronic perspective, any changes in the phenomena under analysis with the same respondents (the subjects willing to be contacted again for research purposes ̶ almost half 95

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of the total sample ̶ provided their email address during the initial survey). The approximately 6,000 email addresses registered in the matrix in the spring of 2020 are associated with socio-demographic profiles that almost identically reproduce on a smaller scale the characteristics of the original sample18. A year later (the survey is still ongoing and currently boasts about 3,000 respondents out of the potential 6,000), the research team has returned to delving into topics such as apprehension, everyday life, the relational system, etc. Research objectives include reconstructing the “balance sheet” of the respondents with respect to areas such as school, work, and leisure time. At the top of the list of new items introduced in the second round of the survey is understanding the cognitive baggage and collective practices related to the vaccination campaign, as well as to analyze, using a more detailed basket of indicators, the area of perceptions of the future. Fully adopting a mixed-methods perspective, a third round of research, which will be implemented in the next few months, will provide for the in-depth analysis of the most interesting results of the double questionnaire survey (and/or results that cannot be fully read and interpreted in the light of the standardized tools used), using the technique of the focused interview. This last phase of research will be centered on smart working and anti-Covid vaccines. The decision to focus on this case study appears fully justified at this point: 1) Conducting the survey remotely in its entirety is a winning collection technique in situations, like the one experienced worldwide, in which live person-to-person interaction is impossible. 2) The web survey can include even long and detailed questionnaires if it is able to activate participation in a survey experience and generate interest in its themes and issues in the target community. 3) The web survey lends itself to conducting surveys over time, even from a perspective that judiciously mixes quality and quantity.

CLOSED WEB SURVEY ADDRESSED TO SPECIAL POPULATIONS Under specific conditions, when you have a list of e-mail addresses of special populations, you can start a closed web survey. This constraint limits the possibilities of application to special populations inside organisations, professional corporations, institutions that adopt e-mail communication as an internal communication channel. It involves sending the link to the online questionnaire to all members of the population by e-mail, so that they can answer questions via the web. Compared to the open web survey, many of the problems of sample coverage are solved, even if, similar to what happens for the postal paper and pencil questionnaire, the problems related to the sample mortality remain, since the choice to fill in the questionnaire is totally entrusted to the respondent. However, compared to a traditional postal questionnaire, the mortality of the sample is a less serious problem because the return of the completed questionnaires is certainly facilitated. Furthermore, if for each member of the population, in addition to the e-mail address, basic characteristics are available, it is possible to estimate a posteriori how much the reached sample deviates from the population. In the summary table (Table 1) the main advantages and limitations of a closed web survey are summarised. In addition to lowering the costs of data collection and entry, it is worth pointing out that it is possible to contact the members of the population several times, which allows for subsequent reminders, the starting of panel studies, as well as for integrating the web survey with qualitative procedures, from the perspective of Mixed Methods Research (see Mauceri, 2014, 2016, 2019). The limits relating to the intersubjective congruence in the attribution of meaning to the questions and to the categories of response, linked to the self-completion of the questionnaire, as well as the need to avoid excessively

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lengthy and complex questionnaires, remain. On the other hand, the quality of the data is greater in the event of intrusive questions or if the risk of social desirability of the answers arises. Table 1. An assessment of the advantages and limitations of a closed web survey Constraints and limits

Advantages

Need to have a list of e-mail addresses for the entire sample population

High response rate (greater than traditional postal questionnaires see Kiernan et al., 2005)

Accessibility only to special populations who have an e-mail address

Increased sample coverage compared to an open web survey

Lower data quality due to self-compilation of questionnaires

Possibility of combining multiple sources of information and multiple levels of analysis and to compare characteristics of the sample and the population

Impossibility of using long questionnaires

Possibility of contacting respondents several times (reminders, panel surveys, quality checks, Mixed Methods Research)

Sample mortality

All those of open web surveys

A Survey of the Quality of University Life Among Those Enrolled in the Master’s Degree Programmes of the CoRiS Department The presentation of the survey we have chosen is a virtuous exemplification of the use of the closed web survey, which exploits almost all the advantages shown in the previous section19. The objectives of the survey are related to aspects of a cognitive, pragmatic and methodological nature: 1) assessing the quality of university life (QUL) of students enrolled in master’s degree courses administered by the CoRiS Department, in relation to the system of expectations and individual needs; 2) identifying the factors of different nature (contextual, relational, individual) that are associated characteristically with the level of satisfaction with the different aspects that make up the QUL; 3) enhancing the results of the survey in order to improve the training services offered by the CoRiS master’s degree courses; 4) experimenting a multi-level assessment approach, integrated and focused on the needs of the members, which can be extended to the university system or to segments of the training offer wider than those considered. The planning of the research design required the adoption of a methodological approach that can be defined as multi-level and integrated (Gobo and Mauceri, 2014; Mauceri, 2016, 2019), in the sense of providing for the combined use, in the phase of data collection and analysis, of differentiated procedures of investigation, in order to link different levels of analysis and to jointly make use of the advantages envisaged by the specificities of quantitative and qualitative social research techniques, commonly applied independently. It is therefore an approach that, escaping from the atomism inherent in the surveys and investigations that make use of random samples on large populations, manifests greater fruitfulness where there is the possibility of analysing the dispositions and actions relating to individuals located within meaningful contexts of interaction with differentiated characteristics (contextual properties). The approach used envisaged a combination of different research tools in order to access information of various kinds and placed on different levels of analysis: secondary secretarial and Infostud data on 1,472 subscribers, relating to properties of educational trajectory and university performance; analysis of data published on degree courses and teachers (contextual properties); compilation of an online

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questionnaire, designed ad hoc, by a sample of students enrolled in the master’s degree programme; two focus group sessions prior to the planning of the questionnaire and four in-depth focus group sessions a posteriori. The quality of university life, surveyed through a questionnaire, was declared to be a complex and multi-dimensional concept and defined, in the specific context of the investigation, as the satisfaction of the CoRiS master’s degree students with respect to the different aspects that make up university life, in relation to the system of individual needs and expectations (standards of assessment centred on the interviewees). The dimensions considered refer to the following aspects of university life: a) quality of teaching on the degree course; b) quality of the degree course training offer; c) quality of departmental support services for teaching and university life; d) quality of relationships established with lecturers and colleagues. All dimensions involved the design of articulated scales in different items to detect, on the one hand, the satisfaction inherent in a series of specific aspects and, on the other, the importance attributed to the same aspects. The central part of the survey consists of an e-mail survey, extended to the entire population of members, thanks to the possibility of using the e-mail addresses provided in the administrative databases available. Each member was invited to complete a structured questionnaire, divided into 30 questions and designed in online mode, through the LimeSurvey platform. Altogether, 530 students returned the completed questionnaire, with a coverage of approximately 37% of the population of the students enrolled. The planning of the questionnaire also included the detection of individual properties, with the role of independent variables: intensity and regularity of attendance at the lectures, time spent on work and care activities, socio-cultural extraction of the family, motivations and information channels to the act of registration, general satisfaction with initial expectations. The complexity of the QUL construct and above all the specificity of the context suggested selecting the indicators related to each dimension, as well as on the basis of the existing literature, also through the ex ante conduction of two linked focus group sessions. The questionnaire was also subjected to two pre-testing cycles. The first cycle, which used face-to-face interviews, focused on the planned questionnaire, with the intention of accurately identifying the risks of distortion for the different dimensions of data quality (Gobo and Mauceri, 2014). The second cycle of pre-testing, on the other hand, focused on the entire detection system that would be adopted during the data collection, paying specific attention to the operation of the online design of the questionnaire. It is understood that the methodological limits associated with the use of online self-compilation (ibid.) prevented the inclusion of questions with reference to further conceptual aspects (area of planning, for example), which would have required specific attention. This deficiency was at least partially compensated for by the use in succession, with respect to the closure of the online survey, of qualitative procedures with reference to some of the cases that had agreed to further lend a hand for the success of the investigation. More specifically, four focus groups were conducted (one for each degree course). The request for a nickname from each participant, at the beginning of each focus session, associated to his/her own e-mail address, also in this case enabled the researchers to carry out matching between the interventions of each participant in the focus and the data related to the primary and secondary sources illustrated above. The focus groups also played an essential role with respect to the interpretation of results which were unexpected, or which would have been interpretable only through the use of ad hoc hypotheses.

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What Possibility of Statistical Inference? The response rate (37%) is well above the average that traditional mail surveys generally get (around 20%) and is mainly attributable, in addition to the effectiveness of the motivational letter of presentation sent in support of the questionnaire, to the crucial nature of the university experience in the student symbolic and biographical universe20. The comparison between the proportions in the overall population and in the sample reached of those enrolled in the master’s degree courses gives the opportunity to consider a posteriori that the sub-group of respondents to the questionnaire is not oversized or significantly undersized in relation to any of the variables related to the socio-personal profile, to the trajectories training or pathway/performance of the students within the master’s degree courses, which can be deduced from the administrative data of the Secretariat and Infostud for all the students enrolled (Table 2). Table 2. Comparison between the profile of the population of the students enrolled in the CoRiS Department and the sample

Female students Average age Residence (Off campus)

Population

Sample

    68.3%

    73.4%

    26.7

    26.6

    57.7%

    60.2%

Exams taken (from 2 to 4)

    25.7%

    24.5%

Exams taken (5 or more)

    52.2%

    52.6%

    28.0

    28.3

University of origin: “Sapienza”

    43.8%

    42.7%

CoRiS internal 3-year degree course

    30.3%

    26.4%

Communication, evaluation and social research for organisations

    6.9%

    10.7%

Media, digital communication and journalism

    23.3%

    26.0%

Average exam score

Degree course being followed

Organisation and marketing for business communication

    53.5%

    45.4%

Development and international cooperation sciences

    16.4%

    17.9%

    26.0%

    23.3%

Position in studies Outside prescribed time Matriculated

    35.1%

    35.9%

Inside prescribed time

    33.5%

    36.4%

Part-Time

    5.4%

    4.4%

(Source: Our elaboration of the Secretariat and Infostud data)

There is only an over-representation of 5% for female students and an under-sizing of 8% of students enrolled in the master’s degree in Organisation and marketing for business communication. Even with regard to traits, such as individual performance in the exams of the master’s degree, which would have required a greater willingness to collaborate in the success of the research by certain groups of members

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(e.g. the most diligent), there are no significant deviations between the composition of the sample and that of the general population. The elements supporting the possibility of generalising the sampling results to the entire population are therefore perhaps more plausible than those, often purely formal, brought to support the chances of statistical inference, recognised a priori where the sample was randomly selected and declared as “representative” during research (see section 5). In this survey the high response rate and the possibility of articulating a complex questionnaire also in the online mode can be ascribed mainly to the fact that we were dealing with subjects particularly motivated to respond and with a high level of education. On the other hand, many other collectives can be imagined, above all organisational contexts, with similar characteristics and that would allow just as well for the adoption of a mixed and multilevel approach.

THE COMBINATION OF OPEN AND CLOSED WEB SURVEYS IN DELIMITED CONTEXTS The specificities of the last case study presented move in two directions: a) the use of the open web survey as a test of a more extensive investigation, carried out in sequence, through a closed web survey in delimited contexts (school classes); b) the possibility of foreseeing the figure of the interviewer in specific ways of conducting the web survey. With reference to the first point, the conduct of a preliminary pilot study in which the open web survey is adopted allows us to pre-emptively control the following aspects: a) the time required to complete the questionnaire (for the purposes of estimating this duration in the selected contexts); b) the presence of properties not associated with others: in view of construct validity, it is possible to identify the variables that, presenting very weak associations, highlight non-corroborated hypotheses. In this perspective, it is possible to eliminate questions and items corresponding to these variables from the final questionnaire; c) the internal consistency and unidimensionality of the attitude scales: the internal controls of the data matrix can indicate items to be reformulated or eliminated from the final questionnaire; d) the presence of questions which are irrelevant or too complex: by maintaining the non-mandatory answer to questions in the online compilation during the pilot study, it is possible to identify irrelevant questions in the eyes of the interviewees or that are too complex, based on the number of missing values; e) the questions not discriminating enough: the unbalances in the frequency distributions can signal questions or items to be eliminated because they give rise to constants, rather than variables; f) the risks of distortion, which may emerge through the control of response sets and control requests; g) the opportunity to introduce new categories of response, following the analysis of the indications recorded in the residual category “other”. The illustrated survey was implemented within the university project Technological Addiction. New forms of digital dependency, socialisation and social networks, directed by Mauceri (Mauceri and Di Censi, 2020). 100

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The project investigates the use of and dependence on digital devices among adolescents between the ages of 14 and 18, defined as “digital natives”, as they have a considerable amount of digital tools (Internet, smartphones, social networks, videogames, streaming platforms), which normally increase the possibilities of amusement, information, learning and social relations. Digital addiction is now widely recognised as a pathological form of use of these channels, due to the significant repercussions compared to the normal course of daily activities and the psychophysical balance of the subjects who use them. Sociological and psychological research has highlighted the main effects of multimedia dependencies, including: social isolation; negative effects on school performance; sedentary lifestyle; increased aggression in daily relationships; stress; depression; sleep disorders; consumption of alcohol, tobacco and psychotropic substances (Young, 1998; Cacioppo and Severino, 2013; Lancini, 2019). Dependence on new technologies entails a redesign of space-time coordinates, including the construction of a parallel virtual world, without the sense directions that make direct social interaction activities with friends, teachers and parents stimulating. The objectives of the survey are substantially two: a) to identify the factors (individual, relational and contextual) that are associated characteristically with the onset of symptoms of dependence on digital technologies among adolescents enrolled in upper secondary schools, with specific attention to the conditions that make the use of multimedia devices compulsive and dysfunctional; b) enhance research results in order to identify guidelines for a conscious use of multimedia channels to prevent the onset of new forms of addiction. The survey tool was built after a laborious testing phase aimed at keeping under control a series of distorting factors linked to the formulation of the questions (complexity or obscurity of the question, underdetermination, overdetermination, obtrusiveness). In particular, thirty pre-testing interviews were conducted in the face-to-face and thirty through the online administration mode. This form of canonical pre-testing has been accompanied by the pre-testing mode of expert review, making the questionnaire subject to discussion within an audience of experts in digital technologies. Only after this phase the final version of the questionnaire was realised - consisting of 42 questions - and the online implementation in LimeSurvey carried out. In particular, an open web survey (pilot study) was followed by a closed web survey, extended to a sample that had greater guarantees of sample coverage. The addiction, as a complex and multidimensional construct, was operativised, providing for the ad hoc design, for each of the digital supports already mentioned, of a relative frequency scale articulated on several items, which reproduced situations of compulsive use. The other problem areas of the questionnaire capture the factors in hypotheses associated with addiction: multimedia use (reasons for use, frequency, duration, sharing of use); lifestyles, socialisation and social networks (ways of using extrascholastic time, academic performance; educational-parenting model, virtual relationships); emotional distress (relational discomfort, exposure to stressors; boredom in the classroom; concealing one’s personality, level of self-esteem); socio-cultural profile. Administration for the pilot study took place on the Facebook platform following online sponsorship; this choice - at very low costs - made it possible to involve the members of the social network in the research who fulfilled the double criterion of being of school age/living throughout Italy. The sharing of the link to the questionnaire by the students and the publication of a post on the research (also containing the link for the compilation) on the reference pages of the CoRiS Department, contributed to increasing participation in the web survey. In the 20 days of online sponsorship, the web survey was proposed to 35,705 people; of them 767 visualised the link. Instead, 374 young people (1% of the Facebook sample) participated in the survey. 101

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The pilot study allowed us to test the questionnaire and have a first analysis of the data collected, which provided useful elements of reflection for the continuation of the investigation. The controls carried out during this phase make it possible to assert that the entire procedural system of the investigation functioned adequately. In fact, only small revisions affected the questionnaire in its final form, although the pilot study made it possible to eliminate and reformulate some questions/response categories. The questionnaire was administered, from October 2018 to February 2019, to the students of a sample (a multistage sampling procedure was used with cluster extraction at the last stage) of 16 uppersecondary schools in Rome. During the survey phase, 3,302 questionnaires were completed via online self-administration, within the school computer labs. The fact of having selected collectives with precisely delimited boundaries (school classes) at the time of sampling made it possible to foresee the presence in each of the sampled classes of an interviewer who supported the interpretative processes of the questions. The fact of having selected the delimited collectives also made it possible to adopt a multilevel approach integrated with the survey. In particular, for each of the educational institutions selected, a context analysis form was filled in by the teaching representatives, with the aim of detecting properties of their respective collectives associated with the use of digital technologies during school hours. This strategy allowed for data processing to relate individual properties with contextual properties, in view of a multilevel analysis. The approach can be defined as integrated because it allowed us to combine in the same research project standardised and non-standardised techniques of information collection, in the perspective of Mixed Methods Research. Specifically, in four of the schools sampled, an additional research-intervention phase was carried out, through the use of an online form aimed at surveying reactions in open form to some audio-visual stimuli/intervention guidelines implemented starting with the results of quantitative research. Returning to these institutes was also an opportunity to start focus groups within school classes centered on the phenomenon of digital addiction.

CONCLUSION: THE WEB SURVEY IN THE PERSPECTIVE OF INTEGRATION To conclude this contribution, it is important to point out that the methodology of the case studies illustrated has proposed specific integration strategies to the web survey pattern that deserve particular attention: a) the use of mixed-modes of data collection; b) the use of a follow-up panel web survey; c) the combined use of standardised and non-standardised information collection techniques (Mixed Methods Research); d) the inclusion of a prior pilot study; e) the link between different levels of analysis through the joint use of multiple sources of information. Starting from the first point, as Couper has argued, “modes are [...] mixed for a variety of different reasons, from reducing costs and increasing response rates, to addressing differential coverage, targeting specific subgroups, improving measurement, and so on” (2011: 897). Adding other forms of data collection, such as the face-to-face interview, to the self-filling of the online questionnaire, is, as in the first research analysed, a valid opportunity to face the problems of self-selection of the sample 102

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that the web survey presents, and the consequent distortions due to the under-sizing of specific types of respondents. Consequently, it is good practice for the integration to take place after the investigation has been completed, faced with a complete picture of the profile of the sample reached through the web survey and of its deviation from the features of the more general population. However, as in the survey on voting intentions, if information is previously available on the deviation between the sample profile and the survey population, and if the expected survey time is very limited, it is possible to imagine concurrent paths of integration between different modes. A problem that obviously arises is the comparability of the information obtained through different methods. However, the implicit assumption is that the quality differences of the data produced by different collection modes used in combination are not so consistent as to negate the benefits of mixed-modes (Couper, 2011; Kim et al., 2019). Rather, as has been established in the survey on voting intentions, the mixed-modes approach can be adopted to guarantee the quality of data (Millar and Dillman, 2011), foreseeing the presence of the interviewer for subgroups of the population specific that could encounter greater problems in self-compilation (e.g. less educated and older people). The use of mixed-modes of data collection that include the use of the web survey is rapidly spreading even in European surveys that had traditionally relied on face-to-face interviews with probability samples. A case in point is the European Values Study (EVS), conducted in 1990, 1999, 2008, and 2017. Originally, this survey exclusively made use of face-to-face interviews lasting an average of one hour. Decreasing response rates and progressively increasing costs led the research team to radically change the way data was collected in the most recent 2017 edition (Luijkx et al., 2020). Six countries that participated in the last wave of the EVS tested the application of mixed-modes of data collection (Denmark, Finland, Germany, Iceland, the Netherlands, and Switzerland). Specifically, modes of self-completion of the questionnaire were used in parallel with the face-to-face interviews. For the purpose of conducting the web survey, completing the questionnaire was preceded by a letter of invitation that provided both the link for online completion of the questionnaire and a paper version of the questionnaire that allowed those who did not use the Internet to complete it. Respondents were offered a monetary incentive or a lottery conditional upon the completion of the survey. Specific attention was also given to data quality requirements. In four of the six countries the full questionnaire was considered too long to be filled out online or for self-completion. The questionnaire was divided into four thematic blocks and one ‘core’ block with background variables. Each respondent had to answer two of the thematic blocks and the core. Splitting the face-to-face questionnaire resulted in six versions of the self-completion questionnaire, representing all possible combinations of blocks. Following this, the respondents were re-contacted for a follow-up that allowed the self-compilation of the remaining two blocks of questions. While the splitting of the questionnaire undoubtedly resulted in higher quality data, it has to be considered that this strategy introduces an effect of panel attrition due to refusals to participate in the second phase of data collection. This meant that in the two countries where the selfcompleted questionnaire was submitted in the full-length version, the rate of complete questionnaires was only slightly lower than in the countries where the splitting of the questionnaire was adopted. In all six nations, the adoption of mixed-modes achieved greater sample coverage and significantly higher response rates than nations that maintained exclusively face-to-face interviews. Beyond the strategies illustrated, with reference to the problem of the lack of statistical representativeness, above all typical of open web surveys, some limits of the probabilistic sample are noted in the margin, which make the renunciation of this constraint less onerous. In this regard, it should be noted that the (supposed) sample representativeness, derived from randomised selection, is not at all exempt from problems and that the substitution of cases that refuse to be interviewed through the extraction of reserve 103

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samples suggests a situation that is not very far from that attributable to self-selection. Consequently, as pointed out among other by Marradi (1989), the representativeness should be appropriately estimated once the survey is completed because the planned sample is often very different from the one actually reached by the survey. If statistical representativeness can rightly be considered a “myth” for this and other reasons, just recently the writer has pointed out how many points of view the atomism inherent in the random sample, which selects individuals not generally in interaction between them, has weakened, in the name of a subordination with respect to statistical sciences, the fruitfulness of a sociological approach to the study of dispositions and social actions that bind individuals to the contexts of which they are part and to the networks of relationships in which they are inserted. Precisely this type of sociological drift, which assimilates the social researcher to a pollster, has blurred the potential of the multi-level approach integrated into the survey alongside the questionnaire, which was also present in nuce in the Columbia School works of the 1940s (Gobo and Mauceri, 2014). Statistical representativeness is, in other words, an indispensable requirement for a pollster who intends to foresee, for example, voting intentions, but becomes an unnecessary constraint for the social researcher, generally more interested in the relations between variables than in simple classification activity. The example of investigating the social effects of the pandemic introduced another important integration opportunity that can be considered in the web-survey, with a view to implementing longitudinal studies capable of recording the evolution over time of phenomena of varying duration. In the example shown, a simple expedient like asking at the end of the questionnaire whether the respondent was available to be re-contacted by email for further information allowed a follow-up phase to be conducted on a panel of subjects that fortuitously had nearly the same characteristics as the initial overall sample. Where a closed web survey is being conducted, the panel study is even easier to apply because the email addresses of individuals in specific populations are available at the outset, allowing the possibility of re-contacting survey participants several times. The use of the panel web survey is rapidly expanding in all areas of social and marketing research, and several methodological studies have documented the limitations and advantages of this particular type of longitudinal study, comparing it with more conventional studies and recording the differences that exist between panel studies conducted by drawing probabilistic and non-probabilistic samples (for a review see Callegaro et al., 2014). With reference to the next point, the studies presented have all highlighted the potential inherent in the integrated use of standardised and non-standardised information-gathering techniques, with a view to Mixed Methods Research. As analytically illustrated in a previous contribution (Mauceri, 2016), qualitative research, appropriately combined with a survey, can perform the following functions: 1) compensate for the blind points of the questionnaire: qualitative techniques can play a constitutive role in the empirical basis, when they are used concurrently or in sequence to the survey to access aspects of empirical reality not normally detectable by questionnaire. 2) make the anomalous data strategic: the analysis of the cases and deviant results, which diverge from the researcher’s expectations, following the teachings of Lazarsfeld and Merton, can refine the interpretative processes in the analysis of the empirical basis, giving the standard research that openness to the unexpected result, which is normally lacking. 3) control and increase the quality of the data: the combined use of qualitative techniques in the different moments of the research (ex-ante: pilot study; in progress: pre-testing; ex-post: analysis of deviant cases) can play a role instrumental to the objective of obtaining higher quality data.

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Another useful integration, indicated in two of the investigations illustrated, is to include in the research design a pilot study prior to conduction of the actual survey, conducted in turn by web survey. In particular, in the research on voting intentions, the pilot study provided an opportunity to simulate the integral conduct of the investigation and thus be able to make the necessary revisions to the overall procedural framework with respect to aspects that signalled deviations from the ideal investigative path, as in the aforementioned perspective of making corrections to the sample reached. In the survey on digital addictions, the open web survey, conducted beforehand, made it possible to check, through specific procedures of data analysis, the holding of the hypotheses and therefore of the conceptualisation plan of the problem set up, as well as to review the survey questionnaire. A final integration strategy, illustrated in two of the experiences presented (quality of university life and digital addictions), is that of forecasting, in a parallel way with respect to the web survey, a series of global contextual properties (Lazarsfeld and Menzel, 1961), through the compilation of an analysis sheet, designed ad hoc, in each of the delimited contexts of action taken as reference by the survey. The application of the multilevel survey is therefore bound to the possibility of extending the survey to a series of collectives with heterogeneous characteristics. All the proposed integration plans aim to make the design of the web survey more comprehensible and complex. Its costs, quite insignificant, also make it possible to invest economically in these specific integrated strategies, also contemplating different ones in the same research design. The triumph of economic rationality, which is expressed in the compression of research funding in recent years, is therefore not necessarily equivalent to a flattening of social research on polling culture. As shown through the surveys presented, complex themes can also be approached using a data collection mode at low cost such as the web survey. Imagining that multiple variants of the web survey can be created and managed, also thanks to the unstoppable technological advancement, the social research of the future will have to continue to evaluate the limits and the advantages of each concrete choice, trying to balance as much as possible the needs of economic and scientific rationality.

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Marta-Pedroso, C., Freitas, H., & Domingos, T. (2007). Testing for the Survey Mode Effect on Contingent Valuation Data Quality: A Case Study of Web Based versus In-Person Interviews. Ecological Economics, 62(3-4), 388–398. doi:10.1016/j.ecolecon.2007.02.005 Mauceri, S. (2014). Mixed Strategies for Improving Data Quality: The Contribution of Qualitative Procedures to Survey Research. Quality & Quantity, 48(5), 2773–2790. doi:10.100711135-013-9923-4 Mauceri, S. (2016). Integrating Quality into Quantity. Survey Research in the Era of Mixed Methods. Quality & Quantity, 50(3), 1213–1231. doi:10.100711135-015-0199-8 Mauceri, S. (2019). Qualità nella quantità. La survey research nell’era dei Mixed Methods. FrancoAngeli. Mauceri, S., & Di Censi, L. (Eds.). (2020). Adolescenti iperconnessi. Un’indagine sui rischi di dipendenza da tecnologie e media digitali. Armando Editore. Mauceri, S., & Taddei, A. (2015). Valutare la qualità della vita universitaria. Indagine sugli iscritti ai Corsi di laurea magistrale del CoRiS. http://www.unimonitor.it/2015/08/la-qualita-della-vita-universitaria-un% Millar, M. M., & Dillman, D. A. (2011). Improving Response to Web and Mixed-Mode Surveys. Public Opinion Quarterly, 75(2), 249–269. doi:10.1093/poq/nfr003 Novelli, E., Lombardo, C., & Ruggiero, C. (Eds.). (2019). La società nelle urne. Strategie communicative, attori e risultati delle elezioni politiche 2018. FrancoAngeli. Osgood, C.E., Suci, G.J., & Tannenbaum, P.H. (1969). The measurement of meaning. Chicago: Aldine Publishing Company. Patrick, M. E., Couper, M. P., Parks, M. J., Laetz, V., & Schulenberg, J. E. (2021). Comparison of a web-push survey research protocol with a mailed paper and pencil protocol in the Monitoring the Future panel survey. Addiction (Abingdon, England), 116(1), 191–199. doi:10.1111/add.15158 PMID:32533797 Peytchev, A., Couper, M. P., McCabe, S. E., & Crawford, S. D. (2006). Web Survey Design. Paging Versus Scrolling. Public Opinion Quarterly, 70(4), 596–607. doi:10.1093/poq/nfl028 Reis, H. T., & Gosling, S. D. (2010). Social psychological methods outside the laboratory. In S. T. Fiske, D. T. Gilbert, & G. Lindzey (Eds.), Handbook of Social Psychology (5th ed., pp. 82–114). McGraw-Hill. doi:10.1002/9780470561119.socpsy001003 Revilla, M. (2010). Quality in Unimode and Mixed-Mode designs: A Multitrait-Multimethod approach. Survey Research Methods, 4(3), 11–164. Schmidt, W. (1997). World-Wide Web survey research: Benefits, potential problems and solutions. Behavior Research Methods, Instruments, & Computers, 29(2), 274–279. doi:10.3758/BF03204826 Shih, T. H., & Fan, X. (2008). Comparing Response Rates from Web and Mail Surveys: A Meta-Analysis. Field Methods, 20(3), 249–271. doi:10.1177/1525822X08317085 Sills, S., & Song, C. (2002). Innovations in Survey Research: An Application of Web-Based Surveys. Social Science Computer Review, 20(1), 22–30. doi:10.1177/089443930202000103

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Smith, C. B. (1997). Casting the net: Surveying an Internet population. Journal of Computer-Mediated Communication, 3(1), 0. Advance online publication. doi:10.1111/j.1083-6101.1997.tb00064.x Smith, E. R. (2000). Research design. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social psychology (pp. 17–39). Cambridge University Press. Swoboda, W. J., Mühlberger, N., Weitkunat, R., & Schneeweiß, S. (1997). Internet surveys by direct mailing: An innovative way of collecting data. Social Science Computer Review, 15(3), 242–255. doi:10.1177/089443939701500302 Taylor, H. (2000). Does Internet research work? Comparing electronic survey results with telephone survey. International Journal of Market Research, 42(1), 51–63. Valliant, R. (2019). Comparing alternatives for estimation from nonprobability samples. Journal of Survey Statistics and Methodology, 8(1), 1–33. doi:10.1093/jssammz003 Van Selm, M., & Jankowski, N. W. (2006). Conducting online surveys. Quality & Quantity, 40(3), 435–456. doi:10.100711135-005-8081-8 Vezzoni, C., Ladini, R., Molteni, F., Dotti Sani, G. M., Biolcati, F., Chiesi, A. M., Guglielmi, S., Maraffi, M., Pedrazzani, A., & Segatti, P. (2020). Investigating the social, economic and political consequences of the COVID-19 pandemic: A rolling cross-section approach. Survey Research Methods, 14(2), 187–194. Vicente, P., & Reis, E. (2010). Using questionnaire design to fight nonresponse bias in web surveys. Social Science Computer Review, 28(2), 251–267. doi:10.1177/0894439309340751 Watt, J. H. (1999). Internet systems for evaluation research. In Information Technologies in Evaluation: Social, Moral, Epistemological, and Practical Implications (pp. 23–44). San Francisco: Jossey-Bass. doi:10.1002/ev.1151 We Are Social & Hootsuite. (2018). Global Digital 2018, https://wearesocial.com/it/blog/2018/01/ global-digital-report-2018 Wieters, K. M. (2016). Advantages of online methods in planning research: Capturing walking habits in different built environments. SAGE Open, 6(3). Advance online publication. doi:10.1177/2158244016658082 Young, K. S. (1998). Internet Addiction: The Emergence of a New Clinical Disorder. Cyberpsychology & Behavior, 1(3), 237–244. doi:10.1089/cpb.1998.1.237

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1 More recent analyses show how the quality of data built through the web does not differ significantly from the standards related to the traditional pen and paper technique (Reis and Gosling 2010). Without certainly wanting to sponsor, let alone lean toward a style of research “at low cost”, the insistence in several points of the work on the containment of the expenses (of detection, sampling,

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storage, etc.) exclusively wants to enhance the expansion research opportunities for a vast audience of social analysts, primarily the new generations of scholars. The correct answer to the questions, especially if complex, is easy despite the interviewer’s absence; many compilation errors typical of the completion of paper questionnaires are solved pre-emptively, in the case of the use of online questionnaires, through the system of symbols/sounds/warnings set and tested in relation to mandatory responses, filters, scales, batches, etc. The issue of the statistical representativeness has not been dealt with in depth here, since this analysis has been duly addressed by other authors (see Elliott and Valliant, 2017; Valliant, 2019). However, as will be explained analytically below, a web survey is often placed in complex and “multi-stage” research designs within which its combination with other survey experiences is established, which can also predict the random extraction of cases and statistical inference. Except in the cases of specific reference populations, the researcher is forced to calibrate his instrument on a rather modest linguistic register, which takes into account the cognitive abilities of the wider system of potential respondents. The imbalance of the sample on some variables is strongly connected with the greater propensity to respond to online questionnaires of some targets with respect to others, although the results of the survey also depend on the topic under discussion. There are also subjects that cannot be reached through the Internet, because they are not internet users, or as occasional users of the web, also due to a low level of computer literacy. This aspect, however, does not occur systematically. It depends on the theme of the investigation and on the distribution of the “seeds” useful for triggering a profitable snow-ball mechanism. Moreover, sometimes the need to log in by the interviewees can further discourage them from completing the questionnaire. Born from the collaboration, at the Communication and Social Research Department (CoRiS – Sapienza University of Rome), between the Electoral Sociology Observatory, PhD in Communication, Social Research and Marketing, CoRiSLab Laboratory. For an analysis of the 2018 digital campaign in Italy, in which the link between Facebook and political participation is deepened, see Novelli, Lombardo and Ruggiero, 2019. During the online testing, particular attention was paid to the critical issues that emerged, also depending on whether the compilation was done via PC, tablet or smartphone. By way of example, all attitude scales originally designed with scores from 0 to 10 were brought to a 0-5 range; this intervention was necessary because the questions containing scales, only partially displayed on some mobile devices, unless of course one scrolls along the whole band of values in order to choose the score closest to one’s own orientation, risked being mistakenly filled in by a large proportion of subjects reached the detection via smartphone. A sort of general test, deliberately carried out over a longer period of time than the four weeks preceding the national electoral appointment set for the official survey, within which the identification in progress of problematic aspects and ad hoc solutions allowed the researchers to reach the second survey with a clearer and more articulated idea of the operational moves to be started and linked to increase their effectiveness. The offline survey took place within a basket of highly differentiated territorial contexts. The interviewers firstly compiled a list of intermediaries for each area; subsequently, thanks to a climate of trust built ex ante by virtue of the precious collaboration of these linking figures, the interviewees

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were contacted, having been selected on the basis of a series of variables suggested by the literature and connected to the profiles which resulted less present in the sample made on the Web. The sample reached is not statistically representative and does not reproduce on a small scale the proportions of Italian electoral targets. Nevertheless, it contemplates within itself all the significant social profiles with respect to the subject under investigation/with respect to the factors of influence considered and contains specimens of each in sufficient numbers for the purposes of the robustness of the bi- and multivariate analyses developed (for example, the level of education variable, in the highly unbalanced online version in favour of higher qualifications, was reported towards a more appropriate distribution). On the other hand, some issues of social and scientific relevance need to be addressed from a different perspective than the one typically adopted for opinion polls; therefore, the selected research strategies often involve the renunciation of statistical representativeness in favour of the pursuit of in-depth objectives and complexity analysis. See Heervwegh et al. (2003); Marta-Pedroso, Freitas, Domingos (2007); Heervwegh and Loosveldt (2008). Deserving of a mention are Extensive surveys of a transnational nature (Eurobarometer, European Social Survey, etc.), which are cyclically replicated and their data can easily be accessed in order to start interesting (and statistically robust) secondary comparative analyses. In this sense, a web survey can constitute an experience of complementary investigation aimed at deepening (contemplating adequate questionnaire questions) specific aspects and/or specific social groups. For a first reflection on the effects of the Covid 19 pandemic on social research practices see Bonini, 2020. It is worth noting the interesting prospect of adopting rolling cross-sections designs (even using probabilistic samples extracted in series during the time span allocated to the survey and grafting on a CAWI survey), especially if, as during a pandemic phase or, indeed, during an election campaign, the intention is to capture “collective dynamics” and social changes (Vezzoni, Ladini et al., 2020; Johnston, Brady, 2020). To access the online research report, see Mauceri and Taddei 2015. The number of the sample estimated, through the formula that guarantees the sample representation with an error of 0.05, is equal to 324 units, therefore lower than the size of the sample reached (530 students).

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Doing Web Surveys During Uncertain Times:

Reflections From a Research Experience on the COVID-19 Pandemic in Italy Gabriella Punziano https://orcid.org/0000-0001-8783-2712 University of Naples Federico II, Italy Felice Addeo University of Salerno, Italy Lucia Velotti https://orcid.org/0000-0002-1458-1745 City University of New York (CUNY), USA

ABSTRACT The chapter will focus on using a web survey administered using social networks as a gathering point to collect data on people’s risk perception and their undertaking of protective behaviors during the Italian COVID-19 crisis. This was an unprecedented moment in the digital age when there was no possibility of physical contact due to the limitations imposed on coexistence by the health emergency to stem the spread of the virus. This is when digital connections are the only link among people, and the only tool that can be used for doing social research is trying to satisfy the desire for knowledge without limiting the potential for knowledge production even in times of profound uncertainty and several limitations. Analyzing the participants’ feedback on web surveys during times of deep uncertainty allows the authors to show what is clearly happening to social research currently. The discussions will be supported by an auto-ethnography conducted on comments left by the respondents to the survey.

DOI: 10.4018/978-1-7998-8473-6.ch008

Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 Doing Web Surveys During Uncertain Times

INTRODUCTION: PANDEMIC, CRISIS AND DESIRE FOR KNOWLEDGE1 The COVID-19 Pandemic has changed the idea of “normality” on a global level, redefining systems, patterns, values, and priorities; all this has given rise to a desire for knowledge and understanding both from ordinary citizens and decision-makers, both eager to understand the now overturned and revolutionized dynamics of the world and everyday life. The etymology of the word Pandemic is «from the Greek “pandemos” pertaining to all people, from pan- all plus dēmos people» (Online Etymology Dictionary). Thus, a Pandemic involves people worldwide and differs from the word epidemic that indicates a more restricted area, such as a community. In essence, a Pandemic is a global epidemic. The term Pandemic does not indicate the severity of the disease but its diffusion, so the word Pandemic refers to its geography. In addition to contagion geography, another element that needs to be considered when defining a Pandemic is the disease’s novelty. The World Health Organization (WHO) defines a Pandemic as “the worldwide spread of a new disease” (WHO, 2010). The novelty of the disease is crucial because it deepens the uncertainty faced by the affected communities. A Pandemic in disaster science and crisis management can be described as a transboundary crisis (Ansell, Boin, and Keller, 2010; Goldin and Mariathasan, 2014; Quarantelli, Lagadec, and Boin, 2006; Boin, 2019) or a catastrophe (Quarantelli, 2000, 2006). In a catastrophe, most likely, neighboring communities cannot help and compete for scarce resources (Quarantelli, 2000; 2006). Unlike emergencies and disasters, catastrophes make impacted communities more vulnerable since they cannot seek help from neighboring communities. There is a loss or a lack of facilities and response personnel. Also, while disasters are considered “local” because they impact circumscribed communities, catastrophes call for the national government (Quarantelli, 2000; Quarantelli et al., 2006). COVID-19 definition of transboundary crisis provides a better idea of the event’s level of management and governance. Transboundary crises, as the words say, do not have boundaries. The boundaries to which a transboundary crisis refers to are legal, political, geographical (Boin, 2019). Pandemics are well-established examples of transboundary crises (Baekkeskov, 2015). COVID-19 includes all the elements that make it difficult to manage transboundary crises, which are (Boin, 2019): • • • • •

the existence of multiple domains and multiple manifestations: transboundary crises may involve several countries; the incubation and rapid escalation: transboundary crises level of development varies, escalating or being reabsorbed and then exploding again; the difficulty in pinpointing where a crisis started and how it evolved exactly; the involvement of multiple actors with conflicting responsibilities: transboundary crises can generate crises of governance and leadership; the lack of ready-made solutions: transboundary crises require a non-routine response.

Although it is true that the Pandemic is not a new threat to humanity, it should be emphasized that the COVID-19 emergency has some unique connotations. COVID-19 is the second declared Pandemic of the twenty-first century after the H1N1 influenza (swine flu). On March 11th 2020, The WHO declared the novel coronavirus (COVID-19) outbreak a Pandemic, with diffusion to 114 countries, 118,000 cases, and deaths (WHO, March 11th, 2020). The WHO delayed declaring COVID-19 as a Pandemic, even though it had warned the international community on more than one occasion. The declaration of COVID-19 113

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as a Pandemic was not a decision taken lightly by the WHO that was fully aware of the consequences of such a statement on social lives and worldwide economies. WHO’s behavior in defining COVID-19 as a Pandemic shows how a Pandemic is not an easily identifiable event differently than other hazardous events such as earthquakes and hurricanes to name a few that are of immediate comprehension. COVID-19 is a severe acute respiratory syndrome coronavirus (SARS-COV 2) disease identified in December 2019 in Wuhan, China. The disease is airborne, it can be transmitted from an infected person through sneezing, coughing, laughing, and infected surfaces. Symptoms of COVID-19 range from sore throat, fatigue, nausea, vomit, and diarrhea. However, some people can be asymptomatic. The existence of asymptomatic people plays a role in understanding the number of infected people. It can make the infection containment almost impossible if not followed up with intense testing of people to find and isolate infected people. The fact that COVID-19 is an airborne disease, combined with the fact that people can be asymptomatic, impacts the identification of people affected. Self-distancing and quarantine quickly became the main effective measures of infection containment. Also, COVID-19, compared to other diseases, has the longest incubation period that goes from two to fourteen days and, in some cases, up to twenty-seven days (Elizabeth Yuko, September 12th, 2020). Additionally, compared to other past Pandemics, the world is now more populated (Liz Mineo, May, 2020). Another difference is that actual populations are more mobile. Furthermore, today’s society is more complex due, for instance, to the increased interconnectedness of the supply chain. Last, the spreading of COVID-19 is also due to variants of the virus that spread more quickly and, in some cases, seem to be slightly more deadly. The prolonged health emergency that has strained the systems and authorities responsible for governing this crisis has resulted in protective measures that include physical and spatial isolation as a primary preventive factor, in this scenario defined as social. These are the only measures able to withstand the challenge of an invisible enemy, sometimes without manifest symptoms, that runs fast and changes, at least until the arrival of the first vaccines, whose effectiveness is still variable and still must be tested on the variants that develop as the virus continues to run. As can be seen, talking about Pandemic also implies talking about crisis and its management in many ways. The term crisis is loosely used in formal and informal conversations to indicate a disruption of people and organizations’ routines. Crises are situations or events «perceived by a community as capable of disrupting its core values or generating a threat to essential facilities (Boin and t’Hart, 2007) under conditions of deep uncertainty» (Velotti, 2016). Based on this definition, it is possible to distinguish three characteristics of crisis management: 1. presence of a threat, 2. uncertainty, and 3. urgency (Boin, t’Hart, Stern and Sundelius, 2016; Boin and t’Hart, 2007). The presence of a threat is sometimes difficult to detect since the threat is not always measurable in physical damage or diffusion. For instance, a threat can refer to a society/community value system. COVID-19 threatened several values such as societal safety, citizens’ freedom of movement, collectivism (there was fear of the other), and understanding the surrounding physical world. The second element that characterizes a crisis is the urgency of doing something. In essence, the urgency is «the perception that time is at a premium (...) the threat is here, it’s real, and it must be dealt with as soon as possible» (Boin, t’Hart and Stern, 2017, p. 6). The third element of a crisis is uncertainty. Uncertainty refers to the nature and the consequences of a threat. For instance, there can be uncertainty about what is happening and how the situation will evolve. In a Pandemic, it is fundamental to understand the threat and how the crisis will unfold adequately. In Italy, the management of COVID-19 shows a particular characterization for describing how the events unfolded and the deep uncertainty decision-makers and laypeople encountered in handling the situation. Indeed, the Italian government, being one of the first Western countries responding to COVID-19, had 114

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to deal with uncertainty about protective actions to recommend containing and mitigate the effects of the virus. In the initial stage of the crisis at the end of January 2020, there was deep uncertainty on the modalities through which the virus spreads, how long it stays on surfaces and how people get infected. This uncertainty quickly translated into other kinds of crisis: from the general health crisis to a social and economic crisis since to arrive to a crisis of governance and leadership generated by the involvement of the central and the regional level of government in organizing the response and recovery to the Pandemic. Also, the Italian experience with the COVID-19 crisis followed a circular pattern of making sense of the crisis, deciding what to do, trying to resolve it, and starting the cycle again with new Pandemic waves. Crises are events that are not clearly defined in time and space: «A crisis may smolder, flare-up, wind down, flare up again, depending as much on the pattern of physical events» (Boin and t’Hart, 2003). The management of COVID 19 in Italy results from governance and leadership in use, the unfolding of events, and the socio-economic context. The Italian structure of governance exposes leadership, coordination, and collaboration issues in a multilevel governance contest resulting from the interaction of municipal, regional, and central levels. What becomes relevant to the argument is that, as a mean to contrast the spread of the virus, several measures for social distancing were implemented starting from the first national lockdown in March the 8th 2020. Social distancing and containment measures concerned aspects of social life, such as celebrating weddings and funerals and carrying out sports events. During the lockdown phase, schools and universities closed, and distance learning was implemented. All commercial activities, with few exceptions, had to close. The only shops that could stay open were the grocery stores and later also shops for children’s clothes. Transportation and mobility were reduced and, in some cases, were banned entirely. For instance, there was a block of air traveling and interregional movements. People’s mobility was restricted to the proximity of one’s house if needed to take a pet out. All the containment measures were detailed in the “I stay at home” (“Io resto a casa”) decree. The lockdown determined a net rupture with daily established routines. In this kind of situation, people needed to reorganize their lives and make sense of the new reality. Even though everybody was equally exposed to the same containment measures, at least during phase 1, not everybody reacted in the same way. People had to rebuild their way of making sense of a new reality. A reality in which the world as it was known suddenly changed. The first perceptual change is related to people’s sense of safety. COVID-19 is an invisible threat challenging to escape if not by further changing our realities. As explained, the government’s duty to ensure people’s safety was accomplished by restricting people’s freedom and mobility rights. These mitigation measures, implemented to slow down the diffusion of the virus, are related to changes in the sensescape, the economic, and the social sphere. The lockdown confined people to their houses and determined a difference in noise levels due to decreased aerial, vehicular, and pedestrian traffic, which led to an increase of sounds like the sirens of ambulances and church bells. Touch was also altered since with confinement and social distancing, touching gestures typical of Italian culture such as hugging, touching each other, and shaking hands stopped. A new way to rebuild a sense of community and a different way to spend their leisure time is constituted by the multiple activities people have implemented in both the real and the virtual space. In a lockdown situation, particularly during phase 1, Italians performed on their balconies using them to extend their social space (Clinch, March 14th, 2020) to socialize and build a renovated sense of community. During the lockdown, children drew rainbows with the written “everything will be fine” (“andra tutto bene”) and then hung them on their balconies. Social life also extended to virtual life with an increase in participation in online events such as virtual fitness classes. Even celebrating the weekly mass or Easter became 115

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troublesome since gatherings were not allowed. The sight was also impacted not only by the emptiness of familiar and less familiar landmark places such as San Peter square during the Easter Mass but also by the display on television of dramatic images such as those reporting on the number of daily deaths. Italians were mainly compliant with the lockdown rules, even though they were not always clear. Even though the violation of the lockdown rules and quarantine were condemned, Italian mainly complied because they understood the severity of the situation. Problems arose with the beginning of phase 2 characterized by an initial reduction of containment measures and a persistence of restrictions on personal mobility as a function of the reactivation of activities that would bring production systems back into operation. In this stage, regions went back in charge of managing the Pandemic, and different regional leaderships entered the competition. This stage was quite confusing because of a lack of clarity, coherence, and consistency in protective behaviors and social practices. Moreover, it is precisely in this period of intense reflection on the consequences of governmental choices regarding risk management that several other actors responsible for producing knowledge in addressing and supporting the choices to be made in this difficult management are entering the debate. This is how at least a hundred surveys were born within the Italian and international academies and various research institutes. The research questions, the disciplinary domains and the arguments touched upon are the most varied ranging from health and economic issues to more social ones. Among these studies, it could be contemplated the national web survey on COVID-19, government measures and social behaviors carried out by who wrote. The main research objective was to understand if and how ontological insecurity and cultural worldview may impact 1) risk perception, 2) adoption of protective measures, and 3) support toward governmental regulation during the COVID-19 Pandemic, especially after three months of a heavy lockdown. However, the survey focuses assessing of regulatory procedures and social behaviors undertaken by subjects in the second phase of COVID-19 crisis. Beyond the results achieved by the study, the richness of which is far from the scope of this chapter (for the detailed results of the study see Velotti, Punziano and Addeo, 2021), here the intent is to bring the reader in a methodological meta-reflection that invites to think about the consequences of the Pandemic on social research and its specific characteristics in the digital era and to the test of impossible physical presence.

BETWEEN BOLD CHOICES AND METHODOLOGICAL IMPLICATIONS The choices made in this research may seem bold and sometimes doomed to failure if framed within the framework of classical reflections on digitally transited social research (Hine, 2005; Roger, 2013). However, a logic other than the evolution of method alone has been adopted here and involves the evolutions in method induced by a change of scenery as impressive as the Pandemic. In particular, the method chosen to address the aims of the research was the web survey, considered by our research group the best solution to collect the opinions of a wide range of respondents in Pandemic time. Web surveys are becoming more and more familiar to researchers as the Internet has reached every place and every person in the world (Callegaro et al., 2015). Moreover, during the mobility restrictions forced by the lockdown measures, digital methods were almost the only feasible options to do social research, especially considering the research unit of analysis: Italian aged 18 and over. Respondents from this population were enrolled through a two steps sampling strategy. The first step was selecting consistent and reliable online questionnaire collection points; the second step consisted of using the selected collection points to collect responses to the online questionnaire.

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Facebook is still the most used social media platform in Italy, and it is also the most intergenerational one (Observatory Internet Media, 2020), that is why the selection of the online collection points began considering those Facebook pages and groups that met the following criteria: • •

number and participation: at least 500 members and with more than 20 posts per day, used as proxy of sharing and participation into communities’ activity; keywords: everything will be fine (#andràtuttobene), I stay at home (#iorestoacasa), at the time of covid-19 (#altempodelcovid-19), at the time of the coronavirus, emergency COVID/coronavirus, red zone, coronavirus Italy, Pandemic, swabs, phase 2, masks, I do not sanction, covid2019, quarantine, revival decree, restart, lockdown, start again, new normal, post covid-19, flash mob, applaud, balconies; geographical references, by selecting the thematic groups linked to the topics dealt with that contain clear references to regions, cities, territorial memberships (among them also the groups of You are from ‘name of the place’ if). The total list surveyed is about 500 pages and groups; however, not all our requests to subscribe to the selected pages and groups have been approved, just as not all posts containing invitations to fill in the survey have been approved, reducing the number of collection points to about 300. These Facebook groups and Facebook pages were used as collection points on which the invitation to participate in the survey has been placed every week along the collected period that goes from 1st of May to the 3rd of June 2020.

The main topics covered by the survey included: demographic questions, information and media trust, uses, and knowledgeability, values such as cultural worldviews, political orientation, and religious behaviors, risk profile with direct and indirect experiences with the virus, protective behavior, and the agreement with governmental measures to manage the crisis and the Pandemic. Questionnaire has 50 close-ended questions, of which at least 15 were Likert batteries, two compulsory open-ended questions and one final and optional open-ended question asking for voluntary comments on the survey and the study. The two steps sampling strategy led us to collect about 1500 responses. If this may seem a low number considering that hypothetically the link to the questionnaire reached almost 50,000 users; in this case, the response rate would be around 3%. However, given the willingness to participate, an average 20 minutes’ survey length, the result achieved is more than appreciable since almost all the respondents fully completed the questionnaires. Moreover, the respondents felt involved in the research, and this is also underlined by the fact that many people replied to the last open-ended question exceeding the usual five medium-length sentence standards with words full of emotions and feelings: a sign of high involvement in the research topics. The methodological choices discussed so far have been made deliberately in contrast to the methodological habit regarding the administration of web surveys, and this was based on the intuition that the world had been turned upside down by the Pandemic, starting with everyday life and ending with the new emotionality, behavior and collective feeling that our research tried to explore. In the Social Sciences, all the empirical studies have their methodological limitations whose function is not to limit the gnoseological scope of the research results, but to frame them in the right perspective; this research is no exception. The methodological limits of our research lie mainly in the non-probabilistic sampling procedure and in the limited time range of our research object, phase 2 of the Pandemic in Italy. Our sampling procedure certainly has weaknesses, such as the impossibility of having a representative sample due to the distribution of the reference population with respect to geographical and sociodemographic characteristics. According to the traditional quantitative perspective of social research, 117

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these sampling weaknesses would not allow for statistical generalization of the research results and, again, according to this line of thought, this would be a major limitation. In recent decades, however, epistemological reflections in social research methodology have downsized the relevance of probability sampling as a necessary element to define the scientific level and the quality of a research. This is also true if we consider that, as highlighted by scholars such as Marradi (1997), the random extraction on which probability sampling is based does not absolutely guarantee statistical representativeness and, consequently, results in generalizability. Shifting the focus from general considerations to what we did, it must first be said that our research is exploratory. Moreover, the research team addressed the problem arising from the sampling procedure by adopting some statistical weighting techniques, widely used in social research, including in recent studies on the social impact of COVID-19 in Italy (see, for example, Lombardo & Mauceri, 2020). Regarding the temporal scope of our research object, the opinions and perceptions of Italians during phase 2 of the COVID-19 Pandemic, there is no doubt that the results obtained from our analysis could be outdated if we consider the events that occurred in the following months. However, any study in the social sciences must be correctly interpreted by considering the spatiotemporal context of reference without limiting its scientific relevance and gnoseological scope. The results of our research depict a social and cultural slice of life of Western society, the Italian one, grappling with an unpredictable event that has created a crisis whose consequences are not yet commensurable. Our results may be useful in interpreting other crises like this one that may occur in the coming years. In addition, this study can be helpful in understanding mistakes made during stage 2, the so-called reopening that subsequently led to a second Pandemic wave. Specifically, the use of a sampling strategy based on the first definition of the posting strategy and then on the dissemination strategy of the questionnaire leads us to reflect on the importance of calibrating the sampling tools and the concept of representativeness no longer on the conventional geographical and socio-demographic dimensions, but more on the digital social infrastructure, the only one that withstood the challenge and did not suffer from the limitations of the freedom by the virus containment measures. It is not only a matter of recovering the concept of post-demographic research introduced by Rogers (2013), according to whom the digital researcher, rather than working on individuals, deals with the set of actions they perform on the Net, contributing to the definition of the digital scenario. In this case, it is a question of focusing on the individual and not on the actions, sampling the digital scenario as a set of spaces within which the research target population can be relatively circumscribed, identifiable, and technically reachable beyond the limitations and social distancing. These spaces, imaginable as a map even when they do not create real communities, are nevertheless collectors of users, and if they are identified by maintaining explicit criteria to guarantee the heterogeneity of the sample reached, they can also guarantee the maximum heterogeneity in the data collected by the researchers. A second turning point of this research is the intuition that, despite a long and demanding questionnaire, the respondents have filled it in anyway. Not only because of the greater availability of time and online presence in a period of forced isolation, and restricted mobility, but above all because the respondents as citizens and people affected by a destabilizing event needed to express themselves, to do their part, and to think they were useful in a situation where they were confined to their homes. And it is precisely in the light of this element that an even more daring choice was made, and it consists into the discussion of the last question, the voluntary open-ended one, posing it as the heart of our discussion in this chapter.

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PARTICIPANTS’ FEEDBACKS In the survey research, it is usual to leave a final open question to collect comments, feedbacks, and feelings expressed by the respondents. However, research experience leads us to say that in most surveys, when conducted with adequate scientific rigor, this type of space is mostly filled with thanks, compliments, or expressions of interest in the survey results. When the rigor is lower, instead, technical suggestions (which are the ones the researcher expects to collect just to reflect on the perceived limits of the survey) and explicit criticism may prevail. Usually, these spaces do not collect great reflections, especially when the web surveys are long. Furthermore, because it is not a compulsory question, this space is not filled out, and the questionnaire is sent directly filled in its mandatory part. However, now we are experienced a time in which uncertainty prevails, and a perception of endemic risk does not seem to abandon the collective imagination. For these reasons, individuals taking part in the survey seek spaces of expression to release these emotional tensions by verbalizing and framing them. In our research, the web survey was subjected to the test of sampling plans, posting strategies to ensure heterogeneity of the population reached if not representativeness of the cases, but above all, it was subjected to the test of length and the inclusion of open-ended and non-obligatory questions. As anticipated, the reorganizations of the everyday life introduced by the Pandemics lead us to reflections of undoubted interest for research. Our reflection starts with an autoethnography of the answers (Chang, 2016) to this last question; the generic formulation of the question lies precisely in the fact that rather than being a real question, this is a formula of leaving for those who filled in the questionnaire: “We thank you for your patience and the contribution that is certainly fundamental to our study. You can leave your concluding remarks in this space. If you would like to, please leave a comment on the questionnaire and any general suggestions”. By constantly monitoring the survey, we noticed at the end of the first week that out of the first 900 replies received, about 800 had an answer to this last question. However, this was not a generic answer. Many of these were reflections matured. In total, more than 50% of the respondents to the non-forced answer on additional things to say on the study have left insightful comments. We were stunned by this exceptionality, so we decided to analyze these responses by categorizing them into proper classification. Looking at table 1, we expected most responses to be compliments and thanks; this was not the case as these two categories together accounted for 16% circa of the responses. The first thought this distribution led us to was that the questionnaire probably had technical problems, so we checked what breadth in the distribution the technical suggestions and explicit criticisms had. Following the recording, “technical suggestions” was the modal category (19.3%). The main criticism of the survey was its excessive length, which ranks third in the distribution (10.0%). This denotes that the sample maintained a high level of focus in such a lengthy study designed to go in-depth with respect to certain issues. And while this may mean that perhaps more attention should have been paid to the wording of the questions or the usability of the survey with digital tools, we are talking about a total of 29% of responses that with the 16% of responses in compliment and thank arrive to the 45%. So, the question we asked ourselves was: what about the other answers? What is surprising is that the categories that follow in order of distribution are those that highlight the need for research to develop knowledge, social utility and become a tool for intervention in the governance of the emergency (about 13.0%), but above all conceived as a useful tool for self-reflection, a way to do one’s part (9.3%). In the first category, named collective usefulness of the research and perception of self-efficacy, among the others, we could mention comments like:

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Table 1. Distribution of recoded open-ended questions among prevalent categories Categorisation of responses to voluntary comments on the survey

%

technical suggestions

19.3

collective usefulness of the research and perception of self-efficacy

13.0

excessive length

10.0

self-reflection and awareness of the risk situation

9.3

compliments

8.6

thanks

7.3

hopes and return to normality

6.0

request for updates on results

5.7

recriminations, pessimism and fears

5.3

policy suggestions

5.0

call to responsibilities

4.7

suggestions for research

2.7

call for accountability

2.0

denials and conspiracy

1.3

Total (n = 1150)

100.0

• • • • • •

• •

I hope it helps to make it better; I hope it gives a serious answer to the questions of the people; I hope my answers will be helpful to your study. Thank you for this moment of personal reflection; ...also included an open-ended question so I could explain why, behind an answer there would always be an argument to be made; Thank you for giving each of us the opportunity to express our opinion on Covid -19; I wanted to thank you researchers who are working to map as accurately and as possible the legacies of the Pandemic in this country. Your work is invaluable, and although I am not involved in research, I value the work you do. You researchers should have much more prominence and consideration for what you do, but above all: your salary (I speak at least of the Italian situation that I know quite well) should be discreetly higher; I really appreciated this questionnaire for two reasons: first of all, it served me as an example since it is the subject of the studies I am following and secondly because I think it is important and useful to give voice to what we young people think about it; Without research, there is no future.

These comments refer to the spirit of self-efficacy together with a perception of the usefulness of research in improving and governing uncertain times like the one we live in. Self-efficacy expresses a sense of empowerment one has for a situation. In a nutshell, this research made the respondents feel useful for contributing to the resolution or understanding of the management and response of COVID 19. The second category we refer to, self-reflection and awareness. Self-reflection are comments lefts by the respondents and concerning the importance of the questionnaire to present an opportunity for reflection on their and other behaviours.

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• • • •

The questionnaire is well constructed and leads one to reflect on our own behavior in recent months and that of others around us; It makes one reflect on situations that are very likely to arise; I hope that people with this second phase will not take anything for granted about what has happened in these two months and is still happening. The questionnaire is, in my opinion, very thoughtful and satisfying; Excellently structured. In my opinion, by answering the questions we can also clarify within ourselves, obviously with the limits of knowledge that each of us has. Direct participation also induces us to feel part of a community, to want to engage in it and perhaps be more demanding with those who represent us politically.

The opportunity for self and other reflection ranges from observing of one’s behaviour to that of community of practices and more in general other peers. The reflections range from justifying other actions contextualizing them in the deep uncertainty generated by COVID 19. Also, respondents provide reflections on human condition and conduct in philosophical and moral terms. • • • • •

• •

•

Dealing with a new situation like this Pandemic is not easy for anyone. We move forward by trial and error, hypothesis, tests and experiments. I thank those who help us, even by making mistakes; Great questionnaire thanks. My understanding is that it is hard to swear at the government’s actions since no one has ever addressed something like this. So, all in all this is good. The economy will take some damage, but I am very optimistic about recovery; The questionnaire is well constructed and leads one to reflect on our own behavior in recent months and that of others around us; I have noticed that too often they let people who are not in the field talk about sensitive issues, spreading false and dangerous news... a lot of information has been piloted... first spreading terror and panic and then extreme superficiality; Some rigid behaviors and careful observation of the rules imposed, are purely due to the situation of health emergency and family responsibilities. Normally, it would all be quite paradoxical, but I believe that respect for these basic rules, oneself and others, can bring us to a quicker end of the situation; It has surprised the total absence of a Citizenship involvement and the request for a useful “active participation” that was not the only, restrictive and authoritarian stay at home; I hope that this terrible experience teaches us to be more human and that the state comes to the aid of all those people who have lost their health, their work and their dignity. It is right that the weakest people are protected and that the health system is strengthened. Because even the powerful will need assistance sooner or later; Even if from the point of view of large numbers, the system of social life from all points of view - economic, health, organizational, anthropological, etc. - will inevitably be modified, the individual behavior - and, consequently, social - will slowly return to its original state. Like all animals on Earth, man is essentially characterized by individualistic and only rarely altruistic sociobiological behaviors, aimed at the survival of himself and his family group. However, while animals have always had to deal with nature and the animal environment that surrounds them, therefore they must submit to the strict rules of survival of the strongest and the most cunning (from a sociobiological point of view), man since he evolved as a species sapiens has completely overturned the 121

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•

rules of survival and evolution, putting the surrounding environment and all other living beings including humans - at his service. In fact, it has triggered a mechanism that if on the one hand has led to countless significant discoveries in various fields of science and knowledge, with progress unthinkable only a few decades ago in the treatment of diseases, literacy, food production, sustainability of production activities and so on, at the same time has ended up losing sight of its origins as a biological species and respect for the natural world and his fellow man, whose future and existence itself are linked by a single thread. Today, the man who was able to improve his existence and even life expectancy has become at the same time the author of its destruction. Hope lies in the generations of our children and their children’s children, if they will be able to find a more just balance in the coexistence between man and nature, aware that only knowledge and awareness of our limits as a human race can save us from the inevitable social and environmental decline; We are in care, not in war... For this reason, the awareness of being in care - and not in war - is a fundamental condition also for the “after”: the future will be marked by how much we will have been able to live in these most difficult days, it will be determined by our ability to prevent and care, starting with the care of the only planet we have available. If we know and know how to be custodians of the earth, the earth itself will take care of us and will preserve the indispensable conditions for our life. Wars end - even if they resume as soon as the necessary resources are found - care instead never ends. If, in fact, there are diseases that are incurable (for now), there are not and never will be incurable people.

According to the excerpts reported, respondents cover different topics: politics, health management of the emergency, economic and social situation, are debated topics, also argued in a rich and emotionally way, to glimpse precisely that need to express themselves that has made it possible to have comments so rich and useful to the reflection of the method. This led us to reaffirm with conviction that in times of uncertainty and restricted social relations, what remains for individuals is the ability to express their voice through all means available thanks to the digital, whose interconnection is not put in crisis by the current Pandemic. And the web survey is equated to any digital devices that allows to amplify one’s own voice and reflections that one wants to leave to others strongly and decisively, to the researchers, and to the world. The sample’s desire for participation and demand for knowledge also spills over into the request for updates with respect to the survey, a category that recovers 5.6% of responses. Comments such as: • • • • •

It would be appreciated to be informed about the overall outcome of these surveys, the usefulness they will have and the inferences they will lead to; Good luck with the scientific publication, it will certainly be interesting. I hope to be able to view it before it completes mine (albeit of a different nature, specifically: legal); I would love to read the results of your research! Very detailed questionnaire, I am curious to read the results; I would love to be able to read the results of your analysis.

On the other hand, emotional engagement is recovered by positive categories, such as hopes and return to normality, and negative ones, such as recriminations, pessimism and fears. In the first case with comment such as: 122

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• • • •

I would hope not to go back to normal. That was the biggest risk; It’s crucial that politicians understand the importance of mental health as well as physical health, you have to allow people to go to their relatives outside the region; I would like the government to act more decisively. I know very well that it is not easy to deal with such a situation. I know they are all in good faith. But they have unfortunately made mistakes that I consider serious; I have hope that we will return to normal soon, but who knows if the “powerful” want that. No fear of the virus but of how they are manipulating us. In the second case, instead, with comments like:

• • •

•

I am very concerned about the situation we are experiencing, but one in particular worries me! democracy! Beware of the wounds on the Planet that will not heal; There are too many entrepreneurs who ask for a subsidy even though they are big tax evaders, they cry poverty worse than their employees... It would take a strict fiscal control, if now the government turns a blind eye and helps them must be regulated that at a later time, who will have received unduly because x false statements or mendacious in part or x because discovered evader not only will have to return everything but will be sanctioned in various ways, making him close the business or paying a heavy fine and a percentage of what was declared. With the obligation of registration in a public register to be kept exposed in the municipalities and provinces and regions; It must be admitted that the measures of restriction of freedoms and constitutional rights have been taken because we have not been able to isolate the positive covid19. Treatment worse than evil will produce its harmful effects.

What gives hope is the hope for a return to normalcy, the power of the vaccine, the possibility of imagining a possible future. But, on the other hand, it is the normality, the mismanagement, the corruption of the political system or of people lacking responsibility, ethics and dignity, even in extremely difficult situations, that is worrying. And, even at this point, a reflection of the method comes in. The topics highlighted in the non-forced answer under analysis were in part also addressed in the other two open questions provided within the survey. However, the responses to the non-forced question expanded upon and remarked some of the comments previously made, just to emphasize once again the need to find spaces to express themselves. Of the same calibre are the call to collective and individual responsibilities (4.65%) and the call to accountabilities (1.99%), with respect to government performance, emergency management, and politics: • •

What strikes me very much in this phase 2 is the irresponsibility of many people who behave as if nothing had happened or as if everything was over; After phase 1 I started again to feel an irritation towards politics that seemed not very responsible: quarrels among themselves and discrediting others to get votes. I am also sorry to see all these people become “sergeants at the window” who vent their anger and fear towards others, we should help these people and educate those who do not adopt responsible behaviors (instead of punishing or insulting them);

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•

I hope that people with this second phase will not take anything for granted about what has happened in these two months and is still happening. The questionnaire is, in my opinion, very thoughtful and satisfying.

Other categories that help us reflect on the depth of the arguments collected with this last box are those that we have defined policy suggestions (5.0%) and suggestions for research (2.7%). The policy suggestions range among a very vast set of argumentations such as: • • • • • • • •

Each region should manage its own needs and close the border; It is absolutely necessary to change the current government under the leadership of Minister Conte, since it has shown itself to be not up to ‘the situation; I hope you will take the opportunity to implement Smart working, distance learning and other institutions that allow people to enjoy culture or work even if unable for any reason to move; I would suggest to the institutions a drastic reduction in public spending for the management of the bureaucratic bandwagon and for the subsidies to parties, newspapers and other useless political and information media expenses; Any contact with other people or with places frequented by other people is now dangerous for one’s own health and for that of the community; We will never get out of the crisis if Italy’s political/economic management is not radically changed. We won’t get out of it if healthcare doesn’t become public again and, above all, “ethical”. Therefore, it is not possible to determine how long it will take to get out of the crisis; We must not dream of a life as before without a vaccine that defeats Covid. Until then no buses, trains, subways, more bikes. Stimulate people to bike/electric to work. Create the infrastructure for bicycles; I wish institutions would listen to doctors more than economics. This is a war and in war no country progresses economically. If, for having opened too early and for the congenital indiscipline of the Italians there is a new peak like today in Korea we will really go down economically.

This space is rich, dense, meaningful. Full of arguments that come from a profound re-elaboration of the situation and from a lived experience that surely not everyone is allowed to have with respect to other issues and situations of risk. Perhaps it is precisely in this democratic nature of the knowledge of the risk that has been given to all through the more or less direct experience with the virus that has made possible such a complex of arguments, which in the final instance are poured into the suggestions for research to be read as food for thought, addresses for the production of new investigations and highlighting areas of knowledge that citizens themselves feel the lack of coverage by the world of scientific research: • • •

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It would be helpful to know more about the situation of in and out of school students to support them and their families. I have not seen any questions about this; Very interesting, I would introduce questions with hypothetical proposals for political and economic reforms; The questionnaire succeeds is covering the most important points of the current situation we are experiencing, despite this, I would recommend adding a mini section regarding the sudden reopening of churches and places of worship, personally senseless and potentially dangerous;

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• • • • •

I hope that all the research being done on the current situation will bring out the great difficulty of families with children, which often translates into mothers sacrificing work for their children; Thank you for this survey. I hope you will give space in your studies to the effects of smart working in the world of work (social, psychological, economic and environmental, as well as on the quality of work produced in this mode); Include questions about the condition of women before during and after the crisis. They would be interesting results; You could raise questions about coverage of very critical age groups. We young people have no present and no future. No stability; It would be nice to know if people’s level of depression, or even eating disorders.... Have increased during this situation.

We can say the respondents are showing ways in which they would like to participate in the academic work. This is a paradigmatic shift of the participant who wants to be co-producer and co-creator of knowledge. Thus, the result of this survey can also be read as a successful way of creating a partnership between the researcher and the respondents (Velotti, Botti &Vesci, 2012; Velotti & Murphy, 2020). Finally, the residual category that collects 1.3% of comments has been defined denials and conspiracy and presents comments such as: • • • •

We fight tenaciously against this atrocious staging, against the lies, the war is without quarter. Powerful forces beyond the earth are helping us; It is much easier to deceive people than to convince them that they have been deceived! Don’t try to make this story last any longer: it’s over. Finish it; The crisis has been intentional and piloted to frighten, control and impoverish the people and unfortunately Latin people are more vulnerable than Northern people.

This category shows that different opinions have been reached and with them also that slice of the sample that possesses them, pointing out once again that an adequate posting strategy that maps the digital and scrutinizes it for circumscribed areas of users cannot but be effective in times of total uncertainty and limitations to physical co-presence.

CRITIQUES AND PROBLEMATIZATIONS FOR FUTURE SCENARIOS OF ANALYSIS APPLIED TO DIGITAL RESEARCH The current COVID-19 crisis will be engraved in the worldwide collective memory as a disruptive landmark generational event, like the Second World War, or the 9/11. Most scholars, media professionals and ordinary people are convinced, rightly or wrongly, that the Pandemic we are currently experiencing is already an event of profound historical significance that should be given a prominent role in the daily narrative: the Pandemic is constantly evoked in the media and in everyday life with expressions such as ‘in times of COVID-19’ or ‘in times of Pandemic’. Thus, attempts to historicize the Pandemic have already begun, even before it has actually ended and before we have any idea of its social, economic and political consequences.

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Thus, the Covid-19 Pandemic is not just a ‘simple’ epidemic like others that have occurred in the recent past, it is something profoundly different. In this respect, Sir Anthony Giddens states that we are experiencing the world’s first ‘digidemic’ because the digital age has radically transformed our societies and our daily lives. According to Giddens, the peculiarity of the current Pandemic has its origin in the structural changes that have simultaneously contributed to cause it, and to constantly reshape it. The current circumstances caused by the Pandemic have therefore been shaped by problems endemic to contemporary societies: racial and economic inequality, industrial and agricultural production systems that have generated unsustainable development, national health systems that have been put in crisis by funding cuts, and a public opinion that, partly due to social media, has become too emotional and impulsive, to the point of conditioning with its anxieties the ability of the world’s democracies to deal with crises and emergencies. For these reasons, the Covid-19 has destabilized the organization of societies and people’s daily lives all over the world. No aspects of social life were unaffected by the Pandemic: from individual choices on what protection and safety measures to adopt, to national governments’ decisions on public health and economic policy; from the management of family life, to changes in the workplace; from the need to rethink, due to social distance, almost all human interactions on the basis of socio-technical mediation of technological devices, to the proliferation of disinformation, whereby false or distorted news, even from official sources, had a negative impact on the management of the crisis by governmental and health institutions. This cultural climate also impacts many research activities from several scientific fields: scholars have to deal with a phenomenon constantly mutating, both in its concrete clinical manifestations and in the way society, economics, and politics are affected by it. To deal with such a changing research object, social research on COVID-19 is certainly not standing still in its attempt to understand the Pandemic as a social phenomenon, and consequently to generate knowledge-based responses that can be useful to institutions and citizens. For example, many scholars are trying to create models to predict the effects of COVID-19 on national economies in the medium and long term, which are generally leading to not very rosy forecasts. In fact, according to some authors the Pandemic will likely lead to a decrease in financial resources, enlarging the gap among poor and rich and increasing inequalities in access to education and research (IIEP & UNESCO, 2020; Bania & Dubey, 2020; Schleicher, 2020). Looking at this situation from a broader perspective, we can say that the whole range of social science disciplines - analytical frameworks and research methods - is called into action to understand how to deal with the Pandemic and its consequences. Considering what has just been written and drawing on our own research experience, from here on we will discuss some epistemological and methodological reflections on the future of social research after COVID-19. The Pandemic represents a major epistemological challenge for the social sciences: it is an object of research that has not yet been crystallised or defined, which does not make it ready for a comprehensive a posteriori analysis. In addition, the climate of uncertainty caused by the COVID-19 emergency caused the emergence or spread of new social actions and behaviours, the so-called Pandemic social practices, i.e. the social practices that emerged and continued during the Pandemic and were somehow linked to the discovery and spread of the virus (Werron & Ringel, 2020). These are all those human actions that we could define as generative because they not only have to do with the virus, but they literally give birth to it and reproduce it, i.e. they turn it into a concrete social phenomenon to be reckoned with. As 126

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mentioned, the Pandemic could increase social and health inequalities, but it could also be an opportunity, as Amartya Sen says, to bring progress to today’s societies in terms of greater social cohesion and improvement of the general health of the population. Social research has a duty to explore and create the potential conditions for moving towards the positive scenario envisaged by Amartya Sen: to ensure that opportunities can emerge from the crisis, through rigorous sociological analysis and empirically based proposals aimed at avoiding, or at least mitigating, the negative effects of the current crisis. Currently, the most serious problem concerns concrete empirical research activities: traditional social science research methods are either impractical or require adaptation to protect the health and safety of both researchers and research subjects. The Pandemic has had many strong impacts on the lives of social scientists both regarding the concrete research activities and the relational activities related to the work of a researcher. An example of the latter type of impact has been the need to cancel or postpone all live discussions, such as conferences and seminars (Schleicher, 2020). At this moment, the academic and scientific debate is only taking place online due to mobility restrictions. The absence of these forms of cultural and scientific discussion “may undermine research efficiency and innovation in the future” (Bania & Dubey, 2020). From the perspective of social research methodology, COVID-19 has had an ambivalent impact on how research is conceived and conducted; in other words, there have also been both positive and negative effects: not all bad things come to worst. The Pandemic has had the undoubted negative effect of making impossible all data collection procedures that rely on communication and relationships between people. This has damaged both qualitative and quantitative approaches. The former because the most common qualitative data collection techniques, interviews, focus groups, observation, are based on interactions between subjects within a social and cultural context. The latter, especially the survey, have been affected by the restrictive measures adopted during the Pandemic: in the last year, people have been inundated with a huge number of online questionnaires, many of them with very low quality standards, raising legitimate doubts about the quality of many studies conducted in recent months. In other words, the scientific validity is obviously fluctuating, and it is likely that the current modus operandi, internalized by younger researchers in particular, might lead to lower the average quality of the research activities in the long term. However, the Pandemic has also had the positive effect of speeding up the spread and adoption of new methodological trends within social research, especially digital methods. This is because some of the living conditions of individuals have changed: mobility restrictions have forced them to stay at home, constantly practicing the condition of being ‘always connected’, which, on the one hand, has led people to produce almost continuously data with their online activities, posts, comments, likes, tweets, i.e. big data; on the other hand, it has made them more available for research carried out with quantitative or qualitative digital methods, i.e. small data. “In other words, as with many other aspects of social life, the Pandemic has acted as an accelerator of practices gradually but also very slowly establishing: COVID-19 has overturned the inertia of digital innovation in social research” (Velotti, Punziano & Addeo, 2021). The research experience discussed in this chapter is a valid evidence of what has been argued so far: in a pre-Covid era, it would not have been possible to involve such a large sample as ours in such a short time. Other research conducted in Italy during the same period achieved very high sample sizes (Lombardo & Mauceri, 2020). During the Pandemic, Social Sciences have been able to be creative, both by revitalizing traditional methods that had never been mainstream, such as network analysis or

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content analysis, and by experimenting with new solutions, such as topic modelling, machine learning and digital methods (Veltri, 2017; Molteni & Airoldi, 2018). Social research can play a crucial role in understanding the social and cultural effects of critical events, such as a Pandemic, by using digital methods to explore and interpret these phenomena. Therefore, social researchers need to address and overcome the challenges that emerge from digital methods, while making the most of its advantages, by developing appropriate epistemological and methodological solutions, along with efficient procedures for testing the reliability, validity and quality of research tools (Bania & Dubey, 2020). The future of social research lies in its ability to hybridize with digital technology without losing its nature. As Velotti, Punziano & Addeo (2021) pointed out, COVID is turning out to be a great opportunity for Social Science to innovate its theoretical and methodological background. The huge number of publications during the Pandemic testifies that Social Sciences still have much to say about how our societies may improve, by helping policy-makers to develop shared strategies that can lead to an inclusive and sustainable recovery after the Pandemic. Above all, social research can ensure that the voices of those communities in need are heard and discussed and that those affected can be involved in the decisions that affect them. Social science thus regains a central role within the public and scientific debate, proving to be a valuable provider of antibodies to deal with crises and emergencies such as the one we are currently experiencing.

ACKNOWLEDGMENT This research was not supported by external funds. However, our thanks for these reflections go to the respondents to the survey, capable, sensitive and kindly people whose commitment has helped to bring about the epistemological and methodological reflections necessary in an age of great upheaval.

REFERENCES Ansell, C., Boin, A., & Keller, A. (2010). Managing transboundary crises: Identifying the building blocks of an effective response system. Journal of Contingencies and Crisis Management, 18(4), 195–207. doi:10.1111/j.1468-5973.2010.00620.x Baekkeskov, E. (2015). Transboundary crises: Organization and coordination in Pandemic influenza response. Disaster research: Multidisciplinary and international perspectives, 189-206. Bania, J., & Dubey, R. (2020). The Covid-19 Pandemic and Social Science (Qualitative) Research: An Epistemological Analysis. http://www.guninetwork.org/report/covid-19-Pandemic-and-social-sciencequalitative-research-epistemological-analysis Boin, A., T’Hart, P., Stern, E., & Sundelius, B. (2016). The politics of crisis management: Public leadership under pressure (2nd ed.). Cambridge: Cambridge University Press. Boin, A., T’Hart, P., & Kuipers, S. (2018). The crisis approaches. In H. Rodríguez, W. Donner, & J.E. Trainor (Eds.), Handbook of disaster research (pp. 23–38). Cham: Springer International Publishing.

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Boin, A. (2019, January). The transboundary crisis: Why we are unprepared and the road ahead. Journal of Contingencies and Crisis Management, 27(1), 94–99. doi:10.1111/1468-5973.12241 Boin, A., & Hart, P. T. (2007). The crisis approach. In Handbook of disaster research (pp. 42–54). Springer. doi:10.1007/978-0-387-32353-4_3 Callegaro, M., Manfreda, K. L., & Vehovar, V. (2015). Web survey methodology. Sage (Atlanta, Ga.). Chang, H. (2016). Autoethnography as method (Vol. 1). Routledge. doi:10.4324/9781315433370 Clinch, M. (2020, March 14). Italians are singing songs from their windows to boost morale during coronavirus lockdown. CNBC. https://www.cnbc.com/2020/03/14/coronavirus-lockdownitalians-aresinging-songs-from-balconies.html Goldin, I., & Mariathasan, M. (2014). The butterfly defect. Princeton University Press. doi:10.2307/j. ctt5hhqgq Hine, C. (Ed.). (2005). Virtual methods: Issues in social research on the Internet. Berg Pub Limited. IIEP & UNESCO. (2020). What price will education pay for COVID-19? IIEP-UNESCO. https://www. iiep.unesco.org/en/what-price-will-education-pay-covid-19-13366 Lombardo, C., & Mauceri, S. (2020). La società catastrofica. Vita e relazioni sociali ai tempi dell’emergenza Covid-19. FrancoAngeli. Marradi, A. (1997). Casuale e rappresentativo: ma cosa vuol dire? In Politica e Sondaggi. Torino: Rosenberg & Sellier. Mineo. (2020). Harvard expert compares 1918 flu, COVID-19. Harvard Gazette. Molteni F., & Airoldi M. (2018). Integrare Survey e Big Data nella pratica della ricerca. Sociologia e ricerca sociale, 116. . doi:10.3280/SR2018-116009 Observatory Internet Media. (2020). Annual report. Politecnico di Milano. Quarantelli, E.L. (2000). Emergencies, Disasters, and Catastrophes Are Different Phenomena. Preliminary paper #304: University of Delaware Disaster Research Center. Quarantelli, E.L. (2006). The Disasters of The 21st Century: A Mixture Of New, Old, And Mixed Types. Preliminary paper #353: University of Delaware Disaster Research Center. Quarantelli, E. L., Lagadec, P., & Boin, A. (2006). A heuristic approach to future disasters and crises: New, old and in-between types. In H. Rodriguez, E. L. Quarantelli, & R. R. Dynes (Eds.), Handbook of Disaster Research (pp. 16–41). Springer. Rogers, R. (2013). Digital methods. MIT Press. doi:10.7551/mitpress/8718.001.0001 Schleicher, A. (2020). The impact of COVID-19 on education - Insights from Education at a Glance 2020. OECD. Velotti, L. (2016). Accountability in Decision Making Process for Vertical Evacuation Adoption (Ph.D. thesis). University of Delaware.

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Velotti, L., Botti, A., & Vesci, M. (2012). Public-private partnerships and network governance: What are the challenges? Public Performance & Management Review, 36(2), 340–365. doi:10.2753/PMR15309576360209 Velotti, L., & Murphy, P. (2020). Service and value co-production and co-creation in emergency services and emergency management. International Journal of Emergency Services. doi:10.1108/IJES-05-2020-069 Velotti, L., Punziano, G., & Addeo, F. (2021). Covid 19: Government policies and human social behavior in Italy. CRC Book, Routledge. Veltri, G. A. (2017). Big Data is not only about data: The two cultures of modelling. Big Data & Society, 4(1), 1. doi:10.1177/2053951717703997 Werron, T., & Ringel, L. (2020). Pandemic Practices, Part One. How to Turn “Living Through the COVID-19 Pandemic” into a Heuristic Tool for Sociological Theorizing. Sociologica, 14(2), 55–72. doi:10.6092/issn.1971-8853/11172 WHO. (2010). What is a Pandemic? WHO. WHO. (2020). Director-General’s opening remarks at the media briefing on COVID19. WHO. Yuko. (2020). Ways Coronavirus Is Different From All Epidemics Through History. Reader’s Digest.

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The chapter is the result of the collective work of the three authors. However, Lucia Velotti is responsible for the introductory and methodological part, Gabriella Punziano for the methodological, procedural and analytical development and the processing of the findings, and Felice Addeo for the critical and concluding part.

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Chapter 9

What Prevents You to Plug in to Online Surveys? Burcu Karabulut Coşkun https://orcid.org/0000-0001-5287-2239 Kastamonu University, Turkey Ezgi Mor Dirlik Kastamonu University, Turkey

ABSTRACT In today’s world, which has been administered by computers and artificial intelligence in many areas, online data gathering has become an inevitable way of collecting data. Many researchers have preferred online surveying, considering the advantages of this method over the classical ones. Hence, the factors that may affect the response rate of online surveying have become a prominent research topic. In line with the popularity of this issue, the purpose of this chapter was to clarify the concept of online surveys; give information about their types, advantages, and usage; and investigate the factors that affect the participants’ response behaviors. Besides the discussions on the theoretical framework of online surveying, an online survey aiming to determine the factors affecting the participation in online surveying was administered to a group of people to investigate the response behaviors thoroughly. The findings revealed that rs might affect ants’ response behaviors to online surveys in various ways radically.

INTRODUCTION Surveys are systems used to obtain or explain the attitudes, behaviors and personality traits of individuals. These systems consist of 7 stages. These stages are; setting goals, designing research, identifying or developing valid and reliable data collection tools, collecting data, organizing, analyzing and reporting data (Fink, 2003). There are many types of surveying. Online surveying is one of these types. The online survey method has become a popular data collection method in educational settings for the last three decades. Due to the COVID-19 pandemic, the online survey method has started to be used more than ever because of the limitations. The importance of the Internet and other online means for DOI: 10.4018/978-1-7998-8473-6.ch009

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collecting data is perceived as critical technological improvements in the data collection field. Dillman (2007) stated that email, the Internet, and other electronic survey methods dramatically improve data collection efficiency. These efficiencies consist of nearly the complete elimination of paper and pencil, postage, emailing methods of data collection. Also, the data entry costs have disappeared. Also, he stated that thanks to the electronic-online survey method, it has been more possible to reach international samples. In addition, the time required for survey implementation may be reduced from months, weeks to days, and sometimes hours. Thanks to low cost and time efficiency, these methods allow for synchronous time; hence respondents and researchers get the chance to observe the data results being compiled instantaneously. Online surveying, also known as web or internet surveys, is used by both the managerial and academic communitiesnity. It is widespread for surveying business areas, especially in marketing and consumers, academic studies, theses, and research. Despite the advantages and widespread usages in many fields, several issues should be considered in online surveying. The advantages listed above have made online survey methods the most preferred data collection method in many research areas. While using online survey methods, the researchers should consider the factors that may affect this data gathering process. Response rate is one of the most crucial factors that may affect the whole process of data gathering in terms of online surveying. The response rate in online surveying depends on many factors arranged and controlled by the researchers. These factors are sample, delivery mode, invitation design, the use of pre-notification and reminders, and the use of incentives (Sales and Bista, 2017). Because of the importance of the response rates in the research settings, these factors have been investigated in many studies. For example, Fan and Yan (2010) asserted that sponsors might influence the response rates. It has been found that targeting the population is vital in response rates, and students and employee populations have been defined as the most likely groups to respond to online surveys than the general populations (Shih & Fan, 2008; Vance, 2011). As for academic research, the usage of invitations that include personal greetings, titles, and addresses significantly increased response rates (Heerwegh, Vanhove, Matthijs, & Loosveldt, 2003; Joinson, Woodley, & Reips, 2007). Also, asking for help from the responders is more effective in increasing the response rates (Porter, 2004a). In several studies, it is also found that reminders and pre-notification are the significant elements that enhance the response rates in online surveying (Porter, 2004b; Spruyt & Van Droogenbroeck, 2014; Veen, Göritz, & Sattler, 2016). The listed studies are only a limited part of the related literature, and many studies are investigating the response rates in online surveying worldwide. However, the same situation is not valid in Turkey. Online surveying is a relatively new issue in Turkey, and it has been popular since the 2010s. Hence there is a limited number of studies investigating the factors that may influence the response rates of the students’ in Turkey. For this reason, it is aimed to analyze the factors that may change the response rates of adolescents in online surveying. Within the context of this study, the research questions are listed below: 1. What are the most determinative factors affecting the adolescent online survey response behaviors? 2. Are these behaviors affecting demographic variables related to participants? Before investigating these research questions, related literature that helps to clarify the reasons hindering the people from attending an online survey was provided in this part of the study. Firstly, the online survey concept was discussed with the item types used in online methods, online surveys, the steps in

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online data collection, advantages and limitations of the online survey, and the factors that negatively affect the online survey response rate.

BACKGROUND The Types of Items That May Be Used in Online Surveys Thanks to the facilities provided by the computer technologies, it is possible to use various types of items in online surveying. Online surveys include dichotomous items, multiple-choice items, items in a multimedia format, single-response and multiple-response items, and even open-ended items. For example, Survey Pro promotes the many types of questions available in its surveys at the company website (www. surveypro.com/info/design.html), such as: • • • • • • • • • • • •

Yes/no or other dichotomous questions. Open-ended text responses (long answer and short answer). Multiple choice – select exactly 1 of n (select a single answer). Multiple choice – select exactly k of n (select a fixed number of answers). Multiple choice – select as many as k of n (select variable number of answers). Multiple-question batteries (multiple questions using the same scale). Likert and semantic differential questions. Open-ended questions. Rank order (preferred first choice, second choice...). Constant sum (allocate 100 points among choice options). Horizontal-scale, vertical-scale, drop-down, side-by-side formats. Conjoint analyses questions.

The items listed above showed that many item types are possible to be used in online surveys, and some of these items are not feasible to be used in paper-pencil tests. The possibility of allowing such a wide range of items simultaneously has been one reason for online surveys’ preferences. In this way, it is possible to measure different levels of cognitive skills more efficiently at the same tests.

The Use of OS - Steps in Online Data Collection As an example of a traditional data collection process, Spickard (2017) stated that data is collected in 6 stages while conducting a scientific study. The steps followed in the data collection process are defined separately for the traditional data collection process and individually for the online data collection processes. These steps are: 1. 2. 3. 4. 5.

Developing the research question Choosing the structure for the research design Identifying the data type that is needed Selecting a data collection method Selecting a website for data collection 133

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6. Choosing a method for data analysis. On the other hand, online data collection stages require different processing steps than the steps given above. There are three ways to distribute surveys electronically (Evans & Mathur, 2005; Val Selm & Jankowski, 2006). The first is to link to web surveys. Linking is used to switch from one site to another or from one file to another. Links can realize these connections with text, pictures or any visual tool. As a second way, the created questionnaire can be sent by embedding it into the email message or attached to the web address. Thirdly, distribution can be made by sharing the survey address on the website and social media via an announcement. Before collecting online data, the characteristics of the sample group members (sample size, access, age level, socio-cultural and economic opportunities, Etc.) should be analyzed and evaluated together with the advantages and limitations of these tools that will be used. Then, the research can be carried out by determining all the stakeholders involved in the application and its features. Granello and Wheaton (2004) stated that the following steps should be followed for data collection with online tools. 1. Determining the sample group that will attend to the research: It is expected that all individuals in the sample will have internet access. Besides, panel access offered via the Internet should be clear, simple, and easy to use for all participants. Furthermore, the interfaces currently used and suitable for users in the application can be more functional because of being familiar to the users. 2. Determining whether the application will be made over the Web or via email: It is essential to consider both applications’ advantages and limitations before making this choice. For example, attention should be paid to email security in evaluating the institution of work, collecting psychosocial data. For the studies planned to be organized via the Web, online questionnaires should determine the webspace features required for data storage. In addition, the number of instant users and their suitability should be evaluated. 3. Determining the questionnaire format and question layout order according to the type of the questions: It will be appropriate to avoid the complex interaction features as in HTML in the questionnaires sent via email as much as possible. In this way, flatter and simpler designs can be transmitted to users without the limitation of supporting additional features by email providers and without distorting the initially designed layout. Also, data collection tools sent via email do not include interactive buttons, pop-up windows, and multiple-choice boxes. If anyone wants to use these features, a web-based scale tool should be chosen. Besides having more comprehensive features than those collected by email in Internet-based tools, it is also possible to categorize. Segmented scales will allow participants to fill the questionnaire more healthily, progressing gradually, instead of performing long tasks at once to fill out the questionnaire. Such applications can only be prepared with Web 2.0 tools running on the Web. 4. Writing scale questions/items: Writing a questionnaire question with paper and pencil shows similarities with many parameters, such as biases against the answer data, formal differences, and simple and understandable language. 5. Executing the format of the arrangements: When formatting the scales to be used, it is crucial to choose a legible font and translate all the same font and integrity. A good page design can positively affect legibility and navigational behavior. Besides, a well-organized courtesy page contributes to the healthy operation of the applications they use for disadvantaged screen reading.

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6. Sharing the research text and researcher information: Sharing information about the planned research and the participant’s approval after reading the report and starting to fill the scale will be ethically appropriate. Also, it is vital to share information entirely to reach the researcher in a problem encountered. 7. Determining how data is transferred to the computer environment: This issue is best decided by planning what type of data it will be. For example, quantitative scales applied via the Web can be retrieved from the server, where it is saved via a numerical table and calculation file. Simultaneously, qualitative data can be transferred from the server to the computer via text files. Finally, the data set can be entered into the database manually by the researcher in scales sent via email and whose data are expected to be returned in text form. 8. Pre-implementation of data entry work: This step’s essential purpose is to determine whether an error will occur in the database due to data entry. The researcher tries to fill in all the data entry boxes in the scale that he will apply via the Web. Also, it will be tried in which format the dataset file can be saved to the computer. This trial process will ensure that the data are recorded in the database, and if necessary, adjustments are made on the scale. 9. Adding anonymous “debug” variables to web-based scales: It is possible to encounter many scale application errors. This is because they are filling the leaves anonymously. The data collected from the sample group can be made available to the participant when an error is encountered. Data collection from anonymous participants can be prevented by including date, time and IP address data in the database. For example, when a participant realizes that it has been filled, it can be filled again, and thanks to the date and IP feature in the database, single data, information, data can be retrieved. Otherwise, his two data may be incorrectly included in the study. 10. Conducting a pilot study: It is recommended to execute a pilot study of the scale to smaller subgroups before applying it. This application phase is a trial application for the scale. The comprehensibility of the scale items will determine smaller subgroups whether there is a factor preventing participants from applying the scale. It will also allow the scales to be used over the Web to be affected by the internet connection or determine how they look through different browsers (Wyatt, 2000). 11. Scheduling a schedule for sending reminders and sending emails: Research indicates that respondents who were sent a questionnaire joined the application within a few days after receiving the notification. For this reason, it is vital to plan and execute the scale submission and reminder activities afterward within the framework of a work schedule. 12. Saving/downloading data periodically: It is vital to download regularly and save the data set collected through the scale. Downloading the dataset will prevent losses due to internet-related data loss or theft. (Granello & Wheaton, 2004).

Advantages and Limitations of Online Surveys With the increasing use of the Internet in daily life, the areas that are used are increasing. The decrease in the cost of hardware and software of computers and the expansion of internet access areas are the most important reasons for this increase. Individuals in the information society also prefer to use these technologies, which are increasingly used, to obtain information first (Nie, Hillygus & Erbring, 2002).

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Along with acquiring knowledge determined smaller worldwide, the transfer of communication to these technology channels has brought the opportunity to reach individuals in mass. Most of the research in social sciences is based on the principle of collecting data from communities. These technologies also directly facilitate the data collection processes of scientific research. It will be much easier and more possible to collect data from large groups of participants over the Internet. However, data collection via the Internet has many limitations as well as its predicted advantages. These two sub-dimensions are explained in detail in the headings below.

Advantages of OS Online surveys have many advantages. When these are examined in detail, it can be said that most of them are parallel to the benefits that the Internet and technology provide to human life. The advantages of OS are: 1. Access to a Large Group of Participants / Global Reach: The high number of internet users worldwide allows an online survey to reach a broad audience. 2. Time / Speed/ Timeliness: Online surveys are powerful in scheduling, and they minimize the time of scale practices on a large group of audiences. 3. Cost: Survey costs can be divided into two categories as preparation phase costs and post-implementation costs. Thanks to the advanced survey software, preparation costs before the survey application are very low. After the implementation, there is no need for any additional equipment and personnel because of recording the data automatically into the database. 4. Ease of Collecting Data: Participants can easily apply the scale without the need for a tool other than electronic media. 5. Ease of Follow-up: Since sending emails over the Internet is simple and the cost is negligible, the process of sending reminders for online surveys can be followed at a low cost. 6. Ease of Data Entry: The data obtained from the participants in online surveys are automatically registered directly in the database. There is no need for an additional process between answering the questionnaire and entering the data into the database. 7. Ease of Data Analysis: Data obtained from online surveys can be analyzed instantly by most interfaces with simple classical methods. Databases in a suitable format can also be downloaded from the site for additional and comprehensive analysis. 8. Flexibility: Online surveys are very flexible in collecting data in the desired format (embedded, linked by emails, announcement attachments, Etc.). Also, a survey can be adapted with more than one version and can be applied as a single survey on groups with different demographic characteristics. 9. Simplicity: Many online survey applications can be used without technical knowledge. These types of applications also have agents that explain the survey creation process step-by-step. Furthermore, thanks to user-friendly interfaces, ready-made survey templates can be used, and individualization of features according to user preference can be carried out. 10. Accessibility: Online scales are created on a web address. If anyone who created this address shares it with participants, they can access the scale. Therefore, even individuals in places that cannot be physically visited can access the scale during the scale’s application. 11. The lack of prejudice: It is not expected that participants show any previous biases, thanks to seeing the scale items for the first time and all together. 136

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12. Security: Online scale application systems have a blocking technology for the multiple answers of the same participant with using IP control (Brennan, Rae & Parackal, 1999; Medlin, Roy & Ham Chai, 1999; Mann & Stewart, 2000; Taylor, 2000; Yun & Trumbo, 2000; Wright, 2000; Llieva, Baron & Healey, 2002; Garton, Haythornthwaite & Wellman, 2003; Wilson and Laskey 2003; Muhtaseb, 2004; Evans & Mathur, 2005; Van Selm & Jankowski, 2006; Sue & Ritter, 2012; Terry & Braun, 2017).

Limitations of OS As mentioned above, online surveys have many advantages. However, there are some limitations for researchers considering online surveys. In this section, online scale application limitations are discussed rather than the traditional scale application process. Here are the name and details of the limitations: 1. Technological Conditions: Computer and internet hardware infrastructure working seamlessly is essential for online surveys’ healthy use. Internet speed may vary depending on the hardware from which internet service is purchased. The effect of speed on usage situations may also be reflected in the survey applications. A similar problem is also valid for the tool’s hardware used for Internet usage (computer, mobile phone, tablet, etc.). 2. Access Problems: The act of sending surveys with email addresses can be perceived as an unauthorized and confidentiality violation for some users. Although it seems more appropriate to share the survey address on social media for optional participants, it may not be accepted in chat rooms or discussion platforms similar to the previous situation. In this case, the platform manager is responsible for removing a highly responsive survey address without asking the shareowner. 3. Sampling / Limited Populations: The comfort of spreading online survey addresses to all individuals may create a problem of reaching individuals other than those with demographic characteristics planned to be included in the sample. Also, in the questionnaires offered via web addresses and preferred by volunteer participants, the problem may arise that the research findings cannot be disseminated to the general public due to the possibility of not heterogeneous sample characteristics. 4. Perception as Junk Mail / Gray-and Blacklisting: Online scales can be defined as unsafe in the servers’ security settings because they share a link in the emails. Furthermore, emails with this type of content will be gray-blacklisted by the providers. Messages received from these email addresses added to this list do not reach users in any way. 5. The boredom of Email Surveys: Many researchers prefer creating surveys via online platforms because of their ease of access, use and cheapness. For this reason, online surveys can be sent to the same individuals many times without realizing it. Since this situation will cause boredom with the participants’ survey application, they do not prefer to apply for the survey. 6. Survey Length: One limitation is that participants get bored with answering the questionnaire and leave the page before completing the survey. That is why it is essential to include brief and fun elements in the surveys whenever possible. 7. Concerns About Security and Privacy: Scale participants may be concerned about the security of the information they declare. These concerns may be related to whether the information they indicate reaches the correct source and whether it will be kept confidential. In this case, scale practitioners are expected to submit a signed confidentiality statement.

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8. Limited Information About Respondents: Most of the participants’ demographic information can be obtained from website registrations. For this reason, asking the data to be brought in the survey will extend the survey, and as in the previous factor, it will cause the participants to get bored. 9. Personal Conditions: Participants may not have the necessary motivation to administer the questionnaire. This situation may be due to personal well-being as well as factors related to the questionnaire. 10. Lack of Online Experience/Expertise: Although the internet usage rate is high globally, the participants may not master the interface used for the survey application. This situation may cause difficulties for the participants in the application of the questionnaire. 11. Incomplete Instructions: The instructions must be clear and understandable. Online questionnaires are based on individual participants’ practices and cannot directly ask the practitioner questions on a topic they do not understand. The participants can abandon surveys without clear and understandable instructions before they are completed (Miller, 2001; Andrews, Nonnecke & Preece, 2003; Bannan, 2003; Greenspan, 2003; Ray ve Tabor, 2003; Wilson ve Laskey, 2003; Berry, 2004; Evans & Mathur, 2005; Van Selm & Jankowski, 2006; Terry & Braun, 2017).

Factors That Affecting the Participants’ Response Behaviors Towards Online Survey Many factors affect the participation of people in online surveys. Groves et al. (1992) classified these factors into three categories; 1. societal-level behaviors, 2. characteristics of the sample and 3. attributes of the survey design. Keusch (2013) investigated the validity of this classification by analyzing the literature in detail and defining the factors that may influence people’s behaviors when taking an online survey. Based on Keusch (2013)’s results, many factors influence the response rates of the participants. These factors can be classified as societal factors, sample characteristics, survey design attributes, reminders and questionnaire design. The factors and the components of these factors are presented in detail below. Societal factors may be listed as: • •

Survey Fatigue: more online survey invitations lead to lower participation (Porter et al., 2004), Culture: collectivism affect the response rate to the online survey positively (Petrova et al., 2007),

Sample Characteristics: • • •

•

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Gender: females are more inclined to participate than males (Busby and Yoshida (2013), Dykema et al. (2012), Laguilles et al. (2011), Marcus and Schütz (2005), Patrick et al. (2013), Porter and Whitcomb (2005)) Race/ ethnicity: It was found that black people have less tendency to participate than non-blacks (Patrick et al., 2013) Personality: Web-survey participants have been found as more socially engaged, open to experience, curious, agreeable, less artistic, and conscientiousness (Brüggen and Dholakia, 2010; Fang al., 2009; Marcus and Schütz, 2005; Petrova et al.,2007, Porter and Whitcomb, 2005a, Stingelbauer et al., 2011). Personal topic interest: The people’s interests have been found to affect the participation in the OS (Keusch, 2013, Marcus et al., 2007, McCambridge et al., 2011, Porter and Whitcomb, 2005b).

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• •

Attitude towards survey research: It was determined that the ones who have positive attitudes towards survey research might lead to higher Web survey participation (Petrova et al.,2007). Previous participation survey research: The persons have consistent participation behaviors across different research settings (Keusch, 2013; Petrova et al.,2007).

Survey Design Attributes Contact: In this category, there are four dimensions of contact that may influence the behavior of the participants towards online surveys; contact mode, pre-notification, the timing of invitations and sender. (Petrova et al.,2007) • • • •

Contact mode: The results on this issue have been defined as mixed. While the invitations with emails reached more people in some research, the invitations with mail achieved more participants. Pre-notification: Again, mixed findings were determined for the usage of pre notifications. Timing of invitations: invitations sent on weekends are not responded to immediately, winter is the best season for the OS, and Wednesday is the best day of the week to send the invitations. Sender/sponsor: Mixed findings were found, the relationship between the sender and the receiver. One of the findings was that female sender enhances the participation in male populations. Trust in sponsors, the familiarity of sponsors are also effective in the involvement of the people.

Subject-line: While appeals for help receive more returns, references to surveys and promoting incentives negatively affect the response rate (Patrick et al., 2013. Invitation message: There are again mixed findings on the usage of invitation messages. It was found that length of the messages and stating the deadline have both negative and positive effects; typing a password reduces the participation rate (Patrick et al., 2013). Reminder: Sending email reminders may increase the response rate; however, the reminders reach an early saturation point. After that point, the postal reminders do not enhance the response rate in the OS (Patrick et al., 2013). Incentives: Mixed findings have been detected for incentives. While unconditional incentives enhance the response rate, monetary incentives do not affect the response rate. Also, offering the results of the research as an incentive has no impact on the response rate. Questionnaire design: There are three categories under the questionnaire design that affect the participants’ behaviors in the online surveying (Keusch, 2013, Marcus et al., 2007. • • • •

Paging vs. scrolling: no difference was determined between paging and scrolling. Progress indicator: It was found that static progress indicators have no or negative influence on the survey completion. Questionnaire Layout: the features of the layout were not found effective on the response rates. Questionnaire Length: The relationship between the actual length and the announced length was found negatively correlated.

Many factors may affect the rate of participation in the OS. Undoubtedly, one of the most important factors in the sample characteristics is the age of the participants. The sample’s features have been investigated in many studies to find the best sample for the study. It has been stated that personal character139

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istics, interests, culture, attitude and gender affect the response behaviors of the people. However, there is a limited amount of information about the age of the participants. There is no study investigating the impact of the participants’ ages on the OS to our best knowledge. Although it is a well-known fact that young people can use the Internet more frequently and effectively than older one, no research aiming to find out the impacts of the ages of the response rate has been discovered. In line with this gap in the literature, within the concept of this study, the adolescents’ behaviors and the factors that may affect their response rates were analyzed. Thanks to the study’s findings, it is expected to determine the factors that may influence the adolescent’s response rates to the OS. Adolescents are essential OS participants because they tend to spend much more time on the Internet than the other age groups. Hence making the OS more attractive to adolescents may lead to getting more valid and reliable data by means of the OS.

METHODOLOGY Research Design A cross-sectional quantitative research method was utilized to analyze the factors that influence adolescents’ online survey response behaviors. In a cross-sectional research approach, the study data are collected from the participants at a single point in a relatively short time period (Johnson & Christensen, 2017). Hence, the researchers may not aim to measure directly changes that come over time in this study but instead will use descriptive statistics (percentage, average mean) and inferential statistics (chi-square and correlations) to report the students’ attitudes’ to online surveying and to detect the factors that may affect the students’ response rates. The factors investigated in this study are Email Checking habits, Attitude Toward Research, Interest, Rewards, Length of Survey, Value Privacy, Survey Structure, Reminder, Pre-notification, Survey Time Received.

Sample This study was primarily aimed to identify the factors that may hinder the adolescents into OS; hence the data gathering or conducting an empirical study was not the focus of this work. Only a small group of adolescents were reached to test the factors detected in the related literature. Since we had no purpose to generalize the findings of the study, a convenient sample method was preferred in parallel with the study’s main purpose, and the participants were acknowledged about the purpose of the research, and their consents were provided. The sample was composed of 215 adolescents. They were freshmen at a public university in Turkey. Most of them were studying at the educational faculty (f=215, 55%); 30% of the participants were faculty of engineering (f=64), and the rest of the participants were from the other departments of the university. The age of the study group ranged between 17 to 19; hence they could be accepted as adolescents. As for the gender of the participants, 60% (f=130) percent of the participants were females, and 40% (f=85) were males. This information about the participants were collected with demographic questions located at the beginning of the questionnaire. In addition to these basic questions, the participants were also asked about the cities in which they were living with their parents. Based on the answers of this question, it was found that 48% percent of the participants were from the eastern parts of Turkey. The middle east of Turkey was found as the second most popular(20%) region for the residencies of the participants. The mediterrian region was found the last region for the residencies of 140

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the participants. Based on these results, it was found that the sample of the study was composed from different cultural backgrounds. However, due to mobility changes of Turkey, the cultural background information about the participants was not used within the context of the study.

Data Collection Tool Data of the study was gathered via survey and a questionnaire measuring the factors influencing the response rates of adolescents towards OS was used. The questionnaire was prepared based on the items and dimensions of the survey of Saleh and Bista (2017). The original survey is composed of 10 dimensions with 28 items. Firstly, we translated the items into Turkish from English, which was the original language of the questionnaire. We excluded a dimension of the questionnaire that was about pre-notification which included expressions especially for workers rather than the students. We also changed many expressions to make it more understandable in Turkish. Upon the many changes on the items, our survey was composed of nine dimensions and 26 items with a 5-points Likert scale from “strongly disagree “to “strongly agree.” The dimensions were composed based on the factors affecting the online survey response rates of the participants. The related research was also taken into consideration at the determination phase of the survey’s dimensions. The dimensions included in the questionnaire are email checking habits, attitude toward research, interest, rewards, length of the survey, value privacy, survey structure, reminder, survey time received which were mainly determined by existing literature (Dillman, 2007; Fan & Yan, 2010). At the beginning of the survey administration, the participants were asked several demographic questions about their age, gender, educational status,and residency in order to describe the sample in detail.

Data Collection In line with the purpose of this chapter, the survey was applied in terms of an online channel. Firstly, the final form of the survey was transferred into a web-based survey platform called FreeOnlineSurveys. The participants were invited to the survey with an email that gave detailed information about the purpose of the study and brief information about the survey. They were assured about anonymity; hence no identity information was asked. This precaution was administered in order to reach more valid answer patterns. Throughout three weeks, they were able to access the survey, and after the 20th day, the online surveying was finished. They were not promised any incentives, or no reminders were used to prevent the possible intervention to their attitudes toward the online surveying, which was the central theme of the surveInstead, the researchers exerted to provide an online survey context to the participants as they have faced usually. Hence no manipulations were operated at the phase of data collection.

Data Analysis The data collected with the questionnaire was analyzed at descriptive level. Only the rates of the answers were calculated. No further analysis aiming to investigate the differences among the subgroups were conducted because the subgroups were not distributed in a balanced way. Also the total score can not be calculated from the questionnaire due to the structure of the instrument. Thus, in line with the main purpose of this research, descriptive statistics were estimated at item and dimension level and the ideas of the participants were discovered. 141

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Findings As aforementioned, the purpose of the data collection within the concept of this study was to overview the determining factors affecting the response rates in online surveying. Hence no inferential statistics were calculated; only the descriptives were estimated based on the participants’ preferences. Therefore, the research questions which were guided this study were that: • •

To what extent do the factors such as email checking habits, attitude towards the research, rewards, length of the survey, the survey structure, assurance of privacy, and frequency of reminder affect survey response rates? Which of these factors affect most of the response rates in online surveying?

To reveal the effects of these factors on the participants’ behaviors to online surveying, the response percentage and frequencies of each item were calculated, and these values were given in Table 1. In Table 1, the participants’ perceptions of possible behaviors towards online surveying were presented in percentages and frequencies. These statistics should be analyzed to get an idea about the factors that hinder or motivate the participants to online surveys. The participants’ answers were analyzed based on the factors, and the first factor was email checking habits. Most of the participants stated that they were more inclined to open an email from an organization they belong to (86,3%). Also, half of the participants stated that they open emails from a non-profit organization. The second item of this part seemed confusing because while 35% of the participants stated that they open emails from the people they know, more participants, nearly 43% of them, stated they do not open these emails. The reason for this preference should be investigated with open-ended questions to get the gist of these response rates. The second factor that was analyzed in the study was the interest of the participants in online surveying. Three statements were presented within this factor, and for all of them, it was found that most of the participants were more inclined to complete the online survey because they have an interest in the topic of the survey. Also, most of them were found willing to learn their results, and this kind of promise makes them more motivated to complete the survey. As for the rewards, two statements were analyzed, and it was found that the participants become more inclined to complete the survey if they are promised to get a reward within the invitation. However, most of the participants behaved neutrally towards getting monetary rewards. Hence it can be concluded that financial rewards do not affect the response rates of the participants at a high level. The other factor was related to the length of the survey, and the results showed that most of the participants are more motivated to answer the shorter survey, which takes less than 15 minutes. Hence, the researchers should consider this issue and utilize the shorter form of the surveys, especially the online methods. Within the factors of value privacy, it is so clear that participants appreciate anonymity. They become more willing to complete the survey when their identity information is promised to be hidden. The survey structure has more items than the other factors investigated within the concept of this study. It was found that the strongest motivator of the participants in the online surveying şs the items being concise and short. Also, they want to get information about the survey content within the email, and the professional-looking invitation is also important to make them more inclined to complete the survey. Sending reminders was also investigated, and most of them stated that they would be more inclined to answer the survey if they got a reminder. Besides, as is expected, most of the participants found more than three reminders irritable. 142

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Table 1. The responses of the participants to the survey Category

Email Checking Habit

Interest

Rewards

Length of Survey

Value Privacy

Survey Structure

Reminder

Pre-notification

Survey Time Received

Item

Agree

Disagree

Neutral

I open all my emails

39.4%

31.2%

29.4%

I only open emails from people I know

35.4%

42.2%

22.2%

I am more likely to open an email from an organization I belong to

86.3%

10.4%

3.1%

I am more likely to open an email from a non-profit organization

49.8%

29.6%

30.4%

I am more inclined to complete the survey if I have a vested interest in the topic

83.8%

9.6%

6.3%

I am more inclined to complete the survey if the author promised to share the results with me

69.2%

11.1%

19.6%

I am more likely to fill out academic (educational) surveys

73.4%

9.6%

6.3%

I am more inclined to complete the survey if promised a monetary reward

37.8%

14.4%

47.8%

I am more inclined to complete the survey if I received a reward with the survey invitation

47.4%

36.7%

15.9%

I am more inclined to complete the survey if I know how long it will take to fill out beforehand

67.8%

15.6%

16.7%

I am more inclined to complete the survey if it takes less than 73.7% 15 minutes

13.3%

13%

I am more inclined to answer the survey if I am assured of anonymity

89.3%

7.4%

3.3%

I am more inclined to complete the survey if I am assured that my answers will remain confidential

84.1%

6.7%

9.3%

I am more likely to open an email with subjects clearly indicating the research nature of the content

76.3%

4.8%

18.9%

I am more likely to fill out the survey if the email invitation looked professional

71.1%

7.8%

21.1%

I am more likely to complete the survey if the questions items 88.6% are short and concise

3.3%

8.1%

I am less likely to finish the survey if it included open-ended questions.

55.2%

20%

10%

I am more likely to fill out the survey if the email invitation includes my name

58.5%

18.5%

23%

I am more likely to complete the survey if I receive a reminder

69.6%

8.9%

4.8%

I get bothered if I receive more than three reminders from the 61.5% researchers.

17.8%

20.7%

I am more likely to open the email if I received a pre-notification

%

f

a) By email

91.8%

370

b) By mail

8.2%

33

I am more inclined to complete the survey if I received the email.

%

f

a) At beginning of the day

26.6%

72

b) During my lunch/break time

29.2%

79

c) At the end of the day

13.7%

37

d) In the evening

34.3%

93

I am less likely to answer the survey

%

f

a) During the summer months

20.3%

55

b) During the holidays

58.3%

112

c) At the beginning of the school year

10.3%

28

d) Toward the end of the school year

18.1%

49

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As for using pre-notification, it was found that using pre-notification by emails would increase the response rates in online surveying. Although nearly all participants preferred to get notifications with emails, only 8% of them chose to get pre-notification with mails. The last factor analyzed in the study was about the time of the survey, and various preferences were determined in this issue. Most of the participants (34.3%) stated that they become more inclined to answer the survey if they receive the email in the evening. A similar number of participants preferred to get an email at the beginning of the day and lunchtime. However, fewer participants stated that the best time for receiving email was at the end of the day. Also, more than half of the participants stated that they would be less likely to answer the survey if they received the emails during the holiday and summer months. The school time, beginning or the end of it did not make significant changes in the participants’ preferences.

CONCLUSION The rapid development in technology makes people more dependent on technological devices and the internet day by day. Especially in the process of COVID-19 pandemic, everyone has gained much more familiarity in online practices. In order to suppress the prevalence of the COVID-19, face to face interaction among the people has been minimized in all of the fields of human lives . These conditions have been valid for social research, too. Researchers have been obliged to gather data with the means of online channels.Hence most of the research has been conducted by online surveying and this method has gained much more importance. With these obligatory changes all over the world, scientific research has started to be administered using internet facilities, especially data collection methods, which have adapted these changes more radically than the other parts of the research process. Besides the feasibility and facilities provided by online data collection tools, some crucial issues should be considered by using these methods. One of them is investigating the factors that may affect the response rates and the validity and reliability of online surveying. Within the concept of this study, online surveying was investigated, and the factors that affect and even prevent the participants from tending online surveying were analyzed. The primary purpose of the research was to cover the OS in detail and especşally to inform the researchers the factors that may affect the response rates of the adolescents in the OS. In line with this purpose, the related literature was analyzed and the factors were discovered by using many studies conducted in this field of research. In addition to the bulk of theoretical research, data was collected from a small sample of adolescents about the factors affecting their response behaviors towards the OS. These results revealed many factors stated in the related literature affecting the response rate of online surveying. These factors will not be repeated in this section again, but the researchers must consider these factors to get valid and reliable data in terms of online surveying. Also in the following research, modelling studies may be conducted in order to find out the predictive factors influencing the response rates of the adolescents towards the OS. Moreover, with a qualitative study much more detailed knowledge may be obtained about the perspectives of the adolescents toward the OS.

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ACKNOWLEDGMENT This research received no specific grant from any funding agency in the public, commercial, or not-forprofit sectors.

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ADDITIONAL READING Aerny‐Perreten, N., Domínguez‐Berjón, M. F., Esteban‐Vasallo, M. D., & García‐Riolobos, C. (2015). Participation and factors associated with late or non‐response to an online survey in primary care. Journal of Evaluation in Clinical Practice, 21(4), 688–693. doi:10.1111/jep.12367 PMID:25929295

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Haladyna, T. M., Downing, S. M., & Rodriguez, M. C. (2002). A review of multiple-choice item-writing guidelines for classroom assessment. Applied Measurement in Education, 15(3), 309–333. doi:10.1207/ S15324818AME1503_5 Huang, H. M., & Liaw, S. S. (2005). Exploring users’ attitudes and intentions toward the web as a survey tool. Computers in Human Behavior, 21(5), 729–743. doi:10.1016/j.chb.2004.02.020 Keusch, F. (2012). How to increase response rates in list-based web survey samples. Social Science Computer Review, 30(3), 380–388. doi:10.1177/0894439311409709 Nair, C. S., & Adams, P. (2009). Survey platform: A factor influencing online survey delivery and response rate. Quality in Higher Education, 15(3), 291–296. doi:10.1080/13538320903399091 Pedersen, M. J., & Nielsen, C. V. (2016). Improving survey response rates in online panels: Effects of low-cost incentives and cost-free text appeal interventions. Social Science Computer Review, 34(2), 229–243. doi:10.1177/0894439314563916 Saleh, A., & Bista, K. (2017). Examining factors impacting online survey response rates in educational research: Perceptions of graduate students. Online Submission, 13(2), 63–74. Wright, B., & Schwager, P. H. (2008). Online survey research: Can response factors be improved? Journal of Internet Commerce, 7(2), 253–269. doi:10.1080/15332860802067730

KEY TERMS AND DEFINITIONS Likert Scale Questions: Is about the participants’ level of acceptance of the statement. Likert type scales can be organized as five-point Likert to ten point. OS: Online surveys which are accessed and used for data collection from electronically forms located on websites.

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What Happens When the Way to “Follow the Medium” Changes? A Reflection About Emerging Perspectives in the Post-API Era Suania Acampa University of Naples Federico II, Italy Giuseppe Michele Padricelli University of Naples Federico II, Italy Rosa Sorrentino University of Naples Federico II, Italy

ABSTRACT Digital methods allow social researchers and IT professionals to work together to produce instruments to comprehend current social phenomena. To develop these tools, they felt the need to “follow the medium” by reorganizing their data collection and analysis strategies on what they learned from the medium. For many years, digital research has been based on application programming interfaces (APIs) querying, an approach based on the extraction of records of data made available by the platforms through their programming interfaces. But what happens when the way to “follow the medium” changes? This contribution addresses the methodological challenges and the potential alternatives in research activities that affect the researchers’ role due to recent restrictions. Two examples of research experience conducted before the APIs’ closure are proposed in order to lead towards an initial reflection on its critical effects.

INTRODUCTION Re-Imagining Social Research: The New Scenario The role assumed by digital technologies in the last 30 years has driven social science to a scenario where DOI: 10.4018/978-1-7998-8473-6.ch010

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 What Happens When the Way to “Follow the Medium” Changes?

«Web-mediated research [...] is already transforming the way in which researchers practice traditional research methods transposed on the Web» (Amaturo & Punziano, 2016: 35-36). The new methods, which emerged with the advent of CMC (computer mediated communication), in the 1990s after adapting traditional approaches to cyberspace logic revolutionized techniques and research actions, reaching a paradigmatic way that permits researchers today to «observe and study social phenomena in digital context, taking account of the web not only as object of study, but as well the role they play in relation with it» (Rogers, 2013: 14). The prolific field of study of Digital methods, in fact, allows social researchers and IT professionals to work together to produce instruments that are useful for gathering organized data that can give back the current description of a social phenomenon its diachronic changes as well. The increase of social media platforms in the last ten years and the chance to collect large datasets through the Application Programming Interface (APIs), has driven this field of study to define its instruments and tools for the study of digital native data provided by digital platforms. Starting from this background, what happens when the way to “Follow the medium” (Rogers, 2009) changes? The APIcalypse (Bruns, 2019) that followed the Cambridge Analytica case in 2018 inevitably led us to reflect on the instability of digital data sources and the dangers of anchoring research to a deterministic perspective with a technological inclination. Starting from the closure of APIs, this contribution aims to understand and describe the potential of new research methods in facing obstacles and limitations that could emerge during the research phases. The Cambridge Analytica scandal brought up several ethical questions about users’ privacy, improper data use and social network’s sharing practices (ibid.), to which Facebook immediately responded with a tightening of terms of service (TOS) on public APIs services, reducing the functionality on Pages, Groups and Events in April 2018. Finally, in the summer of 2019, the platform definitively declared closed access to any data download, enabling third part tools. This was a critical decision for digital research and its addiction to the study of social phenomena, ranging from political mobilization to cultural consumption. The versatility offered by the Facebook API was precious, indeed, because it helped researchers to constantly move between quantitative and qualitative moments of the analysis (Venturini, & Rogers, 2019). In fact, digital methods reduced the gap between rich but scarce qualitative data and large but raw quantitative data, making it possible to study collective dynamics not excluding individual interactions (Venturini, Jensen, & Latour, 2015). The current restrictions inevitably have acted on the way academics can do research, imposing limits not only on the amount of data and the range of time that can be analysed (consequently penalizing longitudinal research), but also on possible methods and topics that can be covered without violating social networks’ TOS. So, in light of APIs’ closure, researchers who continue doing research through social networks are forced to look for alternative paths, not only by changing their tools, but also the way of viewing and thinking about the research itself. With the matter of data access becoming a problem, questions related to the alternative practices for data collection are posed. Were digital methods based only on the APIs’ interrogation techniques? Which are the main problematic effects for doing online social research emerging from the APIs restriction? The re-enactment of the closure event and the focused literature review offers help in replying to these questions in order to understand how the repertory of the online research actions and techniques for alternative research paths is composed. The description of two research experiences carried out in 2016 and 2019 will help reflect on alternative ways (already working before the APIs shutdown) to study social phenomena in the digital environment. The open conclusion 150

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of this contribution aims to lead to s further reflections on its empowerment and then discuss, through an epistemological perspective, the new horizons in carrying out social research online.

DIGITAL DATA AND THE ISSUE OF FRAGMENTATION A large variety of social contexts are now digital: from relationships, to work, to consumption, to information, to local transport. This has generated changes and evolutions and sociology is being called upon to respond to these implications by developing its theoretical, epistemological and methodological baggage in order to understand the forms of interaction that develop in a society where the contrast between online and offline no longer exists (Marres, 2012; 2017). To observe and study society on the net, it is possible to use “natively digital” data, produced and collected directly in digital environments and which require natively digital research tools and methods to be analysed. To develop these tools, social researchers felt the need to “follow the medium” (Rogers, 2009) by reorganizing their data collection and analysis strategies based on what they learned from the medium (Caliandro & Gandini, 2019). The expression coined by Richard Rogers contains the founding principle of Digital Methods: the objective is to make the most of natively digital data, making the medium not only an object of analysis but also a source of methods. The digital age, “newly coordinated, open, processual, non-linear and constantly on the move” (Adkins & Lury, 2009), has meant that social research must face the need to re-invent itself. With the birth of web 2.0, based on interaction, user generated contexts, prosumerism and characterized by the spread of Internet, social media, algorithms and big data, social sciences open to a paradigmatic change or digital turn (Amaturo & Punziano, 2016). In fact, new technologies are not only an innovative object of study, but also an environment that imposes specific logics and regulations on social acts, and in which it is possible to observe and analyse social relations, practices and phenomena (Lupton, 2015). Following Caliandro & Gandini (2019), Digital sociology is the device through which the international sociological community «has begun to think about the criteria, modalities and approaches for studying social relations and cultural practices in the digital society» (p.18), in order to observe old and new phenomena in an original and innovative way. This approach leads to the development of new research methods, tools and strategies, aimed at analysing the large amount of data generated in digital contexts; and, at the same time, to the construction of a new theoretical architecture, capable of evolving the sociologist’s wealth of knowledge by focusing on the study of digital as a social “place” in which it is possible to monitor, measure and analyse society itself (Marres, 2017). These transformations act in a transversal way on the fields, times, places and ways in which social research can be carried out: thanks to the wide availability of online databases, virtual libraries and platforms for accessing specialized magazines and e-books, space-time barriers that prevented access to knowledge have been broken down. This democratizing process has acted on academic professional practice making work more flexible and smarter, giving the opportunity to operate even remotely, build networks with colleagues from all over the world and share ideas, interests and research through blogs and social media. The social sciences have been slower than other disciplines in welcoming these innovations (Daniels & Feagin, 2011), and there have been (and still are today) attitudes of reluctance and technophobia, justified by concerns of an epistemological nature. In a positivist perspective, reorganizing empirical concepts and developing computational methods, technical innovation would lead to a new “golden age” 151

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of social research (Lury & Wakeford, 2012; Marres, 2012). For pessimists, instead, innovation is a serious threat to the established forms of traditional sociology, which risks losing sight of the “human element”. According to Marres (2017), apart from positive/pessimist perspectives, digitalization would be acting on sociological practice by implementing a redistribution (or rather, a fragmentation) of digital social research which affects the methods, work practices, tools, skills, professionals and users involved. Regarding methodological redistribution, Marres identifies four different “degrees”: at the two extremes of the continuum, it is possible to find the methods-as-usual (the traditional methods of sociological research, which reject the possibility of any digital innovation) and big methods (which, on the contrary, see in digital data greater reliability and objectivity). In the middle are virtual methods (Hine, 2002), the transposition of qualitative social research methods into online digital environments e.g., the survey becomes a web survey, the interview becomes a web interview etc.; and digital methods, which proposes the idea that social research should benefit from the analytical and empirical skills embedded in online media. This hybridization process makes it possible to study the ontological object of the web from the device perspective taking account of the web and, simultaneously, the role the researcher plays in it. Each of these represents a dynamic “methodological space”, a set of platforms, devices, data, settings and actors where to explore different approaches to sociological analysis of the web, also configuring new forms of participatory research. In fact, social research’s digital transformation could even possibly lead to renegotiating division of labour and defining new professional profiles. Manovich (2011) argues that “big data society” is leading to a fragmentation of work and expertise between disciplines, academics, users, business, governments and industry This is also caused by limited access to data, skills deficit and ill-equipment. In this regard following Lupton (2015), one of the first researchers to talk about digital sociology, suggests the need to rethink social research in a creative way, integrating different tools, disciplines and approaches in order to develop a “living sociology”, a dynamic social science in contrast to the “dead” and “zombie” one. Rather than focusing on fragmentation, Lupton emphasizes the need for integrated forms of research, supported by a critical reflexivity, which could allow researchers to rethink their relationship with digital tools, and therefore to consider issues (possibility, depth, ethical dimension, etc.) and essential implications for the progress of social research tout court. Considering this background, the interest of researchers regards what features of social phenomena to focus on in an investigation through the analysis of digital data (Natale & Airoldi 2017: 11-18). The research designs, set following the research questions, lead to four potential dimensions to develop any empirical investigation: media context, public opinion, digital behavior and users. The (new) media context, as in past decades, involves mainstream media framework and is a key aspect that the social researcher must address. The properties of the medium undoubtedly affect interplay practices in terms of sense production due to the expressions of content and different use of digital instruments of a shared repertory (e.g., different ways of engagement practices through tagging and mentions between Facebook and Twitter). In the more to more connection architectures, several technical, economic and communicative factors have supported the spread of information between users and groups, also thanks to the social media power of integration with other media. In this way social media has become a gold mine of opinions and attitudes. The large amount of content, expressed by users and groups through posting and interaction practices, has opened up an online perspective of study of public opinion. Through content analysis techniques, such as sentiment analysis, the entire range of computerassisted methods becomes useful for extracting the sentiment by textual unit of analysis and for focusing, in an aggregated way, the opinion trends on several issues.

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The study of digital practices approaching users as aggregate has become fundamental today. Because of privacy reasons, in fact, socio-demographic data are not always available or time friendly: for these reasons the ‘post-demographic approach’, in which the subjective component is studied in the aggregation of the actions it produces, concerns the study of traces left online. Now that the dimensions of what it is possible to study through the analysis of digital data have been established, a fundamental aspect to plan, prepare and do online social research regards the ways to access data.

THE IMPORTANCE OF APIs FOR SOCIAL RESEARCH For many years digital research has been based on the collection and analysis of semi-structured and unstructured data from platforms collected through APIs querying: an approach to computational social sciences and digital sociology based on the extraction of records of data made available by the platforms through their programming interfaces (Venturini & Rogers, 2019). Data collection was made relatively simple thanks to the collaboration between researchers and developers who know the potential offered by this technology for the knowledge of social phenomena transposed to the network. Many tools such as ElFriendo (Rogers, 2009), Netvizz (Rieder, 2013) and T-CAT (Borra & Rieder, 2014) were born and these allowed researchers to collect data directly from the platform, and archive and view them in the form of CSV or JSON files. The Cambridge Analytica scandal and a series of restrictions previously initiated to protect user privacy have led to the closure of the APIs. Free access to platform data is closed and regulated exclusively by the companies owning the platforms that allow access especially to sectors with which it is easier to monetize, such as large marketing and advertising companies (Perriam, Birkbak & Freeman, 2019). Why are APIs important? Thanks to the APIs, researchers can collect data on a large scale and can analyse databases related to significant events. Without APIs, web interfaces must be scraped: a laborious process that drastically limits the amount of information that can be collected and processed. Natively digital data offers research a series of interesting and unprecedented possibilities such as overcoming the spatial and temporal limit that allows for diachronic analyses. The digital ethnographic method permits researchers in fact, through (non) participant approaches, to observe behaviours, relationships and social practices: digital ethnography guarantees a level of truthfulness to the research not found in the data collection practices related to technique of classic interviews (Lupton, 2015) and portrays spontaneity values in natively digital data production (Natale & Airoldi, 2017; Rogers, 2009). These, in fact, are not transposed from other media sources (such as, for instance, digitalized data) but are collected, instead, following the medium directly connected to the social phenomena, in a way that enables the researchers to make a primary use of secondary data. Today there is no free way to extract content from platforms without violating their TOS: alternative possibilities, such as Facebook’s partnership with Social Science One, still do not allow supply of data to all. Blocking the Facebook API will thus widen the gap between “big data rich researchers”, who have access to proprietary data and work in the interest of the company they are affiliated with, and “big data poor” who work especially in the academic world (Boyd & Crawford, 2012). Banning access to data is turning platforms into real black boxes that are not subject to external supervision and criticism. The Cambridge Analytica scandal has created a worrying side effect for social research on the web: the first consequence is certainly the tendency of researchers to migrate towards 153

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“easy data” (to be collected), directing their research questions on websites or platforms which are not very restrictive in terms of user privacy, but which at the same time cannot be considered mass platforms like Facebook, Instagram, WhatsApp and therefore not always useful in investigating particular social phenomena. Twitter, for example, operates three well-documented public APIs in addition to its premium and enterprise offerings. Twitter’s relative accessibility leads it to be widely over-represented in social media search. Anyone who studies social platforms knows how distinctive the characteristics that differentiate Facebook from Twitter are, not only in terms of platform operation (maximum number of characters for posts, retweets, hashtags) but also in terms of type of users. “Twitter doesn’t represent all people” (Boyd & Crawford, 2012) but a very particular subset. While rare, there are still apps that have survived the APIs’ shutdown after a rigorous review process, but the restrictions they are forced into do not allow researchers to freely extract user information or personal activities retroactively unless they are contained in groups or public pages. This restriction is problematic for several reasons: Reason One: The impossibility of longitudinal study of social phenomena on the net exposes researchers to a real “dependence on the moment” which inevitably tends to strengthen paradigmatic approaches to data-driven research. Reason Two: Exclusive access to particular data sets weakens the engine of science: replicability. Non-reproducible research slows progress (Boyd & Crawford, 2012).

TWO RESEARCH EXPERIENCES TO COMPREHEND API’S ALTERNATIVE PATHS Taking into account the critical threats that can affect data collection practices due to the reshaping ways to access data, in the following paragraph we offer two examples of research experiences conducted before the API closure in order to suggest a first reflection about the critical effects of restrictions. Both report very interesting results related to all 4 dimensions (media context, PO, digital behaviour and users) and obtained following the medium through two different research paths. The first one, published by Rossella Rega, in 2016 «Twitter as a New Engagement Opportunity. Analysis of the Questions and Answers between the Italian Prime Minister and Citizens» is adopted here to comprehend the replicable value of a work originally developed following a Twitter API inquiry. The second, one published by Anna Sophie Kümpel in 2019 «Getting Tagged, Getting Involved with News? A Mixed-Methods Investigation of the Effects and Motives of News-Related Tagging Activities on Social Network Sites» is adopted here to understand how alternative paths for data collection can help longitudinal studies not limited to data driven approaches. The study conducted by Rossella Rega focuses on a live-tweeting event dating back to April 23, 2014, when the newly elected Italian Prime Minister Matteo Renzi held a Question & Answer event on Twitter, giving citizens the opportunity to ask questions directly to him through the social platform. This event, known as #matteorisponde, followed Renzi’s announcement of a comprehensive reform plan to change the Italian political, institutional and constitutional structure through economic, electoral and welfare measures. The interest in this initiative arose from the type of use that Renzi made of Twitter, usually used by political figures as a “top-down” or “one to many” communication tool: the Prime Minister, instead, (in the wake of what Obama had already done on a live TV event in 2009) proposed a “bottomup” communication opportunity to clarify his political actions without journalists’ intermediation. 154

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Rega used natively digital data extracted from Twitter, a media context chosen by Renzi himself, particularly widely used in the field of political communication. The aim was to highlight differences between the issues raised by citizens (dimension of public opinion) and those actually dealt with by the PM in the sixty minutes of live streaming (digital behavior); also, to try to understand whether the platform can encourage the engagement of the public sphere (dimension of users). The study has two objectives: (1) to highlight the issues that most attracted the citizens’ attention; (2) to analyze the questions to which the Prime Minister replied, with the aim of reporting the discrepancy between the citizens’ priorities and those established by the PM himself. Using the approach of digital methods, the researcher collected all the tweets containing the hashtag #matteorisponde created between April 15, 2014 (10:11 GMT) and May 5, 2014 (15:48 GMT) through a software that interfaced with the Twitter streaming API. The structured dataset, consisting of 15,797 tweets, then underwent a content analysis. After an extensive training phase, the author and another researcher began a manual analysis of tweets, excluding from the dataset the incomprehensible ones (written in a non-European language) or those generated by trending topic services. Subsequently, they carried out a manual classification of tweets through the users’ classes identified by Sara Bentivegna and Rita Marchetti (2014), adapted to the specificities of the research. In this way, they structured eight classes of users: citizens, associations/ groups, journalists, insiders, bloggers, political actors, news media, others/not classified. Tweets were also classified according to the frame (negative, positive or neutral), the approach or the format (pure questions, rhetorical questions, position stand, jokes, reporting) and the problems presented (divided into macro themes), in order to understand the tone, the type and topics of all posts containing the hashtag. Classifying the issues raised through the posts (education, social policies, public administration, developmental policies, job issues, economy/finance, politics, justice/security, internal policies, innovation, environment/land, civil rights, other)into macro themes, researchers were able to identify both the most active users’ class (“citizens”), the type of prevailing frame (“neutral”) and the main issues raised (unemployment, university funding, social inclusion, workers’ rights, tax reduction and meritocracy). At the same time, through the observation of the 39 answers provided by Renzi, they were able to highlight which topics (intentionally or not) he had dealt with and which he omitted. Conducted between 2014 and 2016, in the period of full development of digital methods, APIs and big data, this study focuses on Twitter as the platform selected by the Prime Minister himself to hold the event. Let’s assume for a moment that we want to replicate this research on the same dataset, carry out new analyses with different questions on the same dataset or simply want to implement the same research method on Twitter: we would be faced with the problem of not being able to reconstruct the same dataset of the author. In fact, the closure of APIs has obviously compromised this possibility: although Twitter still allows the collection of data both through its APIs and some still active apps, only samples of posts relating to the last seven days may be collected free of charge, preventing us from carrying out longitudinal analyses. In 2016 Renzi held a second #matteorisponde event, this time on Facebook. Assuming we want to replicate Rega’s research on this Facebook’s Q&A event, it would be hard to access to the social’s APIs, due to the scarce economic resources generally available to public universities’ researchers. This limit raises the issue of the accessibility of the data, now made available only to those who can bring economic benefits to the company, both through the purchase of data and the promotion of services such as advertising, sponsorship, and so on (companies, private universities, etc.)

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Wanting to estimate different alternatives of collection data, instead, these ones cannot be applied without taking into account the possible non-representativeness of the dataset; in the same way, as previously said, the use of web scraping techniques not only could cause legal problems of TOS violation, but also raises doubts of an ethical nature, as well as of representativeness related to the issue of algorithmic bias. Anna Sophie Kümpel’s research aims to study how distinct news curation practices influence users’ intention to consume encountered content, allowing her to deal with all four dimensions (media context, public opinion, digital behavior, users) described above. In this mixed-methods investigation, using Facebook as an example, the researchers first examine the results of an experiment showing that getting tagged in comments to news posts promotes news consumption the most. Based on this finding, the researchers then focus on actively tagging users by investigating news tagging motives/practices with interactive qualitative interviews centered on participants’ Facebook activity logs. The findings show how news tagging, albeit a strong catalyst for reading and interacting with news, mostly favors users already interested in news, thus challenging the optimistic assumption that the Social Network Sites might foster incidental learning among less interested audiences. Kümpel’s research project builds on an explanatory sequential mixed-methods design (Creswell, 2014). The two-phase design began with the collection and analysis of experimental data on news reading intentions (study 1, quantitative approach), followed by the subsequent collection and analysis of interview data on news-related tagging activities (study 2, qualitative approach). Study 1 was conducted in October 2017 and investigated how distinct Facebook news curation practices, differing in terms of personalization and perceived accessibility, influence users’ news reading intentions. To achieve this purpose, she conducted an online experiment in which German participants were exposed to a news post that supposedly reached them either because: 1) a news provider posted it (no social curation), 2) a friend shared it with their entire network (social curation, not personalized and accessible for the user’s friends), 3) a friend sent it to them in a DM (social curation; personalized and not accessible for the user’s friends), or 4) a friend tagged them in a comment to the post (social curation; personalized and accessible for the user’s friends). Study 2 was conducted in April 2018 and investigated the experiences of actively tagging Facebook users (“taggers”), focusing on the motives, routines, and social affordances of the practice. This second study was based on semi-structured qualitative interviews, complemented by an interactive, dialogic examination of the Facebook activity log of each participant. According to Facebook, this log “is a list of your posts and activity, from today back to the very beginning” and includes both active (i.e., tagging friends) and passive (i.e., getting tagged by friends) tagging activities. This allowed tailoring the questions to participants’ actual behaviors, relating them to individual experiences and insights. The experimental approach used by the researcher shows (even before the APIs’ closure), some possible alternatives to data access. In fact, through the use of mixed methods, Kümpel first manages to obtain data on the phenomenon in question; then —starting from these results— she uses interviews to better understand the phenomenon directly from the voices of the actors. However, interviews do not guarantee the same level of truthfulness to the research found in data collected when people do not know they are being investigated (Lupton, 2015). However, it is important to underline how an attempt in this direction was made by integrating the review of Facebook activities for each participant in the interviews. As in the previous research, even in this case the reproducibility and longitudinally are compromised. As we previously stated, digital research’s “dependence on the moment” (one of the most important consequence of API’s closure) makes it impossible to access the author’s original dataset. In fact, the 156

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actual restrictions prevent researchers from obtaining users’ information: currently data access services such as CrowdTangle allow you to monitor and analyze data on posts, but not on users; therefore, a search of this kind is still affected by the limits imposed by the platform in the study of the effects of the dynamics of the platform on users.

TWO DELICATE ASPECTS The enthusiasm for web research is linked to the perception that the web and platforms could offer easy access to huge amounts of data. Big data has generated a profound change in the way we think about research, not only from the point of view of scalability (Lazer et al., 2009) but also from the epistemological and ethical point of view: big social data reformulates the questions about knowledge constitution, research processes, and how we should interact with information and the categorization of reality. The research experiences previously proposed highlights of how the way of data access and data collection had changed due to the limitations of APIs. These contributions help us reflect about two delicate aspects that regard contemporary social research. First: our positioning as researchers. As discussed, accessing social media data is not easy. This difficulty produces a narrow and uncritical research culture towards platforms: a researcher with access to proprietary datasets is likely to choose research questions that do not raise disputes between him and the company he works for. But the troubling effects on research questions are just some of the effects that need to be considered when evaluating the future of big data management and future research agendas. Manovich (2011) points out that “only social media companies have access to truly great content, especially transactional data. An anthropologist who works for Facebook or a sociologist who works for Google will have access to data that the rest of the academic community will not”. This produces an irregularity in the type of research that those who have access to large data sets (generally large companies) can do and those who have access to small data sets (generally researchers from public universities) cannot do. This clarification is important because the data gap is also present between private universities with enough resources to acquire access, and the public ones. The result is that the divisions among scholars will widen significantly. According to Manovich (2011), Big Data divides people into three classes: those who create data, those who have the means to collect it and those who have the experience to analyse it. It is generally believed that, once the APIs are closed, the sociologist must necessarily equip himself with new computer skills (web scraping) in order not to give up the possibility of digital research and to relate to other actors and other social forces that can use the same data, threatening his privileged role as an expert in society. Starting from this “crisis of empirical sociology” (Savage & Burrows, 2007; 2009), Lupton (2015) proposes that the researcher think of innovative and creative ways of incorporating digital technologies into sociological practice to make them both an object and an instrument of research, testing new research paths capable of revisiting the more traditional methodological approaches in light of the opportunities and limitations offered by digital platforms. Back & Puwar (2012) advocate a “live sociology” in which the researcher can deal with “living data” in a creative way by experimenting with new critical and open ways of practicing sociology. Following the authors, the living methods include different levels and approaches, such as new tools for real-time investigation, functional to social research. In fact, following the medium also means becoming aware of its continuous change and considering these new limitations

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as a characteristic of the medium itself, which can open new research questions that can count on the flexibility and creativity of the social researcher. Second: direct practices of data collection, as well as the ones made through web scraping procedures (not related to API access), can be affected by the functioning of platforms based on the algorithmic personalization of contents. Direct data collection forces researchers to observe the dynamics on the net through the same interfaces as the actors they study: the alternative offered by scraping practices is problematic, not only from a legal point of view but also from a practical point of view. Algorithmic platforms, in fact, model the contents on the interests and preferences of users to be submitted. In this way the scraping procedures regards collection practices towards contents that the algorithm has modelled for that particular researcher / user who accesses the platform in the moment of collection. This makes it very different from the neutral selection of content provided by an API. While accepting the risk, the strict controls initiated by the proprietary companies most of the time do not allow for the success of scraping practices.

AN OPEN CONCLUSION This scenario encourages the deliberate violation of TOS: there is a growing interest in the academic community in this practice, many argue that in some circumstances the benefits to society of violating the terms of service outweigh the damage to the platform itself (Rogers, 2018; Venturini & Rogers, 2019), voluntarily ignoring numerous and delicate ethical questions to which the social researcher is called to respond. It is therefore clear that Facebook’s decision to limit free access to data to protect users after the Cambridge Analytica scandal is not credible. Only strict and ethical access to research via the platform’s API can protect users more than anything else (Bruns, 2019). When a user tries to collect data by following a hashtag or a topic, the algorithm tends to show him those results that correspond to a particular perspective on the topic, the one that reflects his preferences. This trend is particularly worrying in the academic context: collecting data that is affected by algorithmic bias can compromise the entire body of research, significantly contributing to producing a bias on results. Very interesting research (Kulshrestha J. et al., 2017) has shown to what extent search results on social media like Twitter are distorted. Kulshrestha and the other researchers collected data for 25 political queries on Twitter during the week of two 2015 US presidential debates (for both Republicans and Democrats), and found that the platform’s ranking system contributes significantly to producing bias in search results by shifting and in some cases altering polarity. The researchers observed, for example, that Twitter’s input data stream for the most popular candidates in one party was more biased towards the opposite political perspective; so, if a user searches for the most popular Republican candidate, they will receive more tweets of the opposite party politician than if he sought the most popular Democratic candidate. This classification system in producing output bias can greatly influence the research experience, inevitably leaving open the question of the implications that the very functioning of platforms has on scientific research, which no longer has free and neutral access to data.

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ACKNOWLEDGMENT This research has received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Lury, C., & Wakeford, N. (2012). Inventive Methods. The Happening of the Social. Routledge. doi:10.4324/9780203854921 Manovich, L. (2012). Trending: The Promises and the Challenges of Big Social Data. http://manovich.net/ content/04-projects/068-trending-the-promises-and-the-challenges-of-big-social-data/64-article-2011.pdf Marres, N. (2012). The redistribution of methods: On intervention in digital social research broadly conceived. The Sociological Review, 60(1), 139–165. doi:10.1111/j.1467-954X.2012.02121.x Marres, N. (2017). The reinventation of Social Research. Wiley. Natale, P., & Airoldi, M. (2017). Web e social media. Le tecniche di analisi. Maggioli editore, Sant’arcangelo di Romagna (RN). Perriam, J., Birkbak, A., & Feeman, A. (2019). Digital methods in a post-API environment. International Journal of Social Research Methodology, 3, 277–290. Rega, R. (2016). Twitter as a New Engagement Opportunity. Analysis of the Questions and Answers between the Italian Prime Minister and Citizens. Trípodos, 39, 91–107. Rieder, B. (2013). Studying Facebook via Data Extraction: The Netvizz Application. WebSci ’13, Proceedings of the 5th Annual ACM Web Science Conference, 346-355. doi:10.1145/2464464.2464475 Rogers, R. (2007). Electronic media policy field: Metrics for actor impact and resonance. Ford Foundation. Rogers, R. (2009). The end of the virtual. Digital Methods. Amsterdam University Press. doi:10.5117/9789056295936 Rogers, R. (2013). Digital Methods. MIT Press. doi:10.7551/mitpress/8718.001.0001 Rogers, R. (2018). Social media research after the fake news debacle. Partecipazione e Conflitto: The Open Journal of Sociopolitical Studies, 11(2), 557–570. Savage, M., & Burrows, R. (2007). The coming crisis of empirical sociology. Sociology, 41(5), 885–899. doi:10.1177/0038038507080443 Savage, M., & Burrows, R. (2009). Some further reflections on the coming crisis of empirical sociology. Sociology, 43(4), 762–772. doi:10.1177/0038038509105420 Venturini, T., Jensen, P., & Latour, B. (2015). Fill in the Gap. A New Alliance for Social and Natural Sciences. Journal of Artificial Societies and Social Simulation, 18(2), 11. doi:10.18564/jasss.2729 Venturini, T., & Rogers, R. (2019). “API-Based Research” or How can Digital Sociology and Journalism Studies Learn from the Facebook and Cambridge Analytica affair. Digital Journalism, 7(4), 532–540. doi:10.1080/21670811.2019.1591927

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Criticalities and Advantages of the Use of Artificial Intelligence in Research Jessica Camargo Molano International Telematic University Uninettuno, Rome, Italy Jacopo Cavalaglio Camargo Molano University of Modena and Reggio Emilia, Italy

ABSTRACT In recent years, artficial intelligence, through the rapid development of machine learning and deep learning, has started to be used in different sectors, even in academic research. The objective of this study is a reflection on the possible errors that can occur when the analysis of human behavior and the development of academic research rely on artificial intelligence. To understand what errors artificial intelligence can make more easily, three cases have been analyzed: the use of the IMPACT system for the evaluation of school system in the District of Columbia Public Schools (DCPS) in Washington, the face detection system, and the “writing” of the first scientific text by artificial intelligence. In particular, this work takes into consideration the systematic errors due to the polarization of data with which the machine learning models are trained, the absence of feedback and the problem of minorities who cannot be represented through the use of big data.

INTRODUCTION The objective of this study is a reflection on the possible errors that can occur when the analysis of human behavior and the development of academic research rely on Artificial Intelligence. It is evident that over the last few years the use of Artificial Intelligence is spreading to different areas of daily life. It is therefore unthinkable that it does not have a role even in the field of academic research, a role destined to become more and more important with the development of correlated technologies. DOI: 10.4018/978-1-7998-8473-6.ch011

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 Criticalities and Advantages of the Use of Artificial Intelligence in Research

Consequently, the researcher, who makes use of this system, should be aware of both its advantages and criticalities. “We are living in the era of post-truth. […] The status of scientific research as a reliable source of truth has consequently weakened to the point that politicians, journalists or unscrupulous entrepreneurs consider it equivalent to any other opinion and therefore lacking in legitimacy. Given this situation, it may seem paradoxical that this decade is also seen as the era of data: a revolutionary moment for technological innovation and research mechanisms, and a triumph of the empirical basis of knowledge over pure speculation. Thanks to digital technologies and increasingly globalized research and communication systems, we have very large amounts of data at our disposal - a sea of facts to be studied and interpreted, whose analysis through machine learning algorithms is a fundamental factor in the development of artificial intelligence.”1 (Leonelli, 2018:4) With regard to this topic, in 2000 Abbott argued that sociological research was not ready to meet the challenges that lay ahead. “And the blunt fact is that sociology is woefully unprepared to deal with this problem: We have neither the analytic tools nor the conceptual imagination necessary. Our stockin-trade analytic methods were designed for investigating relations between small numbers of variables and are useless for large-scale pattern-recognition”2. Starting from this assumption, the research tries to understand if the scenario is the same or has changed after twenty years and investigate the state of the relationship between the analysis of big data of human behavior and Artificial Intelligence. The great quantities of available data and the use of Artificial Intelligence to analyze it “may have dreadful consequences for credibility and quality of the knowledge produced”3 (Leonelli, 2018:5). In order to prevent it, it is necessary to know the limits and errors that can characterize a study carried out by using Artificial Intelligence. As Numerico highlights in his work (2019), “the algorithms trained with human databases give sectorial representations that replicate, even more strictly, the beliefs and prejudices of humans, as a result of the correctness of hypotheses concerning the contextual value of meaning”4. To understand the errors which Artificial Intelligence can make more easily, three cases have been analyzed: the use of the IMPACT system for the evaluation of school system in the District of Columbia Public Schools (DCPS) in Washington, the face detection system and the “writing” of the first scientific text by Artificial Intelligence. The three cases examined show the most common biases (lack of feedback, polarization of data and lack of representation of minorities), which can lead to faulty results, if these systems are used in social research; moreover, they stress some issues of epistemological nature. “With the introduction of digitalization, we have a large quantity of data relating to human behavior at our disposal. It is uploaded on platforms that offer their users wide spaces for the storage of the contents generated by the users themselves. Therefore, the perspective of social studies ranging from sociology to marketing seems to be reorganized by the analysis of all these sources that already appear in the form of data. Furthermore, they are not considered as a sample of population or a section of textuality or just a simple representation, but as indicators of the entire sphere of social behavior”5 (Numerico, 2019: 471). Through the cases examined it is possible to reflect on the use of Artificial Intelligence in the field of research, especially in sociology, and on the birth of a new figure of researcher, who should update his/ her “toolbox” in such a way as to be able to “dialogue” with Artificial Intelligence.

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DEFINITIONS Before analyzing the mentioned case studies, it is necessary to pay attention to what is meant by machine learning, Big Data and mathematical models. According to the definition given by Hurwitz and Kirsch (2018), “Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process. Machine learning uses a variety of algorithms that iteratively learn from data to improve, describe data, and predict outcomes. As the algorithms ingest training data, it is then possible to produce more precise models based on that data. A machine learning model is the output generated when you train your machine learning algorithm with data. After training, when you provide a model with an input, you will be given an output”6. The concept of machine learning has been developed since the 1950s (Arthur Lee Samuels, the IBM researcher who coined the term “machine learning”, published a paper about his studies on the topic in IBM Journal of Research and Development in 1959), but it has been used only over the last few years since favorable conditions have been created for its use: the supply of increasingly powerful modern processors and GPUs (Graphic Processing Units perform from five to twenty times as high as traditional CPUs used for algorithm training), the possibility of distributing computation processing through clusters of computers, the reduction in data storage costs, faster performances thanks to storage innovations and the ability to analyze vastly larger data sets. Furthemore, this historical period is characterized by the increasing use of smart systems (smart phones, smart TVs, social networks, etc.) that make it possible to collect a large number of data, and by the presence of IoT technologies (Internet of Things) that allow people to group huge amounts of data to be used as input for AI. The accuracy of a machine learning model can increase substantially if it is trained on Big Data “Big data is any kind of data source that has at least one of four shared characteristics, called the four Vs: • • • •

Extremely large Volumes of data The ability to move that data at a high Velocity of speed An ever-expanding Variety of data sources Veracity so that data sources truly represent truth”7 (Hurwitz and Kirsch 2018:6).

The possibility of elaborating large amounts of data (Big Data) allows the development of Artificial Intelligence models. In order to create precise AI models, it is necessary that data sources are accurate and meaningful, besides being previously cleaned and sorted. The refinement of data provides the foundation for building models that release reliable results.

CASE STUDY: THE USE OF THE IMPACT FOR THE EVALUATION OF SCHOOL SYSTEM One of the cases that best shows the criticalities of the use of machine learning in social research is the evaluation of school system in the District of Columbia Public Schools (DCPS) in Washington. In 2009, the municipal administration launched a program aimed at evaluating the teachers through the results achieved by their pupils. The hypothesis put forward by Michelle Rhee, D.C. Schools Chancellor, was 163

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based on the idea that students did not achieve satisfactory results due to the inadequate teaching they received. “Academic progress must be measured by growth,” Rhee said. “By using value-added analysis we will finally be able to consistently reward and recognize the significant contributions of every adult in a school building”8 Rhee’s objective was therefore to identify the teachers who were not doing their job well and to fire them. To pursue this aim, Princeton’s Mathematica Policy Research consulting firm developed IMPACT9, a teacher assessment tool. The main problem, which the IMPACT developers had to face, concerned the discernment of the different components that could influence a student’s academic achievement10. Therefore, it was necessary to take into account not only the teacher’s skills, but also the student’s family situation and socio-economic background, as well as the numerous variables that can affect the process of learning. For example, the schools examined by means of IMPACT belonged to the poorest and most disadvantaged areas in the city. “Teachers at low-income schools learn quickly that schools do not exist in a vacuum. These schools’ student bodies are full of kids dealing with the toxic stress of poverty, leaving many of them homeless, hungry, or sick due to limited access to quality healthcare. The students are more likely to have an incarcerated parent, to be deprived of fresh or healthy food, to have spotty or no internet access in their homes, or to live in housing where it is nearly impossible to find a quiet place to study. Low-income parents are more likely to engage in shift work or have lower levels of education; younger children tend to have smaller vocabularies before they even reach the classroom, and older students may have to work or watch younger siblings in order to support parents. These constraints explain why teachers at disproportionately minority schools (who are more likely to be themselves minority) struggle to deliver the individualized attention and interventions that IMPACT requests. When rates of parent volunteerism and involvement are decreased, many students are left without their primary adult advocate and teachers are left with limited outside context to explain their students’ struggles, successes, and behaviors”11 (Quick, 2015). At the end of the 2009-2010 school year, all the teachers, whose students had made a score lower than the values of the analysis curve, were fired (about 2% of the teaching staff of the district) and a further 5% in the following school year as well. Sarah Wysocki was one of the fired teachers, she had always been considered an excellent teacher and had always received positive evaluations from her colleagues and her students. In the school year prior to her dismissal, Mrs. Wysocki was entrusted with a first-year class at MacFarland Middle School. Almost all the students had previously attended Barnard Elementary School and, on the occasion of the last IMPACT assessment test, 29% had an advanced level of reading, a figure five times as high as the school district average. During the school year, Mrs. Wysoki realized that, even though the tests certified her students’ skills, the real situation was very different. In fact, many students in the class did not have an advanced level of reading, on the contrary they had difficulty reading simple sentences. It is evident that the IMPACT tests taken by the students at the end of elementary school reported inflated and untrue results. Actually, both schools and teachers had considerable interests linked to the positive outcome of the tests: school administrators could receive bonuses of up to 8000 dollars if the school they managed had proved to be excellent, and it was obviously essential for teachers that students achieved positive results otherwise they would be fired. “The city of Washington, DC is divided by wards: Ward 3 is by far the wealthiest, housing only 23 percent low-income students; Ward 8 is in one of the poorest parts of the city, with 88 percent of its students considered low-income. During the 2013-2014 academic school year, half (yes, literally 50 164

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percent) of the teachers in well-to-do Ward 3 earned a score of “highly effective” -the highest possible IMPACT rating, and the designation that qualifies teachers for salary increases. In comparison, only 19 percent of teachers in Ward 8 earned that distinction”12 (Quick, 2015). As emerged from the survey, which was subsequently conducted by the newspapers “The Washington Post” and “USA Today” in forty-one schools in the district includig Barnard Elementary School, the IMPACT tests were full of deletions and corrections (a high percentage of corrections in the answers is an indication of fraud). Consequently, Mrs. Wysocki’s students started from untrue, inflated evaluations. During the school year, the teacher, as testified by the colleagues’ and school administrators’ assessments, had done her job at her best. At the end of the school year, the pupils were tested and the results were negative. Actually, the students had made a lot of progress during the year, but they had not yet been able to equal the inflated starting results. According to the standardized system of assessment, they had had a decline in their academic performances and consequently Mrs. Wysocki was fired. She tried to contest her dismissal, but she had no success; the teacher evaluation system was considered “fair” by the administration and above all Sarah Wysocki was asked to put forward incontrovertible proofs that testified to the manipulation of the initial tests. The low score obtained by Mrs. Wysocki depended not only on the fact that the results of the tests were not correct, but also on the fact that the system evaluated her both as a Language teacher and as a Mathematics teacher by means of the same TFL (Teaching and Learning Framework), which is a parameter used for assessing a teacher’s skills. In order to value TFL, during the school year a teacher is observed by school administrators and master educators, or expert practitioners that travel from school to school, and who may or may not have knowledge of the teacher’s local communities or expertise in the subject area being taught. During the period of observations, the teacher must demonstrate to be able to use a series of pedagogical and didactic techniques in thirty-minute lessons. Moreover, although 87% of the students in the District of Columbia Public Schools (DCPS) were Afro-American or Hispanic, none of the experts, nominated to assess TFL in these schools, belonged to one of these minorities, a situation that inevitably creates the possibility of biases in observations. “Several aspects of the evaluation are highly dependent on modes of student, rather than teacher, communication. One of the more egregious examples of this is a provision that not only implores teachers to use “academic language” when explaining content, but also requires students to demonstrate verbally and through writing that “they are internalizing academic vocabulary”. This is a huge ask of teachers, especially given the deep language barriers that exist in many minority schools. While it might be pedagogically useful to employ a broad vocabulary in classrooms to encourage student growth, tying a teacher’s performance and pay to a student’s ability to absorb and adopt unfamiliar or uncommon language within a thirty minute observation session is a neither fair nor effective measure. “Teachers whose students interrupt or engage in “inappropriate or off task student behavior” are also downgraded in rating. Given what we know about racial discipline disparities and the skewed perceptions of “disruptive” or “dangerous” behavior across class and color lines, this criteria can perpetuate some very troubling patterns for both students and teachers”13 (Quick, 2015). Finally, another fundamental aspect to be taken into consideration to understand the criticalities of the IMPACT system is the small number of cases analyzed. “Evaluating the performance of a teacher by analyzing the results of the tests of only twenty-five or thirty students is statistically groundless, if not ridiculous. The numbers are far too small, since something may not take its right course. Indeed, if we were to analyze teachers by using the statistical rigour of a search engine, we would have to test 165

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them on thousands or even millions of randomly selected students. Statisticians rely on large numbers to compensate for exceptions and anomalies”14 (O’Neil, 2016:11). By analyzing the functioning of IMPACT, the following biases, which can influence the evaluation of teachers, emerge: • • •

Lack of feedback Racial prejudice A very low number of cases examined

CASE STUDY: FACE DETECTION SYSTEMS In May 2019 Joy Buolamwini, the founder of the Algorithmic Justice League (AJL) -a non-profit organization that works to identify the social implications and harms of Artificial Intelligence (AI)- testified, during a session of the Chamber, that “failures of facial analysis technologies have had real and dire consequences for people’s lives, including in critical areas such as law enforcement, housing, employment, and access to government services”15. Buolamwini founded the AJL after experiencing such a failure personally, when the face analysis software did not succeed in identifying her dark-skinned face until she wore a white mask. Such failures have been attributed to a lack of disparity within the group of engineers who create facial analysis algorithms. In other words, facial detection reaches its highest accuracy rate when used with white male faces. Buolamwini carried out a research “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification”16, in which she tested the operation of three different facial detection algorithms developed by Microsoft, IBM and Megvii. This study showed that the algorithms had difficulty in identifying female faces and people with a non-Caucasian phenotype. In particular, in the case of darkskinned women, there was an error rate of 35% against an error of 1% in the case of Caucasian males. Buolamwini’s research highlighted a reality that was partly already known. In 2015 Jack Alciné, a dark-skinned boy, decided to use “Photos”, the newborn system for archiving and cataloguing images that Google placed at its users’ disposal. Unlike the other filing systems, “Photos” also tagged images in order to describe their main content, so that it helped its users catalogue images according to the subject. Jack Alciné uploaded some images, including a photo where he appeared together with a friend of his, she was dark-skinned as well. “Photos” tagged that photo with “Gorillas”. The system was unable to identify the faces of two dark-skinned people and mistook them for two gorillas. Google apologized for the mistake publicly, but the case highlighted a criticality in the facial detection system. A similar situation occurred in 2009 with the webcam management software in new HP computers. The software was equipped with a system called “face tracking”, which allowed the webcam to automatically follow the user when he moved his face from one side to the other or to zoom to and fro during a video chat session. An Afro-American user, Desi, decided to test face tracking together with his colleague Wanda, who has a Caucasian phenotype. The system had no problems with tracking the movements of Wanda’s face, while it could not follow the movements of Desi’s face. The test conducted by Desi became one of the most shared videos on YouTube and HP admitted that there was really a problem with identification (the company rejected the accusation of racism made by Desi, asserting that it was a problem of brightness and contrast of the image).

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In July 2018, the American Civil Liberties Union (ACLU) conducted an experiment to persuade Congress to ban the use of facial detection in public safety. For the experiment, ACLU used the facial detection system named “Amazon Rekognition”. Artificial Intelligence was trained to identify offenders’ faces through a database consisting of 25,000 identification photos. Subsequently Amazon Rekognition analyzed the photos of the 535 congresspeople. In 28 cases (in other words more than 5% of cases), Artificial Intelligence found a correspondence, which was actually non-existent, between the congresspeople’s and the offenders’ photos. Even if only a fifth of the members of Congress are Afro-American, about 39% of the incorrect correspondences concerned dark-skinned people. The previously mentioned cases show the so-called inductive bias of the algorithm, that is the presence of erroneous hypotheses in automatic learning process. This problem occurs not only in facial recognition, but in every system that uses information about people (biometric, behavioral, credit information, etc.). “The presence of biases in Artificial Intelligence is often associated with a poor quality of training data, which already contains some kind of polarization inside itself. Though this is one of the possible causes, reality has more facets: result polarization can take place even before data collection, or in other phases of the Deep-Learning process”17 (Benedetti, 2019). According to Benedetti (2019), it is possible to identify three phases that can give rise to the phenomenon of result polarization: problem definition, data collection and data pre-processing. The first difficulty is to clearly identify which objective is to be achieved by the system. If the objective is not clear, the system will have difficulty in processing good results. As regards the data collection phase, “There are two main ways in which bias can appear in training data: in the former collected data are not representative of reality, in the latter they reflect existing prejudices. The first case could occur, for example, if a Deep-Learning algorithm was fed with more photos of fair-skinned faces than dark-skinned ones. The resulting facial recognition system would inevitably have more difficulties in identifying dark-skinned people. The second case occurs when the data used to train the algorithm are taken from a historical series coming from polarized environments, i.e., information on the incidence of criminal actions could show a higher frequency of crimes in slums, where there are more police and therefore more police reports than in quieter and less guarded neighbourhoods. In this case the system would only perpetuate this polarization, in a self-feeding cycle”18 (Benedetti, 2019). MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), for example, has developed an algorithm to mitigate bias in a facial detection system, even if training data is not homogeneous. During the training phase, the algorithm identifies the examples under-represented in the data and reproposes them several times in order to increase the efficiency of training and reduce the detection error. The algorithm, which was tested with the same data used by Buolamwini in his research, has showed to be able to reduce the gap existing in the identification of fair-skinned and black-skinned faces, without eliminating it completely. Finally, biases may be introduced during the data pre-processing phase. “The choice of the characteristics to be considered or ignored can significantly influence the ability to predict the model. However, while the impact on prediction accuracy is easy to measure, the impact on polarization (or correctness) is more difficult.” It is important to note that the problem concerning bias in the system was completely ignored both by programmers (human component) and by machines, until an independent audit highlighted its criticality. One of the greatest problems is due to the amount of data that algorithms analyze: the number is constantly increasing and consequently there is an increasing risk of hiding the error. To understand this aspect, it is sufficient to examine the operation carried out by IBM after the publication of Joy Buolamwini’s 167

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research. The company improved its system, reducing the error significantly, but, to reach this aim, it had to create a data set called DiF (Diversity in Face) consisting of 1 million images and 10 encoding schemes. A small number, when compared with the database used by Facebook for its face detection system (Deep Face) in 2014: a set of 4 million photos on which 120 million parameters, divided into 9 distinct detail levels, were identified.

CASE STUDY: LITHIUM-ION BATTERIES – A MACHINEGENERATED SUMMARY OF CURRENT RESEARCH This investigation focuses on the evaluation of the content produced by AI. An interesting case is “Lithium-Ion batteries – A Machine-Generated Summary of Current Research” by Beta writer19, the first machine that generated a scientific book. The book sums up several articles from peer review papers about Lithium-Ion batteries published on Springer Nature’s content platform “SpringerLink” from 2016 to 2018. The algorithm was developed by a research group from Goethe University Frankfurt in collaboration with the publisher Springer Nature. The aims were basically two: firstly to find a possible solution for the problem of managing very big amounts of scientific information in an efficient way, secondly to open a discussion about the future role of AI in research and in scholarly publishing industry. The discussion is principally based on the following questions: Who is the author of the content of the book? The developers of the algorithm or the person who gives the first input such as the topic of the generated book? Who is responsible for the content of Artificial Intelligence from an ethical point of view? As regards copyright, there is a regulatory vacuum in the robotics sector to date. In 2017 the European Parliament approved the “Recommendations concerning civil law regulations on robotics”20, but there is no real legislation on the subject. The bill brought forward by MEP Mady Delvaux declines the three laws of Asimov21 on robotics, addressing them not only to designers and manufacturers of machines (human component), but also to machines themselves (“considering that Asimov’s laws must be considered as aimed at designers, manufacturers and users of robots, including robots with built-in autonomy and selflearning capabilities, since these laws cannot be converted into machine code”22). When an institution of law is recognized to an artificial intelligence, the machine is considered a subject with a capacity for discernment equal to the human being. Consequently, copyrights and patrimonial exploitation rights of the work could even be attributed to the machine. In order to evaluate the goodness of AI’s work, it is necessary to take into consideration the following questions: How has the input dataset been built? Which algorithm has been used to create the machine learning model? How have the output results of the model been validated? The first point is the most important because Artificial Intelligence heavily depends on data, and algorithms cannot learn without them. The most typical problems of the input dataset are its dimensions (if the number of different examples is big enough for the function) and the precautions taken in order to avoid biases on the dataset. In the case of “Lithium-Ion batteries – A Machine-Generated Summary of Current Research”, the dataset consisted in the peer-review papers from one of the most important scientific publishing editor (Springer). The goodness of the dataset was based on the fact that all the articles taken into consideration by the algorithm were peer-review. In order to have only data related to the Lithium-Ion batteries, the Springer input documents were searched through the keywords in pub-

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lication titles and metadata annotations. The input documents included in the dataset were of different types: journal articles, complete books and single chapters. This method used for the input dataset pre-processing reduces the possibility of biases in the dataset, but at the same time highlights the importance of peer-review papers. In the specific case, the basic hypothesis is that possible concept and language errors are eliminated thanks to the human review of the documents. Therefore, if the use of AI for the creation of scientific summaries increases, the peer review of scientific documents made by experts with a deep knowledge in the relevant field will be more and more important. The algorithm workflow is based on three phases: • • •

document clustering and ordering, extractive summarization, paraphrasing of the generated extracts.

The first phase consists in the individuation of all the documents that deal with the same subject based on textual and bibliographical similarities. In the second phase, the algorithm automatically detects the themes and generates the table of contents for each chapter and section. At this point the experts can tune the system by redefining the automatic generated structure. The user can change the position of chapters or remove some of them. In the last phase, the algorithm automatically fills the chapters and sections with the reformulated content deriving from the texts in the input dataset. The answer to the second question (which algorithm has been used to create the machine learning model?) includes a very important hypothesis, that is the transparency of the “authors” in declaring which type of algorithm they have used. Without this information it is not possible to evaluate if the creation process has been performed in a correct way. The problem is that some types of AI algorithms are hardly explainable23 24, that means that for human prospectives it can be very difficult or impossible to completely understand the process that the system has followed for the transformation of the input dataset into the final results. Because of this problem, over the last few years big research efforts have been put on Explainable Artificial Intelligence (XAI)25 26. In the case of “Lithium-Ion batteries”, the algorithm is well described so that it can be as understandable as possible. As illustrated by Fig.1, in “Lithium-Ion batteries – A Machine-Generated Summary of Current Research” all the best practices for the correct use of AI were followed. If the use of AI for the creation of scientific summaries increases, even the figure of the reviewer should change, since the review and evaluation of an AI document require an expert of Neural Network and Natural Language Processing. Otherwise, it will be necessary to create a common standardization for AI and input dataset. For the evaluation of the goodness and correctness of a generated summary, the algorithm and the starting text datasets have to be clear and completely accessible to reviewers. As regards algorithms, it is necessary to check possible “bugs”, while, as regards text datasets, it is necessary to exclude problem of biases. In the case of the book by Beta writer, the input dataset was generated only by clusterized document keywords without attaching any importance to each publication. However, in the future it will be possible to assign a scale of importance to each text. In order to reach this aim, it will be necessary to make

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Figure 1. Model details

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the input dataset and the rules defined in the algorithm completely clear. Without any standardization of the method of review, it will be impossible to detect if the text runs into the typical AI problem of bias.

CONCLUSIONS The analysis of the previously mentioned case studies has highlighted the role of Artificial Intelligence in social research and in particular it has pointed out some of its criticalities. The survey carried out leads to a reflection on the use of Artificial Intelligence in academic research: is it possible to rely on a machine learning model in conducting research? First of all, it is necessary to say that a perfect machine learning model does not exist, but it can have a high accuracy in its prediction. The effectiveness of AI depends on managing sources of errors to reduce them as much as possible and warning the final AI user which source of error could affect his/her work. The power of AI principally depends on two components: the goodness of the input dataset and the experience of the data scientist in the choice and tuning of the algorithm. The input dataset can incorporate the following errors: 1) Errors in data collection It can happen that a systematic or a non-systematic error occurs during the data collection. For example, if the dataset is produced by a signal recorded by a sensor, it is possible that the sensor is broken and so the data contains systematic errors. In the case of a survey, one or more participants can accidentally give wrong information. 2) Generalization of the dataset If the input data used for the training of AI does not represent the most general scenario, the machine learning model can learn only a type of data. This kind of error is illustrated in the case dealt with in “Face detection systems”, where the algorithm identifies only a type of people due to a limited input dataset. In this case, the AI error is called overfitting and it takes place when the model has a very high accuracy in recognizing data similar to the input dataset, but in case of different data, the error is high. 3) Bias in the dataset A bias dataset consists in a dataset that could have a too lower number of examples, or could not be balanced (the number of data coming from a cluster is higher than that of data from other clusters), or has been developed on wrong hypotheses. The algorithm can be characterized by the following errors: 1) Model selection error This error is due to a wrong selection of the machine learning algorithm, as there are several algorithms and each of them has a different purpose (image recognition, natural language processing, classification, etc.). In order to choose the most performant algorithm, it is important to test different types 171

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of algorithms with the same dataset so that it is possible to evaluate their differences and performances. Moreover, transparency is another element necessary in the type of the algorithm used in order to review the correctness of the choice. 2) Error from Model Tuning Very complex machine learning algorithms are characterized by the presence of different parameters that have to be tuned in order to guarantee the desired performances of the model. These parameters are not always very explainable and for this reasons AI research is progressing towards the creation of Explainable Artificial Intelligence (XAI). A good way to limit the problem is to test the performances of the algorithms against all the tuneable parameters and make all the parameters public so that other experts can test the reliability of models. This reflection highligths the need for the researcher to have a critical approach towards AI. As Mazzocchi points out, it is necessary to avoid that the technology used is considered as a black box “Putting a technology in the black box means making it so obvious as to remove it from the scrutiny of users, sometimes even analysts, who therefore cease to see it as something contingent and modifiable and accept it as natural instead. At this point technology is - so to speak - closed, it can no longer be questioned, and in turn becomes a constitutive element of further more complex technological systems”27. Mazzotti (2015) attributes to social scientists a scarce attention to numbers and formal deduction, a lack linked to the perception - erroneous - that their studies have to do exclusively with ideas and theories. Faced with an increasingly important role of AI in research, the sociologist should learn new skills and should ask himself/herself new questions. As MacKenzie (2006) argues, the more mathematical models are used in social research, the more important the role of the researcher is, because “the ability to discriminate between right and wrong results can only be based, ultimately, on the judgment of a human community”28. It is important to note that “The world we live in is becoming more and more algorithmic, from financial markets to automatic face recognition. The time has come to leave algorithms behind us as mythical entities to be exalted or rejected, and instead to study how they work or could work in practice, every day, shaping our future”29 (Mazzotti, 2015: 473).

REFERENCES Asimov, I. (2003). I Robot. Gnome Press. Benedetti, D. (2019, Apr. 8). Riconoscimento automatico del volto: I rischi della tecnologia. Agenda Digitale. Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research, 81, 1–15. Došilović, F. K., Brčić, M., & Hlupić, N. (2018). Explainable artificial intelligence: A survey. In 2018 41st International convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE. 10.23919/MIPRO.2018.8400040

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Gitomer, D., Crouse, K., & Joyce, J. (2014). A review of the DC IMPACT teacher evaluation system. Summative Evaluation of the District of Columbia Public Schools of the National Academy of Sciences. Headden, S. (2011). Inside IMPACT: D.C.’s Model Teacher Evaluation System. Education Sector. Holzinger, A. (2018). From machine learning to explainable AI. World Symposium on Digital Intelligence for Systems and Machines (DISA), IEEE 2018, 55-66. 10.1109/DISA.2018.8490530 Holzinger, A., Kieseberg, P., Weippl, E., & Min Tjoa, A. (2018). Current advances, trends and challenges of machine learning and knowledge extraction: from machine learning to explainable AI. International Cross-Domain Conference for Machine Learning and Knowledge Extraction, 1-8. 10.1007/978-3-31999740-7_1 House Hearing. (2019, May 22). Facial Recognition Technology (Part 1): Its Impact on our Civil Rights and Liberties. Author. Hurwitz, J., & Kirsch, D. (2018). Machine Learning for dummies. IBM Limited Edition, John Wiley & Sons. Leonelli, S. (2018). La Ricerca Scientifica nell’Era dei Big Data. Meltemi Editore. MacKenzie, D. (2006). An Engine, Not a Camera: How Financial Models Shape Markets. MIT Press. doi:10.7551/mitpress/9780262134606.001.0001 Mazzotti, M. (2015). Per una sociologia degli algoritmi. Rassegna Italiana di Sociologia, 3-4, 465–477. Numerico, T. (2019). Social network e algoritmi di machine learning: Problemi cognitivi e propagazione dei pregiudizi. Sistemi Intelligenti, 3, 469–493. O’Neil, C. (2017). Weapons of Math Destruction. How Big Data increases Inequality and Threatens Democracy. Crown Publishing. Quick, K. (2015). TheUnfair Effects of IMPACT on Teachers with the Toughest Jobs. The Century Foundation. Raccomandazioni alla Commissione concernenti norme di diritto civile sulla robotica (2015/2013 INL) – Relazione del 27 gennaio 2017 Turque, B. (2009, Oct. 1). New D.C. Teacher Ratings Stress Better Test Scores. The Washington Post. Wojciech, S., Wiegand, T., & Müller, K. R. (2017) Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint, arXiv:1708.08296 Writer Beta. (2019). Lithium-Ion Batteries. A Machine-Generated Summary of Current Research. Springer International Publishing.

ENDNOTES

1 Leonelli S. (2018), La Ricerca Scientifica nell’Era dei Big Data, Milano, Meltemi Editore

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2 Abbott A. (2020), Reflections on the Future of Sociology. Contemporary Sociology, 29, 296 3 Leonelli S. (2018), La Ricerca Scientifica nell’Era dei Big Data, Milano, Meltemi Editore 4 Numerico T. (2019), Social network e algoritmi di machine learning: problemi cognitivi e propagazione dei pregiudizi, Sistemi Intelligenti, 3, 469-493 5 Ibidem 6 Hurwitz J., Kirsch D. (2018), Machine Learning for dummies, Hoboken, IBM Limited Edition, John Wiley & Sons 7 Ibidem 8 Turque B. (2009), New D.C. Teacher Ratings Stress Better Test Scores, The Washington Post, October 1, 2009 9 Headden S. (2011), Inside IMPACT: D.C.’s Model Teacher Evaluation System, Washington, Education Sector 10 Gitomer D., Crouse K., Joyce J. (2014), A review of the DC IMPACT teacher evaluation system, Summative Evaluation of the District of Columbia Public Schools of the National Academy of Sciences 11 Quick K. (2015), TheUnfair Effects of IMPACT on Teachers with the Toughest Jobs, The Century Foundation, October 16, 2015 12 Ibidem 13 Ibidem 14 O’Neil C. (2017), Weapons of Math Destruction. How Big Data increases Inequality ad Threatens Democracy, New York, Crown Publishing 15 House Hearing May 22, 2019, “Facial Recognition Technology (Part 1): Its Impact on our Civil Rights and Liberties”, 2154 Rayburn House Office Building, Washington, DC 20515 16 Buolamwini J., Gebru T. (2018), Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification, Proceedings of Machine Learning Research, 81, 1–15 17 Benedetti D. (2019), Riconoscimento automatico del volto: i rischi della tecnologia, Agenda Digitale, Aprile 8, 2019 18 Ibidem 19 Writer Beta (2019), Lithium-Ion Batteries. A Machine-Generated Summary of Current Research, New York, Springer International Publishing 20 “Raccomandazioni alla Commissione concernenti norme di diritto civile sulla robotica (2015/2013 INL)” – Relazione del 27 gennaio 2017 21 Asimov I. (2003), I Robot, New York, Gnome Press 22 “Raccomandazioni alla Commissione concernenti norme di diritto civile sulla robotica (2015/2013 INL)” – Relazione del 27 gennaio 2017 23 Holzinger A. (2018), From machine learning to explainable AI, World Symposium on Digital Intelligence for Systems and Machines (DISA), IEEE 2018, 55-66 24 Holzinger A., Kieseberg P., Weippl E., Min Tjoa A. (2018), Current advances, trends and challenges of machine learning and knowledge extraction: from machine learning to explainable AI, International Cross-Domain Conference for Machine Learning and Knowledge Extraction, 1-8 25 Wojciech S., Wiegand T., Müller K. R (2017), Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models, arXiv preprint, arXiv:1708.08296

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26 Došilović F. K., Brčić M., Hlupić, N. (2018), Explainable artificial intelligence: A survey, 2018 41st International convention on information and communication technology, electronics and microelectronics (MIPRO), IEEE, 2018, 210-215 27 Mazzotti M. (2015), Per una sociologia degli algoritmi, Rassegna Italiania di sociologia, 3-4, 465-477 28 MacKenzie D. (2006), An Engine, Not a Camera: How Financial Models Shape Markets, Cambridge, Mass., MIT Press. 29 Mazzotti M. (2015), Per una sociologia degli algoritmi, Rassegna Italiana di sociologia, 3-4, 465-477

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Chapter 12

Learning Algorithms of Sentiment Analysis: A Comparative Approach to Improve Data Goodness Suania Acampa University of Naples Federico II, Italy Ciro Clemente De Falco University of Naples Federico II, Italy Domenico Trezza University of Naples Federico II, Italy

ABSTRACT The uncritical application of automatic analysis techniques can be insidious. For this reason, the scientific community is very interested in the supervised approach. Can this be enough? This chapter aims to these issues by comparing three machine learning approaches to measuring the sentiment. The case study is the analysis of the sentiment expressed by the Italians on Twitter during the first post-lockdown day. To start the supervised model, it has been necessary to build a stratified sample of tweets by daily and classifying them manually. The model to be test provides for further analysis at the end of the process useful for comparing the three models: index will be built on the tweets processed with the aim of detecting the goodness of the results produced. The comparison of the three algorithms helps the authors to understand not only which is the best approach for the Italian language but tries to understand which strategy is to verify the quality of the data obtained.

The work is the result of a joint work of the three authors, however the paragraph “Big corpora and Digital Methods: a critical approach to improve data goodness” and “Sentiment Analysis and Main Text Classification Algorithms” are by Suania Acampa; the paragraph “Supervised Learning Algorithms used in the analysis “and” Index Scores and Tweet Characteristics “are by Ciro Clemente De Falco; the DOI: 10.4018/978-1-7998-8473-6.ch012

Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 Learning Algorithms of Sentiment Analysis

paragraph “Data and Methods to compare some Supervised Algorithms” and the paragraph “Analysis. Path, Models and Comparisons” are by Domenico Trezza.

INTRODUCTION Big Corpora and Digital Methods: A Critical Approach to Improve Data Goodness The ubiquity of digital technologies and the popularity of opinion-rich platforms such as social media and review sites generates a large and rapid amount of user-generated data encoded in natural language daily. Reviews, tweets, likes, links, shares, texts, posts, tags etc.; these are only part of the billions of digital traces that we leave on the web every day, through which it is possible to accurately trace the tastes, opinions, and attitudes of everyone. Big corpora represent a profitable empirical basis for all those who investigate social phenomena on the net. The production and increasing availability of data offers new possible forms of knowledge of social complexity that social researchers cannot ignore. The data revolution is considered as “the sum of the disruptive social and technological changes that are transforming the routine of construction, management and analysis of data consolidated within the various scientific disciplines” (Amaturo, Aragona, 2017, p.1). The new digital technologies and big data allow social research to move from the construction of empirical bases through interrogation to the construction of empirical bases through survey. Big data allows us to measure complex phenomena in detail in real time, thanks to the evolution of IT tools and techniques such as artificial intelligence, machine learning, and natural language processing. This promotes interdisciplinarity between different scientific areas and provides social researchers solid empirical bases for experimenting and integrating new and traditional approaches to social research. These technologies push the social sciences into a scenario in which “web-mediated research [...] is already transforming the way researchers practice traditional research methods transposed to the web” (Amaturo and Punziano, 2016, 35, 36). To be able to describe and analyse this wealth of information, social scientists have also begun to use computational analytical methods to assemble, filter and interpret user generated data encoded in natural language. Text mining is part of this context, a branch of data mining that allows you to analyse vast textual corpora in different languages ​​by extracting high quality information with very limited manual intervention. Natural language processing (NLP) is the area of ​​machine learning dedicated to the meaning of the written word. A very profitable branch of natural language processing is sentiment analysis: it consists in the extraction and analysis of the opinions that users express on the web towards products, services, topics or characters. With language processing and text analysis, sentiment analysis identifies subjective information in sources. The main objective is to determine the general polarity of a text (whether it is a review or a comment) and classify it into three categories: positive, negative or neutral. Sentiment analysis techniques are divided according to the type of approach used: lexicon based or machine learning approach. The machine learning approach treats sentiment classification as a question of general text classification. This approach to classification is divided between unsupervised and supervised learning models. In supervised models it is necessary to arrange a training set labelled with the indication of the polarity of the feeling (negative, positive, neutral) that the algorithm will use to predict the polarity of other textual content contained in the test set. The machine learning approach has the advantage of not 177

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depending on the availability of dictionaries, but the accuracy of the classification methods depends a lot on the correct labelling of the texts used for training and on a careful selection of the features by the algorithm. The results of the three supervised algorithms were adopted and compared through an analysis model that involved the construction of the labelled training on which the three models were tested to evaluate the accuracy of each. The next step involved recoding the processed tweets based on their agreement/discrepancy with the output returned by each of the three algorithms. The tweet analysis allows us to define the components (text, sentiment, and other features) that suggest a plausible relationship with the functioning of the algorithm. These algorithms that work through learning open interesting developments by defining data accuracy parameters in relation to validated benchmarks. Our work examines sentiment in a sample of one-day tweets in Italian (May 4, 2020) related to phase 2 of the post-lockdown. The tweets were processed with the three most widely used algorithms in the literature for this type of analysis (Naives Bayes, Decision Tree and Logistic Regression). The results of the three supervised algorithms were adopted and compared based on the accuracy of each and the predictive ability. To check if there were latent differences in the corpus, it was decided to use a lexical correspondence analysis (ACL) which allowed us to define the components (text, sentiment, and other characteristics) that give us information about the functioning of the algorithm. Although the techniques are advancing rapidly and their performances are improving year by year, the analysis shows that the functioning of the chosen algorithms still present various limits for the Italian language.

SENTIMENT ANALYSIS AND MAIN TEXT CLASSIFICATION ALGORITHMS What do algorithms look for during the human information processing process? The basis of the functioning of text analysis (Mostafa, M. M., 2013), is natural language processing (NLP): the area of machine learning dedicated to the meaning of the written word. Machine learning (ML) is the ability of the computer to learn independently thanks to algorithms that improve their performance in an experiential way from the examples that researchers provide to learn. There are three machine learning approaches (Medhat, W., Hassan, A. e Korashy, H., 2014), that guide text analysis and sentiment analysis in particular: Supervised approach: from a set of labelled data (training), the goal of the classification algorithms is to predict the class attribute on unlabelled data sets (testing). Classifiers learn from training data to make future inferences. For each document to be classified, the algorithms define a vector of properties (called features) that represent it. The extraction of the features from a text is the process of extrapolating its salient characteristic, the most used are words, parts of speech, opinion words, negations (Deshmukh S.N. and Shirbhate A. G., 2016). Unsupervised Approach: learning algorithms detect the latent structure of unlabelled data: the techniques consist in providing the computer system with a series of inputs that the algorithm will classify automatically and independently on common characteristics and statistical rules. Semi-supervised approach: combine a small amount of tagged data with a large amount of unlabelled data. Using this approach is very practical when labelling data requires a lot of human intervention. A highly productive branch of natural language processing is sentiment analysis. In academia there is a growing interest in this analysis because it provides useful tools for public opinion analysis by automatically detecting the information contained in a textual corpus and measur-

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ing polarity (positive, negative, neutral), emotions (angry, happy, sad, etc.) and intentions (interested, not interested). To analyse the sentiment of a text it is necessary for it to be pre-processed: we will illustrate the details in the following paragraphs. Sentiment analysis uses various methods and algorithms for natural language processing, these methods can be divided into: 1. Lexicon based approach. 2. Machine learning approach. In the lexicon-based approach, the definition of sentiment is based on the analysis of individual words or phrases using dictionaries of opinion words where the words are assigned a weight in terms of positivity or negativity. Figure 1. Sentiment classification techniques by Walaa Medhatun, Ahmed Hassanb and Hoda Korashy

All the words in the document are compared with the words in the dictionary: each time a word in the document matches one in the dictionary, the score associated with that word is added to the overall sentiment score of the document (Taboada M. et al., 2011). The general sentiment of the text will be nothing more than the sum of the scores of the individual words. The lexical approach can be based on the dictionary or the corpus.

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In the dictionary-based approach a comparison is made between the words of the corpus and those of appropriately constructed dictionaries (such as WordNet and SentiWordNet) which contain (in addition to words) syntactic-lexical information such as synonyms and antonyms, with the aim of labelling each semantically relevant term with a sentiment orientation score. The process is iterative and ends when no new words are found in the text. In the corpus-based approach, the text is compared with a very large collection of already labelled documents. This approach can be done through two techniques: a) Statistics. This technique is based on the calculation of occurrences starting from opinion words: if the word occurs more frequently in positive texts then it will have a positive polarity, if it occurs more in negative texts it will have a negative polarity. With this reasoning, the same words within the same concept must have the same polarity. b) Semantics. This approach is based on the idea that two neighbouring words can have the same polarity by semantic principle, as in the major and most widely used lexical databases present online for sentiment analysis work. Unfortunately, even the largest database will not be able to cover all the words and their possible combinations. The advantage of the corpus-based approach is that it does not need a previous labelling operation of the incoming texts but is based on already existing dictionaries. This advantage can turn into a limit for the investigation when the availability of dictionaries in the language in which you are investigating is insufficient. The dictionary must be sufficiently large and appropriate to the context we are analysing, since the same dictionary may not have the same effectiveness in different survey contexts. Furthermore, in this approach the comparison takes place with texts written correctly, from a syntactic and grammatical point of view- The texts collected from the Internet, however, are mostly ungrammatical and full of errors. Machine learning is the other possible approach to sentiment analysis. This approach treats the sentiment classification as a general classification problem. This approach to classification takes up the division between the unsupervised and supervised learning models described above. In supervised models, a training set labelled with the polarity of sentiment (negative, positive, neutral) is needed, and the classifier will use this to predict the polarity of other textual content. In unsupervised learning only unlabelled texts are used and the analysis is based on the comparison of words in terms of similarity and differences. One example is the Latent Dirichlet Allocation model (Liang, J. et al., 2016). The LDA algorithm attributes a topic to each word of the document through the analysis of co-occurrence. LDA associates each document to the topics most commonly represented by the words that compose it. In this way it is possible to identify the topics of a text only by observing the co-occurrences of the words with respect to a reference knowledge base. The machine learning approach has the advantage of not depending on the availability of dictionaries, but the accuracy of the classification methods strongly depends on the correct labelling of the texts used for training and on a careful selection of the features considered by the algorithm (Basile V., Nissim M, 2013). For both the supervised and unsupervised approaches, the output always requires careful validation because there is no machine learning model that works better than another. The “No Free Lunch” theorem by David Wolpert and William Macready (1997) is famous in the field of machine learning. This says that a model can be good for one problem and bad for another, so you need to test multiple models 180

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to find the one that works best for the problem under investigation. Analysing feelings accurately is also difficult for humans and the language (especially the Italian one) has so many nuances that a machine will never be able to understand them all. For this reason it is essential to have a vast knowledge of one’s data and the context in which they were collected.

SUPERVISED LEARNING ALGORITHMS USED IN THE ANALYSIS As previously clarified, in this work the supervised classification approach, an approach that usually involves the use of machine learning techniques, will be used. In the supervised classification the division classes of the statistical units are decided by the researcher. A supervised approach generally requires two phases, regardless of the algorithm used. In the first phase, the model is trained on a training set and its accuracy is studied on a test set. In the second phase, the model is applied to the data to analyse. There are three algorithms and their related R packages that we will use in our work: Naive Bayes (r package “e1071”), Decision trees (R package “Cart”) and Logistic Regression (R package “CaTools”). It is necessary highlight that, in line with the “bag of words” model, the starting matrix will be the “Document Term Matrix” for all the algorithms used. The “Document Term Matrix” is a matrix where the number of lines corresponds to the documents present in the dataset while the number of columns corresponds to the significant words in the dataset.

Naive Bayes Classificator The Naïve Bayes classifier represents a probabilistic approach to solving classification problems. Sentiment analysis is a two- or three-class classification problem. The Naive Bayes classifier is built on Bayes’ theorem whose logic is the following: for each possible cause that can trigger a certain - already occurred - event, the probability is calculated. Therefore, the “subjectivist” (Rish, 2001) Bayesian approach allows the scholar to make a priori assumptions, based on their information state, which they enter directly into the model and then these assumptions can be strengthened, rejected or corrected on the basis of the information contained in the data. In Naïve Bayes’ technique, the basic idea is to find the probabilities of categories in a given text document by using the joint probabilities of words and categories (Dey et. Al, 2016). In other words, NBC uses maximum a posteriori estimation to find out the class (i.e., features are assigned to a class based on the highest conditional probability) (Samuel, 2020). For sentiment analysis we can use two Bayesian classification models: Bernoulli and multinomial. The first considers the presence of a term in each document. In the multinomial, on the other hand, in addition to the term’s presence/absence, its frequency of occurrence in the text is also considered. On the other hand, Bernoulli’s NB only considers whether a term is present or not in a document and not how many times it occurs. It should be emphasized that the algorithm is defined as “naive” because it assumes feature independence. In other words, the words within a text have no form of correlation between them. The Bayesian classifier, compared to other algorithms, has the advantage that the training phase can be conducted on a limited number of cases. On the other hand, the assumption of independence between features is difficult to sustain. In the end there is one final aspect to underline related to the use of “Laplace Smoothing”, a correction factor used to avoid the documents’ classification as an “impossible event”. The R package that we are going to use, in addition to giving the choice of reference model (i.e. multinomial or Bernoulli), also gives the possibility of using use “Laplace Smoothing”. 181

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Decision Tree The purpose of the classification and regression algorithms is to describe or predict the class to which a given unit belongs. Classification trees are used when the dependent variable is categorical, while regression trees are used when the dependent variable is cardinal. In the classification trees, as we shall see, the predictive power is calculated on the basis of the percentages of misclassification while in the regression trees it is given by the quadratic difference between observed and expected data. In the case of sentiment analysis, the algorithms used are the classification ones, and the classification tree allows us to identify logical rules to establish the sentiment of a text. (Dey et. Al, 2016). The classification procedure will make it possible to identify the “words”, whose presence or absence indicates a certain type of sentiment. Unlike other techniques, in this technique the “predictive variables” (therefore the features) aren’t used at the same time but sequentially, and chosen for their ability to produce splits that maximize internal homogeneity between groups and reduce the external one. The aim is to produce a logical path useful for classifying the statistical units. The decision tree construction to carry out sentiment analysis involves three phases: • •

•

choice of subdivision criterion (or split): for each level it is necessary to choose the word that produces the best split according to a given criterion for reducing heterogeneity. The indices usually used to establish the best split are “heterogeneity” or “Gini”. definition of a stop criterion in the tree construction: the definition of stop criteria is necessary to avoid overfitting problems (i.e. the loss of generality of the model) since the classification can proceed up to the constitution of nodes determined by irrelevant “words”. These criteria should be based on two principles: simplicity (trees with as few levels as possible) and discrimination (maximum level of heterogeneity allowed in node identification of a rule for assigning one of the J classes of the dependent variable to each leaf: if in each final node there are not all cases with positive or negative sentiment, it is necessary to establish assignment criteria. One of them is establishing class sentiment based on the highest frequency sentiment in the class.

The Curt algorithm we will use in our work uses the Gini index as a measure of heterogeneity. The curt is distinguished by the way it constructs the “tree”: first it builds the decision tree of maximum size, after which it carries out a “pruning” phase on the less significant branches. The levels subjected to cutting are linked to the definition of the complexity parameter. Among the possible “subtrees”, the one with the lowest misclassification rate will be chosen.

Logistic Regression Logistic regression is usually used to identify the possible relationship between one or more independent variables and a dichotomous dependent variable, which in the case of sentiment analysis assumes positive (1) and negative (0) values. This technique allows us to understand the explanatory power of the individual independent variables and which independent variables combination has the greatest discriminating power (Samuel, 2020). The term “logistic” is due to the non-linear relationship between x and y that has a binomial distribution described by a logistic curve. In logistic regression the values predicted by the equation are arbitrary and the output regards the probability that a subject or a text, in 182

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the case of sentiment analysis, belong to one of the two modalities of the dichotomous variable. The probability is expressed through the odd, which is given by the ratio between the observed frequencies in one class and the observed frequencies in the other. Thus, the reference value of the logistics equation is the odd’s natural algorithm, the logit. The logit is chosen instead of the odd for mathematical reasons and specifically because its variation range, which goes from plus to minus infinity, allows us to linearize the relationship between the variables included in the model. To solve the equation, in the logistic regression the OLS method cannot be applied since the assumptions are not verified, so the maximum likelihood algorithm (ML) is used. This algorithm estimates the parameters to maximize the loglikelihood function that indicates how likely it is to obtain the expected value of Y given the values of the independent variables. In the training phase the algorithm, through the analysis of frequencies, identifies which words characterize each modality of the dependent variable.

Model Fit Confusion matrix, also known as contingency table, is typically used in supervised machine learning techniques for allowing the visualization of algorithm performance on a test set. Usually in confusion matrix, rows are labelled with effective labels and columns are labelled with predicted labels. Starting from confusion matrix it is possible to calculate different types of standard performance metrics: accuracy; precision, recall and F-Measure. • • •

Accuracy: indicates the accuracy of the model and is calculated by dividing the number of correct predictions by the total of the statistical units. Precision: is the classifier ability to correctly label a certain category. Given a certain category, precision is obtained by dividing the number of correct predictions by the total number of cases labelled in that category. Recall (or sensitivity): is the classifier’s ability to identify all cases of a certain category. Given a certain category, it is calculated by dividing the number of correct predictions by the actual number of cases that fall into that category.

The value of precision and recall range from 0-1, while the value of Accuracy is expressed in percentage. The estimated model works if its accuracy value is larger than the accuracy of the baseline (the model always provides the most frequent value as a forecast). These metrics will help us to evaluate the performance of each algorithm used in the analysis.

DATA AND METHODS TO COMPARE SOME SUPERVISED ALGORITHMS While the availability of large amounts of data represents the opportunity to build solid analysis paths, on the other hand it poses a non-trivial challenge to the researcher on the methods and strategies of analysis to be adopted when working with large data sets. The recognition of automatic classification techniques in the previous paragraph suggests that supervised machine learning models, although different from each other, are constantly evolving, trying to refine what should be the general objective when doing research: constructing (and returning) reliable data (Marradi, 2007). In this sense, these algorithms, working through learning, open interesting developments by defining data accuracy parameters in 183

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relation to validated benchmarks. Our work examines the sentiment of a sample of single-day tweets (May 4, 2020) related to start of the post-lockdown. The results of the three supervised algorithms seen previously were adopted and compared through an analysis model that involved the construction of the labeled training on which the three models were tested to evaluate the accuracy of each. The next step involved recoding the processed tweets based on their agreement / discrepancy with the output returned by each of the three algorithms. The tweet analysis allows us to define the components (text, sentiment and other features) that suggest a plausible relationship with the functioning of the algorithm.

Definition of Dataset to Train the Algorithm The comparison between the three supervised sentiment algorithms has been conducted on a tweet dataset dated May 4, 2020. The topic was the reopening of activities following the quarantine for the Covid-19 outbreak. Why this day? It responds to the need to test the model in optimal conditions. The hypothesis, in fact, is that the beginning of ‘Phase 2’ generated mixed feelings (fear of the possible infection resurgence, enthusiasm for the end of domestic confinement, fear for the economic situation, etc.). So, the risk of having a one-way sentiment (and therefore useless for the models to be tested) is not high. For the training of the model, a stratified sample of tweets (corresponding to 1% of the main dataset) was built according to proportional shares for the three daily slots: morning (00:00 - 11:59 am), afternoon (12:00 - 18:59 pm) and evening (19:00 - 23:59 pm). As can be seen in Table 1, users ‘tweeted’ especially during the first part of the day and in the afternoon (48% and 42% respectively), while only 10% of the tweets were posted during the evening. Table 1. Tweet by time of day and quote of tweets Base

Quote

Training set

Morning (00:00 - 11:59 am)

9924

48%

994

Afternoon (12:00 - 18:59 pm)

8721

42%

870

Evening (19:00 - 23:59 pm)

2067

10%

207

20712

100%

2071

A sentiment has been assigned for each tweet: positive, negative or neutral. The attribution was stipulated based on criteria shared among the authors of the study. We provided a fourth category, ‘non-classifiable’, to place tweets of this type: 1. Not containing text but images or links to third party sites; 2. Without any reference to the pandemic, phase 2, or the emergency in general. The distribution in Table 2 shows that only 4% of the tweets in the sample belonged to the latter category. On the other hand, of the 96% of the tweets to which it was possible to attribute a sentiment, most (40.6%) expressed a negative sentiment towards phase 2 and only about 25% were judged to express a positive sentiment. A good percentage of tweets, just over 34%, could not be associated with a positive or negative sentiment, so they were classified as neutral.

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Table 2. Tweets by classification and sentiment (v.a. e %)

Unclassifiable tweets

v.a.

%

90

4

Classifiable tweets

1981

96

Negative

805

40,6

Positive

498

25,2

Neutral

678

34,2

Tot

2071

100

ANALYSIS. PATH, MODELS AND COMPARISONS To reduce the elements of possible ambiguity and to test the model better, we decided to only use the tweets with positive and negative sentiment. So the training / testing dataset is constituted by 1303 tweets. The RStudio software was used to analyse this sample of tweets because it is a powerful programming environment with an intuitive interface suitable for the development of scripts for analysing large amounts of data, especially unstructured data. Machine learning algorithms cannot work if applied directly to raw text: first of all, it was necessary to either convert the “character” class content into numbers, specifically into numerical vectors in order to reflect the various linguistic properties of the text (Goldberg, 2017). This procedure is called “feature extraction or coding”: the bag-of-word or the bag-of-ngram are the textual analysis and representation models capable of extracting the features from our text to insert them into a textual corpus first, then into a two-dimensional matrix. With the creation of the BoW Matrix it was possible to transform each tweet into a vector that will be used as input data for chosen machine learning algorithms. The vector transformation process was carried out on all tweets until the vocabulary of all the words that appear in the entire corpus was obtained with the respective frequency of occurrence of each term for each tweet. Although information on the order, structure of the text and semantic relationship between words is lost, the information content remains intact. For the creation of a vocabulary useful for the extraction of features, what is needed is the set of terms that occur throughout the corpus, because it is the frequency of occurrences of words that is used as a feature to train the text classifiers. What often happens is that a textual corpus contains few words that are useful for analysis, it was therefore necessary to reduce the size of the vocabulary because, while it is true that the language is complex, it is also true that not all the complexity of language is necessary to effectively analyse the texts. (Grimmer and M. Stewart, 2012). The first step was to apply the VCorpus function to create a corpus of 1303 tweets. The second step was to pre-process the corpus, in particular: 1. The text was converted to lowercase so that the algorithm does not include different elements such as “Covid”, “covid”. 2. Punctuation was removed to prevent the algorithm from considering terms such as “#covid”, “covid” as different elements. 3. Stop words, very common words with a grammatical function that do not convey meaning, were eliminated from the corpus. 4. A stemming process was activated which returned the inflected form of each word to its root form and then mapped all the words belonging to the same family of meanings.

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Figure 2. Words cloud

Stemming is the computational version of the linguistic concept of lemmatization: the lemmatisation algorithms uses dictionaries and the textual context to discover words that belong to the same root. The Figure 2 shows the words cloud of the most frequent words contained in the corpus following the pre-processing phase. This mode of representation gives us information about our vocabulary. The weight of the tags, rendered with characters of different sizes, is intended exclusively as the frequency of occurrence of the term within the tweets (tf): the larger the character, the greater the number of appearances of the word in the textual body. As expected, the most frequent words were those relating to the second phase of the lockdown imposed by the Prime Minister Conte. Once corpus was pre-processed, the frequency of each single term was extracted by converting the corpus into a documents-terms matrix (dtm). To assess the relevance of the various terms contained in the tweets, it is possible to use the “weighting value” as an indicator, which measures the frequency of

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occurrence of each word in each tweet and which then weighs its weight within the collection (tfi-df). In the documents-terms matrix each column contains the single terms of the tweet corpus; the rows contain the documents (tweet) and the cells contain the number of appearances of that term in that document. The dtm contains 1303 documents (tweets) and 7921 distinct terms with 100% sparse. This dispersion index indicates the presence in the corpus of a mass of terms that appear rarely and that therefore most of the cells report a value equal to 0. We have chosen to set a sparse value = 0.995, only the terms for a sparse value greater than 0.995 have been removed. The sparse topic corresponds to the relative frequency threshold of the document for a term, above which the term is removed. The variable “sentiment” has been added to the matrix with sparse at 0.995 and it is ready to be divided into training set and testing set. The training set was built with the first 900 cases and the test set with the remaining 403 cases. At this point it was possible to process the two sets with the three chosen algorithms. We started the three models (Logistic Regression, Naive Bayes, Decision Tree) for four tests since with the LR algorithm we tried to include 4 quantitative variables in the model, linked to the type of ‘engagement’ of the tweet - and therefore plausibly linked to sentiment - that is the number of favourites, retweets, the length of the tweet and the author’s followers. Tab.3 summarizes the output values of the three supervised algorithms. As we observe, in the four tests we find accuracy values (i.e. the correct percentage of sentiment predicted on the total of tweets) that are lower than the optimal percentage of 90%, ranging from 54.7% of the RL applied only to the text, to a little more than 67% of the Decision Tree algorithm. However, the precision values that calculate the correctness for the single sentiment show us a heterogeneous situation varying from a maximum precision for the positive sentiment calculated by the Naive Bayes model (detects 95% of the total tweets with positive sentiment) to 94% of tweets with negative sentiment detected by the Decision Tree model. This polarization in sentiment prediction suggests that many elements of ambiguity remain in the text of the tweets. Therefore, it is useful to intensify the analysis, this time on the content of the tweets, to explore possible elements of meaning that have conditioned the work of the algorithm. For this reason we decided to build an additive index for each tweet relative to the concordance between the observed and predicted sentiment value for each of the three algorithms.

INDEX SCORES AND TWEET CHARACTERISTICS As seen before, the algorithms used have recorded overall different performances and in our case study have shown average high percentages of incorrect classification. Thus, a high percentage of tweets has not always been classified correctly. This partly unlooked-for data suggested an in-depth study aimed at understanding if the wrong classification was attributable to the content of the tweets. To do this, a “correct classification” index was developed. The index ranges from zero to four, where zero indicates that the tweet was not classified well by any algorithm, while four indicates that the tweet was correctly classified by all algorithms. Fig. 3 shows that only 10% of tweets have always been correctly classified, while around 30% of tweets have been correctly classified at least once. The median category is represented by tweets that have been correctly classified twice (23.1%) out of four, while the modal one is represented by tweets that have been correctly classified three times (36.2%). These data are perfectly in line with the accuracy levels seen above.

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Table 3. Supervised algorithm, values of output Supervisned Algorithm

Logistic regression

R Package

RL

Precision

Dataset training

Predictors

Train/ test

Accuracy

1303 tweet / pos-neg

text and other variables

2,5

1303 tweet / pos-neg

only text

Recall

Negative

Positive

Negative

Positive

56,5%

59,3%

51,5%

69,0%

40,9%

2,5

54,7%

55,6%

53,0%

68,3%

39,5%

NAIVE BAYES

e1071

1303 tweet / pos-neg

only text

2,5

45,0%

17,4%

95,4%

87,5%

38,7%

Decision Trees

CART

1303 tweet / pos-neg

only text

2,5

67,2%

94,2%

18,8%

67,7%

63,1%

Figure 3. Tweet for index scores

Regarding the analysis objective, one aspect that partly affects the “correct classification” index is the size of the tweets. Indeed, the analysis of variance shows a significant relationship between the index and the variable “corpus length” (F = 2.77; p . We have 832 tag/words under the Meta-tags “Pandemic” that includes [Covid], [Coronavirus], [Andratuttobene], [Corona], [Quarantena], [Quarantine] It is a software in open source developed in the academic sphere which allows visualization of the network for social research. to define social casting, read Bennato, D. 2011, Sociologia dei Media Digitali, Laterza, pp. 3-7. It is a mass subcultural phenomenon that has recently become mainstream. Interest in a celebrity or a band (for example One Direction) becomes an obsession, which leads to the world of fiction. The fan constructs sparkling and unofficial accounts of his hero that enhance his physical, moral and professional characteristics. The digital obsession of the fan is similar to that of the stalker: the

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name standom is the crasis between the protagonist of the song by Eminem, Stan, and the suffix “dom”. Today the standom is an ironic and playful way of following the idols.

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Section 4

Digital Methods: Among Transposed and Mixed Approaches The digital methods that have exploded in recent years in social research have slowly taken on extremely peculiar forms, definitions, and characterizations, and this combined set of reflections is systematized in the chapters collected for this section by providing the reader with defining elements and application procedures that recall transposed methods—such as content analysis, social network analysis, or visual sociology—and connoted approaches, such as mixed methods.

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Chapter 21

Digital Mixed Content Analysis on Digital Platform Social Data: The Revival of a Research Technique and Its Declination Between Mixed and Digital Methods Gabriella Punziano https://orcid.org/0000-0001-8783-2712 University of Naples Federico II, Italy

ABSTRACT The explosion of platform social data as digital secondary data, collectible through sophisticated and automatized query systems or algorithms, makes it possible to accumulate huge amounts of dense and miscellaneous data. The challenge for social researchers becomes how to extract meaning and not only trends in a quantitative as well as in a qualitative manner. Through the application of a digital mixed content analysis perspective to data analysis, in this contribution, the author will present the potentiality of a hybrid digitalized approach to social content. This perspective should be seen as an applied example of organizing a framework to guide the application of integrated methods of content analysis (quantitative and qualitative) but also integrated objects of analysis (individuals, relationships, and digital actions) on digital platform social data and to address their varied nature.

INTRODUCTION The explosion of platform social data as digital secondary data, collectable through sophisticated and automatized query systems or algorithms makes it possible to accumulate huge amounts of dense and miscellaneous data (Amaturo & Punziano, 2017). A challenge for social researchers is how to extract meaning and not only trends in a quantitative as well as in a qualitative manner (Punziano, De Falco, &Trezza, 2021). However, an integrated research proposal is not only limited to this. The digital scenario, conceived/considered as an extension of the society, in the perspective of a critical digital sociology DOI: 10.4018/978-1-7998-8473-6.ch021

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 Digital Mixed Content Analysis on Digital Platform Social Data

(Marres, 2017) should be approached following the well-known Rogers’ mottos (2009): follow the medium, follow the native and follow the thing. This attitude reflects the mindset of moving as digital natives but with the expertise and experienced eye that a digital researcher must have (Lupton, 2015). This is not an easy challenge, but it is certainly the main one for those who want to approach analyzing data as complex in nature, shape, and content as those present on social platforms. Actually, the starting point of this contribution is reflected in the statement which maintains that all the content circulating on social platforms, considered as social networking site where it is possible to interact with other users but also with the content that these users leave on the Net, is mainly made up of text in conjunction with images, videos, links, geographical location, enriched by information related to forms of sharing, engagement (such as with the use of reactions, likes and comments, among the other), as well as of relationships and connection. In this particular combination resides the essence of digital platform social data. The aim of this chapter is to show the main possibilities that the digital mixed content analysis approach can provide to the researchers when they deal with platform social data enhancing a perspective where a hybrid digitalized approach to social content can be highlighted. In pursuit of this goal, a definition for the digital mixed content analysis approach will be proposed below. Subsequently, in the chapter it will be exposing the characteristics that make the research object of this approach - digital platform social data - an incredibly powerful object of interest for social research. Then, the path of choices that are posed to the researchers who intend to follow such approach will be given further clarification. In the end, acute reflections, critical and prospective, for the approach will be developed.

DEFINING THE DIGITAL MIXED CONTENT ANALYSIS APPROACH With respect to the possibilities introduced by the digital mixed content analysis approach, it would be fair to start by saying that something bigger is going on. In the years leading up to the digital turn (Lupton, 2014; Marres, 2017), viewed as a paradigm shift in social sciences, a new approach has been increasingly retrieved: The technique of content analysis is used to extract secondary meaning from information that allows researchers to recover and examine the nuances of behaviors, perceptions, and trends, from existing content produced with different purpose compared to those of research (Schreier, 2012; Krippendorff, 2018). Nevertheless, it is not just the approach that is finding a renewed space in social research. Every kind of data is nowadays available on the Net in large quantities and the digital scenario in its transition from the interaction among human actors and between human actors and the product of their actions (posts, comments, reactions, and so on) shifts the whole socio-cultural reflection on this topic. This shift led to a real transition from the classic Internet Studies that aimed to investigate how much and what part of the culture was on the Net to Digital Methods that make the Net not a replicated scenario of society but an object with its own dynamics, characteristics and infrastructure that cannot be investigated with a mere transposition of the frameworks classically used. Therefore, rather than talking about web content analysis, which found its memorable place in the Internet Studies, here it is preferred to talk about digital content analysis. So, Digital Methods can be considered as a set of research and strategy approaches using data produced in digital environments to study socio-cultural changes (Rogers, 2009, Caliandro & Gandini, 2016) using the knowledge about the Internet and the context of the Web not only as an ontological structure but also as a resource method to study people’s behavior and social groups. The digital environment provides a permanent research context that offers scholars a range of techniques 347

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and native tools to measure and interpret social phenomena. Innovating the traditional tools of content analysis also allows researchers to handle a large amount of data preserving the strengths of traditional content analysis (systematic rigor and contextual sensitivity), while maximizing the large-scale capacity of the digital environment. In light of this perspective, Web 2.0 and the data revolution (Kitchin, 2014) have influenced the content analysis approach from three points of view: the wide availability of digital content freely available on almost every topics; the mixing of traditional with non-traditional techniques to face the challenges posed by old and new data which facilitates obtaining sophisticated statistical analysis almost automatically and economically with respect to time and cost resources; the importance of facing a cognitive problem through complex approaches, a mixture of expertise and points of view. However, since we are talking about complex phenomena, even the epistemological and ontological point of view cannot remain anchored to visions circumscribed within single approaches that by nature and construction would direct the researcher’s gaze towards a single direction. Thus, the Mixed Methods approach becomes the lighthouse. Therefore, here the proposal is to recover a multi-comprehensive approach that can provide a deeper understanding of the multitude of user-generated information and of which users make use, reuse, and new consumption (Hesse-Biber & Johnson (2013) arriving to talk about digital mixed content analysis. In this regard, it is sufficient to think that Schreier (2012) and Krippendorff (2018) affirmed that qualitative and quantitative content analysis are not discrete classifications, but rather fall along a continuum, a notion also used by Teddlie and Tashakkori (2011) to define the new horizon for social research methods in the light of the third approach, the mixed one. Stressing the approach along this continuum allows researchers to extract greater opportunities to gain insight into the meaning of data. Already Bryman (2012) states that, by definition, content analysis is a research approach that can be situated at the intersection of quantitative and qualitative methods, a place where both methods can meet, and the manifest and latent meanings of the data are quantified and qualified. Combining this understanding of content analysis with a solid Mixed Methods design could allow researchers to reach the maximum result from the massive growth of digital texts and multimedia data. To recap, the perspective that is proposed requires the researchers to be able to juggle different knowledge and assets, which go beyond the methodological ones (i.e., qualitative or quantitative), but require the digital professional researchers to be constantly updated about new applications, software, algorithms for extraction, management, classification, organization and extraction of knowledge from digital content, closer to the expertise of a data scientist. In other words, this means that there was a breaking down of the boundaries between qualitative and quantitative approaches, between virtual and real as hemic categories, as well as among different disciplines, skills, and expertise, leading to the birth of forced hybridizations which restores the figure of the digital social researchers as intrinsically meta-disciplinary and ultra-frontier.

DEFINITION OF DIGITAL PLATFORM SOCIAL DATA What has been said so far is true for all content on the digital scenario, but it is more and more true for the researchers that use data from social media platforms (e.g., Facebook, Twitter, LinkedIn or similar) for whom there are few guidelines for data collection, analysis, and evaluation. The digital mixed content analysis model should be organizing a framework to guide the application of integrated methods (quantitative and qualitative) of content analysis on digital platform social data, and to address the varied nature of these data. 348

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Digital platform social data are constituted by all those data produced by the constant interaction between users in the Net with social platform of networking, with other users and with the infrastructural components of these platforms. With this term are recalled all that information on a specific system of agency that makes the users producer of the scenario in which they move as well as of the contents that they convey, construct, using and reusing in this scenario. Content and infrastructure of the digital scenario become the main components of the digital mixed content analysis model. These components are also able to be rethought as specific and characterizing metrics. For the content, we can use systems of meaning extraction, machine learning and natural language processing when it comes to texts. However, when these contents take the form of images, photos, videos, and audiovisual materials many other systems of knowledge generation can be adopted, always leveraging on systems of machine learning and classification that lead to develop metrics of sentiment, bias, and so on. Many other metrics can be followed and associated to the infrastructure of the digital scenario in order to further understand the agency and interaction systems developed on social platforms. These are affordances such as shares that can be considered proxies for sharing the content to which the action refers; likes that can be transformed into metrics of liking; comments that can be read as proxies for the level of interaction and capacity of the content to provoke argumentative reactions; mentions as proxies for linking and connection systems; reactions as potential elements for the evaluation of social users’ engagement; clicks as proxies for the level of interest in given objects, issues and ways of dealing with them; and many others. All these metrics indirectly tell us that digital platform social data contains relational, interactional information and natural reputational metrics systems. These are data that are far from the non-digital or digitized one and of which we can only remotely understand the real informative capacity. These data are obviously useful for social research because they are big, always on, non-reactive, on any topic of interest, and each of us is now a generator of data that take on the value of a currency as they are full of information on the most disparate fronts and issues. But they are also problematic for social research. In fact, they are often incomplete, inaccessible, unrepresentative, unstable, biased by underlying algorithms and mining algorithms, indecent (sometimes terribly so), and contain potentially sensitive information (Salganik, 2020). Then, it would be reductive not to mention other issues that tie this data to the digital social scenario. The users who voluntarily leave a trace of themselves on the Net, whether implicitly or consciously, are explicitly vocalizing a thought, an opinion, a position, a preference, an argument, and so on. Classically, in order to reach this kind of elements, social research has worked on complex constructions of systems of attitude detection and measurement, such as through the application of scaling techniques (Tittle & Hill, 1967). Today, such a deep and intimate complex of information, on which the researchers feared to develop a feedback effect, is present in the digital social scenario, linked to the most disparate elements and issues, left on a scenario in which they come to life the moment they are released and continue to exert their influence since they are present in the Net for an indefinite time becoming potential objects of use and reuse. The problem for the social researchers shifts from the complex of reflections on the possibility of constructing adequate instruments for detection to the complex of reflections on the possibility of collecting these contents, skimming them in coherence with the objectives of their own research and generating meaning, knowledge, and functional utility. For all these reasons to the great potentiality the digital platform social data offer they also show the dark side of their limitations. The proposed framework tries to shed light on the choices that the researchers are called to make by reasoning about consistency, possibilities offered and potential that can be explored by adding to the study of this particular kind of data an approach that moves among Digital and Mixed Methods. 349

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BEING A MIXED METHODS RESEARCHER IN A DIGITAL SCENARIO Mixed Methods research centers around researchers being able to collect multiple data using different strategies, approaches, and methods. The desired results of this mixture have the characteristic of being more than the simple combining of the single methods in order to generate greater and more integrated research outcomes (Daigneault & Jacob, 2014). Nowadays, it is not only a question of methodological principle that addresses social researchers, but also the ever-growing relevance of the kind of data used, the information contained therein, the possible multilayers of reality which they lead to, and the undeniable need for integration between these pieces of information to build ever more complete paths of knowledge. Using content analysis in the digital era to analyze digital platform social data, means being faced with old and new challenges. Digital mixed content analysis researchers, in fact, must be aware of several characterizations that accompany the construction of their own research designs on the digital scenario. Firstly, they need to formulate their cognitive questions and make the purposes of their analysis explicit. This particular phase of formulation of the research questions implies the researchers ensure that the phenomenon under investigation has an interesting implication and coverage in the digital scenario and that the producers of the contents of interest for the research have relevance in leaving traces of their activities and interaction with other human and not human users on this scenario, such as with algorithms, bots, and, more in general, with the technical affordances of the digital environment (Caliandro & Gandini, 2016). In other words, the researchers need to be sure that the cognitive question they are asking can actually be answered in the digital scenario. Now, although this scenario can be considered a direct emanation of society, a place where most of the social phenomena find their natural continuation and alternative way of existence, not all phenomena have the same relevance inside and outside the Net. Additionally, not all phenomena are adequately represented on the Net and, very often, when these phenomena concern niche issues, subject to social judgment or belong to limiting or covert communities, the digital scenario, in addition to being difficult for closures and privacy issues, becomes the least consistent way to get to the data of one’s interest. Therefore, the researchers cannot avoid clarifying the link between their cognitive objective, their research object, and the digital scenario. Only an adequate development and justification of the option taken makes the choice robust, and in this scenario this statement is more valid than ever. But this is not the only point. In addition to coming to terms with the inquiry scenario, the digital one, the researchers must also come to terms with the mixed nature of the methods he intends to adopt. In this regard, rearticulating a Levy’s proposal (2017), the cognitive issue in the Mixed Methods approach involve an integrated set of research questions designed to directly address the Mixed Methods nature of the study. Its construction is based on relational language (e.g., using words and phrases such as synergy, integration, connection, complete, better, and deeper understanding) aimed at finding a conjunction between the involved approaches. Following the dictates of this methodological community of reference (Fetters & Molina-Azorin, 2019), the researchers are called upon to provide a solid justification for the choice of Mixed Methods strategy in lieu of single method approaches by clarifying the added value of this path in the specific study being conducted. Now, as mentioned above, due to the multifaceted nature of digital data, a Mixed Method approach becomes increasingly required, but this equivalence cannot be taken for granted, and thus it will be up to the researchers to make its functionality explicit:

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Every good study should begin by specifying its precise idea of Mixed Methods and offering a historical reconstruction of the approach to ensure its relevance, as it is still not fully mature. It is then necessary to motivate the choice of this approach and the purpose of the study, justifying the relationships and the co-presence of both qualitative and quantitative data. This will need to be linked to the nature of the research hypotheses, the sampling strategies, the way in which the data are collected and analyzed, and the very procedures for validating the results adopted. An important space should also be reserved for the explication of practical procedures of analysis and for the language that will allow the juxtaposition and interpretation of data from different methods through the quality-quantity’s dual lens (Punziano, 2016, p. 106, trad.eng.). Therefore, the second need equals to identify the source of the data which can be considered for various reasons and titles (obviously to be justified with respect to the choice made) appropriate to answer the research question. These sources must necessarily contain what in content analysis is called a communicative unit that corresponds to the referent of our investigation (Shreier, 2013; Croucher & Cronn-Mills, 2014). An inappropriate choice in this sense would mean that the researchers would not be able to find exactly the data they are interested in. In another way, the choice of the digital scenario as the main context unit (Krippendorff, 2018) already places the researchers in the reverberation of a precise framework of choices summarized in the Digital Methods approach (Rogers, 2019). If we also add to this issue the desire to extract secondary meanings from the elements present in this scenario, for obvious reasons the researchers must deal with the boundless size of this scenario in which specific containers of the communicative units of interest for the research being conducted cannot always be identified in a simple and linear way. Without this clear definition, all the elements concerning a given phenomenon could fall within the population of interest for the research, also because every content in the Net starts to exist in a precise moment but continues to exist for an indefinite time, potentially influencing all the subsequent content production. Yet the researchers always have in mind a temporal context of reference (which may correspond to the date of release of that content on the Net), the producers, arguments, specifications that lead to circumscribe and direct they gaze towards definable sources. All this set of focus is the heart of this phase. However, the researchers will be called upon to make an extremely precise choice of accurate sources as it can be likened to the preliminary definition of the field. This choice makes it appropriate to identify a specific reference population of content that will become the object of analysis and from which, subsequently, the researchers can implement the third phase of choice. This third phase consists in selecting the case of analysis consistently from the sources and the defined population. Classically, in the quantitative approach, the researchers need to break down the object of analysis by operationalizing the relevant concepts to identify the most appropriate indicators for the empirical detection of the different dimensions of the concept. In the qualitative approach, the researchers resort to the delimitation of the analytical dimensions of interest for the study by reinforcing a series of sensitized concepts that will be translated into appropriate conceptual maps for directing the researcher’s gaze. In the mixed approach, the research object can be broken down disjointedly for each set of inquiry, qualitative and quantitative, if the study is concurrent. While it will undergo a process called data transformation or translation modes (Creswell & Plano Clark, 2017) when the basic design is sequential and involves using the results obtained with one set of analytical procedures to construct, adapt and reinforce the results of the other set. Carried over to the digital scenario, these procedures require the adoption of a flexible model of articulation of concepts and conceptual dimensions. In other words, it means recovering the different articulations of concepts and dimensions trying to decline them 351

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with elements of the digital scenario that are somehow circumscribable, that means selecting appropriate keywords (but also hashtags, mentions, recognizable symbols, and so on) to identify exactly the contents of interest for the analysis, being able to make them identifiable elements and then perform a sampling or a targeted selection on them. This operation will be necessary if the number of elements identified is not technically and practically manageable, but every time the identification of content of interest should lead to the delimitation of populations of objects easily manageable and collectable. In all these cases it will be good to avoid sampling and selection and proceed with exhaustive analysis procedures. The identification of sources together with the selection of keywords become a crucial moment to determine the object of research and the objects under research. The fourth phase is the collection of digital data. In fairness, if the researcher’s study focuses on a few limited elements, the collection of these can also be done using manual copy and paste techniques to organize the information collected in the most suitable way for subsequent analysis. This means that if the analysis procedures to be carried out on this material are mainly linked to textual statistics or automated analysis, the material collected will be easier to manage if organized in relational matrices. If, in contrast, the intent of the researchers are to develop hermeneutic and more qualitative analysis procedures, the organization of the information collected can also be in the form of notes on a word processor. On the other hand, scraping techniques will be used whenever the sources referred to are organized with dynamic contents that require the creation of special algorithms trained to recognize the relevant contents to be extracted and to organize them in the most appropriate form for the intent of the research. API (Application Programming Interface) querying techniques will be used whenever the contents of the sources are organized in a static way and the proprietary content platforms provide the possibility to build query extraction procedures. In all these cases, in addition to the extraction of the main content (a text, an image, a video, or any other element subject to analysis and present on the digital scenario), contextual data can be extracted that will help in the subsequent processing of the collected materials. These context data can refer to two levels. The first is circumscribed to the main content and is expressed in all those natural digital indicators already present on the Net: we refer in these terms to the number of likes, number of reactions divided by types, number of shares, number of interactions and comments, number of views, and so on. The second level is meta-contextual and concerns the extraction of elements of the source that contains the main content of interest. In this case we refer to the name of the source, the number of subscribers/followers, the characteristics and technical content specifications and everything that may be of interest to the researchers. Finally, the last decision-making step that awaits the researchers who want to engage with digital mixed content analysis approaches concerns choices regarding data analysis. Many authors have focused on the analysis of contextual data related to content disseminated on the web developing articulated models of big data analysis (Jimenez-Marquez et al., 2019; Lewis et al., 2013) that indirectly manage to talk about issues such as reputation, sharing, engagement, trends, evolutions, and developments of dynamics related to that content, more in the form of reaction and interaction with it. Other authors (Rogers, 2019; Caliandro & Gandini, 2016) have instead explored the possibilities for qualitative analysis of content per se trying to avoid the possibility to fall into obtaining the dark effects of disconnection from the digital scenario within which this content is spread. However, the analysis procedures, quantitative or qualitative or both, that the researchers decide to adopt will depend on the hegemony of the research question (mixed perspective), and above all on the hegemony of the medium that conveys the contents taken into analysis (digital methods perspective). Regardless of these considerations, the content analysis process will consist of the coding of raw data according to a classification scheme. This scheme quantitatively 352

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claims to extend and generalize the results. Qualitatively, on the other hand, it will attempt to analyze the content more in depth. However, it is an understatement to think that a cognitive question on complex data such as the digital platform social data can involve only one of these sides. The Mixed Methods perspective is not only necessary, but mandatory. Of course, it is true that for researchers using data from social media platforms there are few guidelines for the data collection, analysis, and evaluation. The digital mixed content analysis approach should be seen as an organizational framework to guide the application of integrated methods of content analysis (quantitative and qualitative) but also integrated scenarios and objects of analysis (individuals, relationships and digital actions moving inside and outside the Net) on digital platform social data, and to address their varied nature.

RESEARCH DESIGN PROPOSALS AND EXAMPLES OF RESEARCH Wanting to address the choices that the researchers are called upon to make in circumscribed forms of examples of research designs, here it must be pointed out that in Mixed Methods strategies some extra points have to be defined. There are three points to analyze. The first concerns the sequence of implementation or temporal orientation between the sets for which the researchers will have to define the timing of the research design choosing whether to keep the procedures separate and combine them at the end of the research or to chain them in different ways along the course of the research integrating at different points the two souls of the research. The second involves the choice of priority or emphasis that require the researchers to choose which approach to prioritize or whether to try to find a balance between the two components. The third requires defining the stage at which integration occurs, which will be a direct result of the previous choices. The best-known and most widely accepted classification of mixed methods designs (Creswell et al., 2003) is based on the temporal sequence and purpose of integration. Here is how they can be distinguished: 1. Parallel convergent or triangular design. The methods are used simultaneously with the same priority and following the same steps. The analysis is separate, and interpretations are joint by integrating results, the goal is to enrich the understanding of the two data sources, corroborate results obtained from different methods, or compare multiple levels of analysis within a system. 2. Integrated or nested design combines collection and analysis of a secondary set of qualitative or quantitative data into a traditional qualitative or quantitative research design. ata collection can be either sequential or concurrent, analysis will first be separate and then initiate joint interpretation. The goal is to strengthen the primary data set that by itself does not appear sufficient in providing an adequate answer to the research questions. 3. Sequential explanatory or explanatory design starts from a quantitative research phase to develop a follow-up through a second qualitative phase. Quantitative results will be used to elaborate qualitative questions, set up sampling and data collection. The objective is the clarification and deepening of the obtained results. 4. Sequential exploratory or exploratory design starts with an exploratory qualitative phase in order to adequately inform the second quantitative phase by specifying the research questions and the variables that will guide it. The goal is to shed light on the lack of theoretical and/or empirical knowledge of a phenomenon.

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In order to use this classification for the framing of digital platform social data use in the context of the combination of digital and mixed approach in the content analysis procedure, some operational examples can be formulated. For example, imagine we want to develop a parallel convergent or triangular design to analyze the system of preferences and opinions that develops on the scenario of digital social platforms in a preelection period. One could use together an automatic content analysis on the texts extracted from the social platforms following the hashtags related to the candidates and their deployments. Multidimensional analysis procedures, such as lexical correspondence analysis (Lebart et al., 1998), could be applied on the identified texts to trace latent dimensions of meaning that are configured in social chatting leading to specific diffusions of users’ arguments, opinions, and perspectives. Alongside this procedure, a qualitative procedure of hermeneutic content analysis could be constructed (Bergman, 2010) in relation to the iconographic material present in the extracted materials following the same hashtags. These procedures would lead to peculiar results that give equal status to the two approaches used in parallel. However, while the first approach would lead to synthesize the main argumentative modes spread by different users with respect to the same object of discussion, with the second one we could highlight the emotional levers that push taken different positions towards the object of analysis. The analysis remains separate, and the interpretations would join in the final integration of the result, reaching the goal of better understanding of the two different points of view related to this design. Maintaining the same object of study, developing an integrated design or nested design would change the structure of the analytical process. For example, one could think about extracting the same dataset (still following the usual hashtags of the previous example). An automatic topic modeling procedure (Allahyari et al., 2017) could be developed on this set that could lead to identifying the most debated topics during the pre-election period to somehow monitor the topics that cause the most rupture and polarization. To reinforce this hypothesis that links the topics to their polarized treatment, a qualitative investigation could be conducted on a small portion of material for each detected topic to precisely identify the polarizations. This procedure, which sees a preponderance of the quantitative approach, pursues the objective of reinforcement, typical of this design, by integrating the results of a secondary set of analyses with those obtained with the primary set, which did not appear solid enough to provide adequate answers to the cognitive question to which it was linked. By choosing sequential explanatory strategies, this research object could still be approached in different ways. On the dataset extracted following the defined hashtags, a cluster analysis (Lebart et al., 1997) could be developed following the principle according to which opinions spread on the web in the pre-election period can lead to the identification of different types of voters: those who already have their ideas well defined and clear, those who try to define them, those who are undecided and those who declare themselves completely disinterested. This preliminary quantitative operation would make it possible to develop a second follow-up phase to understand the reasons behind the different positions emerging in the groups outlined, clarifying the electoral prospects precisely in relation to the chosen design and the aims it pursues. Alternatively, by adopting a sequential exploratory design to work on the same object, one could start with a qualitative study on small data (Caliandro & Gandini, 2016) that tests a first qualitative data-driven recognition of themes, arguments, positions, and opinions expressed by users. From the recognition of these categories, one could then instruct specific supervised classification algorithms (Sen et al., 2020) specifying the principles that guide it and the levels of accuracy to be pursued. With a structure organized in this way, the objective pursued will be to shed light on the vastness of the different positions 354

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present on the digital scenario and expressed in social platforms by the users of the Net, building new empirical and theoretical knowledge on a particular phenomenon under investigation, such as the system of preferences and opinions in the pre-election phase. From these few examples it is possible to understand that the possibilities offered by the combination of the digital approach with the mixed methods approach can be varied and extremely diverse, the only dictate for the researchers will be to free their imagination, to think outside the box of disciplines and skills, and innovate processes, rules, uses and results that can be pursued.

CRITIQUES AND PROBLEMATIZATIONS FOR FUTURE SCENARIOS OF ANALYSIS APPLIED TO DIGITAL RESEARCH In these pages a proposal of approach has been put forward, clarifying the starting point to leaving the reader with the possibility of perceiving the vastness of the possibilities that this perspective opens for social research. However, many other reflections will have to be conducted from the methodological point of view of the construction of research designs dedicated to this type of study as well as the practical implementation of the operational procedures of this proposal. Space must also be left for reflection focused on the influence that choices in analytics and scraping can have on the real strength in achieving the objectives of a digital mixed content analysis research. In other words, it is necessary to be aware of the limits reached by the digital approach when it comes to digital platform social data analysis in a mixed methods content perspective. Here is a short overview on some of these issues. First, the representativeness of results which can be reached. Regarding this problem, one wonders what kind of inferences and generalizations can be made about results produced by data coming from the digital scenario, in which the individuals who operate have characteristics that are often very restricted compared to the real variety of individuals who express themselves outside this scenario? Socio-demographic characteristics, levels of ability and levels of availability of equipment and infrastructures, in fact, limit the variability of subjects on the Net compared to all those who move in the world, although the digital scenario obviously breaks down boundaries and grants knowledge that is potentially unlimited in time and space. But who is really represented on the Net? If this question may seem trivial in a study that prefigures itself as post-demographic, in reality the inferences that are drawn from the analysis of phenomena on the digital scene using data from social media tend to aim to talk about the content disseminated on the Net and, indirectly, to refer to those who produce them, to opinions, to fears and needs that they express. In doing that, researchers circumscribe, for example, targeted market niches, groups on which to propose policy interventions, cliques leaders of opinions or in which the concentration of processes of information and disinformation becomes particularly decisive, and to this list we can add an endless list of other purposes. Therefore, the researchers must be extremely careful about the representativeness of the results obtained with this approach and the potential inferences that can legitimately be drawn from them. A second reflection can be addressed to the assumptions of the algorithms used in content analysis procedures, precisely because most of the techniques of content analysis incorporate some assumptions about the characteristics of the language (e.g., meaning not contextualized or categorized without a perfect match). Although sophisticated systems of machine learning will be developed, as well as automatic classification, coding of specific dictionaries for the recognition of expressive polarities of sentiment and very complex algorithmic tools capable of self-learning and being trained to replicate the choices of the 355

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researchers, and as much as all these processes are assessable in terms of reliability and replicability, the perplexities that characterize the birth of content analysis and that underline the problems of accuracy and precision remain valid. Margins of error, often not so residual, remain especially in the analysis of large amounts of data from social networks and the question that arises is: how much trust can we put into these algorithms? How much, instead, should the human footprint demand the control and the breaking of the algorithmic black box? This reflection also leads to questions about how algorithms work for content analysis and the need to make them more accessible, explained and defined. Implicitly, there is the need to continue the process of hybridization on the figure of the social researchers that currently, in addition to addressing the figure of the data scientist, shows the increasingly contingent need for training in terms of computer sciences, informatics, algorithmic engineering and programming. On the other hand, many of the tools currently available to the researchers for the development of API queries or scraping techniques allow them to extract a lot of valuable information for an extensive exploration of the semantic field related to the analyzed phenomenon, and this information extracted, it should be emphasized, often can be retrieved at no cost. This is certainly a strong point for social researchers, especially when this does not rely on dedicated funding and is conducted independently by researchers. If one frames the whole process of constructing research designs in the digital mixed content analysis approach, however, it soon becomes clear that other problems arise on the horizon of the researchers. This approach is built by steps and integrations of successive phases in qualitative and quantitative perspective, big and small data, on contents and relationships, on processes and constructions. The need to contemplate designs capable of looking in all these directions, implies the construction of concatenated procedures in which the loss of information becomes inevitable. Even if this loss could be acceptable compared to what has been gained in cognitive terms and through the integration rise, it is important that in explicating the research path chosen in this approach, the researchers are able to problematize what they leave in the field and what they choose to analyze. Also, they need to explicit how much of this data is retained in the processes of analysis in extension and in the processes of analysis in depth, in the procedures of synthesis and in the procedures of interpretation. The strengths highlighted in this last part of discussion shown how the digital mixed approach can be the only one capable of contrasting and overcoming the limits encountered in each step and ensuring in the final return of the integrated result something unique, dense and extremely different from what each method could give to the researchers because it skillfully combined the power of synthesis with the richness of details in a model that can act as a guide in the analysis of complex phenomena that have their natural extension in the Net. Social research remains a simplification of reality even when it is able to analyze it through different perspectives and grazes. Clippings in research are always necessary and for the researchers being aware of this particularity is the key to critically reading the results produced without ever thinking that he can reach the Holy Grail of knowledge. Finally, resuming the reflection of Hesse-Biber and Johnson (2013), Mixed Methods approach see in the current scenario the proliferation of those models for interpreting and deriving critical insights, and rather than searching for new models, it is now the time for this approach to bring to the social digital turn. This statement could redirect the reflection conducted in this chapter towards more exquisitely epistemological and ontological, more than only methodological, plans. It is time to open the doors to paradigmatic connections to substantiate the innovative methodological procedures that the researchers can implement if they can imagine it.

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ACKNOWLEDGMENT This research received no specific grant from any funding agency in the public, commercial, or notfor-profit sectors. However, thanks for the contents produced should be addressed to the continuous reflection that the research group within the University of Federico II has been carrying out for years on methodological issues related to social research, innovative processes and developments closely related to social change and digital society as its direct emanation. This group is headed by Professor Enrica Amaturo, who has created a recognizable and populated school of which the writer is a part and to which many of the authors of this Handbook belong.

REFERENCES Allahyari, M., Pouriyeh, S., Kochut, K., & Arabnia, H. R. (2017). A knowledge-based topic modeling approach for automatic topic labeling. International Journal of Advanced Computer Science and Applications, 8(9), 335. doi:10.14569/IJACSA.2017.080947 Amaturo, E., & Punziano, G. (2017). Blurry boundaries: Internet, Big-New Data and Mixed Methods Approach. In C. Lauro, E. Amaturo, M. G. Grassia, B. Aragona, & M. Marino (Eds.), Data Science and Social Research. Epistemology, Methods, Technology and Applications (pp. 35–56). Springer. doi:10.1007/978-3-319-55477-8_5 Bergman, M. M. (2010). Hermeneutic content analysis: Textual and audiovisual analyses within a mixed methods framework. In SAGE Handbook of Mixed Methods in Social and Behavioural Research (2nd ed.). Sage. Bryman, A. (2012). Social Research Methods (4th ed.). Oxford University Press. Caliandro, A., & Gandini, A. (2016). Qualitative research in digital environments: A research toolkit. Taylor & Francis. doi:10.4324/9781315642161 Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage publications. Creswell, J. W., Plano Clark, V. L., Gutmann, M. L., & Hanson, W. E. (2003). An expanded typology for classifying mixed methods research into designs. Handbook of mixed methods in social and behavioral research, 209-240. Croucher, S. M., & Cronn-Mills, D. (2014). Understanding communication research methods: A theoretical and practical approach. Routledge. doi:10.4324/9780203495735 Daigneault, P. M., & Jacob, S. (2014). Unexpected but most welcome: Mixed methods for the validation and revision of the participatory evaluation measurement instrument. Journal of Mixed Methods Research, 8(1), 6–24. doi:10.1177/1558689813486190 Fetters, M. D., & Molina-Azorin, J. F. (2019). A checklist of mixed methods elements in a submission for advancing the methodology of mixed methods research. Journal of Mixed Methods Research, 13(4), 414–423. doi:10.1177/1558689819875832

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Hesse-Biber, S., & Johnson, R. B. (2013). Coming at Things Differently: Future Directions of Possible Engagement with Mixed Methods Research. Journal of Mixed Methods Research, 7(2), 103–109. doi:10.1177/1558689813483987 Jimenez-Marquez, J. L., Gonzalez-Carrasco, I., Lopez-Cuadrado, J. L., & Ruiz-Mezcua, B. (2019). Towards a big data framework for analyzing social media content. International Journal of Information Management, 44, 1–12. doi:10.1016/j.ijinfomgt.2018.09.003 Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage (Atlanta, Ga.). Advance online publication. doi:10.4135/9781473909472 Krippendorff, K. (2018). Content analysis: An introduction to its methodology. Sage publications. Leavy, P. (2017). Research design: Quantitative, qualitative, mixed methods, arts-based, and communitybased participatory research approaches. Guliford Press. Lebart, L., Salem, A., & Berry, L. (1997). Exploring textual data (Vol. 4). Springer Science & Business Media. Lebart, L., Salem, A., & Berry, L. (1998). Correspondence analysis of lexical tables. In Exploring textual data (pp. 45–79). Springer. doi:10.1007/978-94-017-1525-6_4 Lewis, S. C., Zamith, R., & Hermida, A. (2013). Content analysis in an era of big data: A hybrid approach to computational and manual methods. Journal of Broadcasting & Electronic Media, 57(1), 34–52. do i:10.1080/08838151.2012.761702 Lupton, D. (2015). Digital sociology. Routledge. Marres, N. (2017). Digital sociology: The reinvention of social research. John Wiley & Sons. Punziano, G. (2016). I disegni e le strategie di ricerca Mixed Methods. In I Mixed Methods nella ricerca sociale. Carocci. Punziano, G., De Falco, C. C., & Trezza, D. (2021). (in press). Digital mixed content analysis perspective for the study of digital platform social data: An application on the analysis of the COVID-19 risk perception in the Italian Twittersphere. Journal of Mixed Methods Research. Rogers, R. (2009). The end of the virtual: Digital methods (Vol. 339). Amsterdam University Press. doi:10.5117/9789056295936 Rogers, R. (2019). Doing digital methods. Sage (Atlanta, Ga.). Salganik, M. J. (2020). Bit by bit. La ricerca sociale nell’era digitale. Il Mulino. Schreier, M. (2012). Qualitative content analysis in practice. Sage publications. Sen, P. C., Hajra, M., & Ghosh, M. (2020). Supervised classification algorithms in machine learning: A survey and review. In Emerging technology in modelling and graphics (pp. 99–111). Springer. doi:10.1007/978-981-13-7403-6_11 Teddlie, C., & Tashakkori, A. (2011). Mixed methods research: Contemporary issues in an emerging field. In Handbook of qualitative research (4th ed., pp. 285-300). Sage.

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Tittle, C. R., & Hill, R. J. (1967). Attitude measurement and prediction of behavior: An evaluation of conditions and measurement techniques. Sociometry, 30(2), 199–213. doi:10.2307/2786227 PMID:6044178

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Chapter 22

How to Study Online Networking:

The Role of Social Network Analysis Fabio Corbisiero University of Naples Federico II, Italy

ABSTRACT Social media and social networks are pervasive in the daily use as well as in a number of applications. Social media and social networks are also intertwined, as the social medial platforms also offer the opportunity to develop and analyze social networks. Over the past two decades, there has been an explosion of interest in network research through social network analysis. Network research is “warm” today, with the number of articles on the topic of social media and social networks nearly tripling in the past decade. This interweaving has been a further breakthrough within field research yielding explanations for social phenomena in a wide variety of new ways. Social network analysis (SNA) has been recognized as a powerful tool for representing social network structures and information dissemination on the web. Here, the authors review the kinds of things that sociologists have tried to explain using social network analysis and provide a nutshell description of the basic assumptions, goals, and explanatory mechanisms prevalent in the field, with emphasis on SNA research methodology.

INTRODUCTION One of the far-reaching explanatory schemes in contemporary sociology focuses on the concept of social structure that refers to patterning in social relations. Sociologically, the concept of structure may be used either to refer on the larger scale to the actors (group) of reciprocally defined social categories that are seen to comprise some social whole, or it can be used to refer to smaller scale social structures as configurations of concrete relationships among actors (individuals) without reference to a level of a larger societal totality. Most important for the science of social structure was what Simmel explained in «The Intersection of Social Circles» (1955): An actor may stand simultaneously in multiple social groups that overlap with each other at the site of that actor. Society is a “web of relationships” that is DOI: 10.4018/978-1-7998-8473-6.ch022

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not organized into a unified and harmonious set of structures but into multiple overlapping, often uncoordinated, and sometimes conflicting, ones (Simmel, 1992). With the exponential rise in popularity of online social networks (OSNs) in recent years, there have been a number of scholars who study and measure the topological properties of such networks. It is not surprising that the need of characterization or comprehension of interconnectedness between social reality and world-wide web has been a central issue for sociologists. Social media and social networks are pervasive in the daily use as well as in a number of applications. Social media and social networks are also intertwined, as the social medial platforms also offer the opportunity to develop and analyze social networks. Due to this interconnections there has been an explosion of new interest in social network research through the approach of Social network analysis (SNA). Social network research is back “warm” today, with the number of articles on the topic of on line social networks nearly tripling in the past decade. For sociologists, this interweaving has been a further breakthrough within field research yielding explanations for social phenomena in a wide variety of new ways. Social network analysis has been recognized both as a powerful paradigm and as a technical tool for representing social network structures and information dissemination on the web. Traditionally, studies on “real” social networks have shown interesting properties such as “small world” or “six degrees” of separation effect (Milgram, 1967) or “scale-free effect” as well (Castells, 2004). A network is a structure of social actors who interact, collaborate, influence, and networks help to form a basis for shared norms, identity, and collective behavior and are present in different forms within off and online worlds. This perspective is well exemplified by the Stanley Milgram theory about the six degrees of separation effect (ib., 1967; Travers & Milgram, 1969). This theory analyses the probability that two randomly selected individuals would know one another by having participants forward a mailed letter intended for a target person. Of those letters that reached the target, the average number of exchanges was six. The experiment was interpreted as showing that all people are connected to one another by an average of six degrees of separation. This small-world concept has also been demonstrated in a replication of Stanley Milgram’s original study via an email-based social research study to examine chains in forwarded email messages attempting to reach 18 target individuals across 13 different countries (Portnova, FrazerLock, Ladd, & Zimmerman, 2007). Moreover, scientists have demonstrated similar patterns on online social networks for small world phenomenon (Leskovec, 2008) or for scale-free effect (Bichler, 2008). The nature and nomenclature of the connections may be rather heterogeneous, especially if we are talking of online ones. Within the most recent sociological literature the online social networks have been defined as follows: «web-based services that allow individuals to construct a public or semi-public profile within a bounded system» (Ellison, 2007, p.210). Furthermore, different social researchers named these networks differently, calling them: «computer-supported social networks (CSSN)» (Wellmann et. Al., 1996), «online social networks» (Chiu et al., 2008), «web-based social networks» (Golbeck, 2006), «web communities» (Flake et al., 2000), or «virtual communities» (Adamic, Adar, 2003). Facebook, Twitter, LinkedIn, Instagram, Snapchat, Pinterest, Reddit and all the other “virtual” social networks we know have become one of the most vibrant objects of study of the Social network analysis. Social research has made clear that the online social networks are seamlessly embedded within real communities and are rarely a separate second life in themselves. Some scholars argued that Internet and web sites not only enables actors to maintain and strenghthen existing real ties but they also aid some to forge new ties (Boase et. al. 2006). In this chapter, we review the kinds of things that sociologists have tried to explain using social network analysis and provide a nutshell description of the basic assumptions, goals, and explanatory mechanisms prevalent in the field, with particular emphasis on research methodology and online social networks. 361

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SOCIAL NETWORK ANALYSIS: THE POWER OF CONNECTIVITY The social network analysis approach was born in the ’30s as a methodology to support a group of sociologists, who wanted to study people, organizations or other entities not as structures in their own right but as interconnected groups. The aim of those scholars was to analyze multiple relationships within social structures and find out how they could affect social behavior. SNA is a scientific discipline not to be confused with Network Analysis: in fact, there are many shared concepts and methods, but they have had parallel developments. The first deals with social entities, the second with everything that can be represented with a graph. The theory of graphs, in fact, is the basis of both. One of the most powerful ideas in the SNA approach is the notion that individuals are embedded in a dense network of social relations and interactions. Social network theory provides an answer to a social question: how autonomous individuals can come together to create durable and functioning companies. SNA provides explanations for a myriad of social networks phenomena, from individual to corporate creativity profitability. We can recall here the magnum opus of Randall Collins “The Sociology of Philosophies: A Global Theory of Intellectual Change” (1998) where the sociologist argues that global intellectual change advances according to a very similar pattern with a handful of philosophical schools (no more than six at the time) dominating public attention. These intellectual networks tend to rely on intergenerational chains and their influence is dependent on profound disagreements with the competing schools of thought. The construction of philosophical ideas is thus embedded within longitudinal social networks of the intellectual universe. Social network search is a very sensitive topic nowadays, with the number of articles in the “Web of Science” on the subject of “social networks” more than duplicated in the last decade. The earliest roots of social network analysis can be traced back to social psychology at the turn of the 20th century, but particularly what Jacob Moreno and Helen Jennings referred to as sociometry in the 1930s (Moreno & Jennings, 1938). Moreno’s perspective on SNA highlights sociometry as a form of physics, complete with its own “social atoms” and “social gravity” laws (Moreno, 1934). The idea of modelling social sciences in a social network way has its roots in Durkheim who argues that human societies are like biological systems in that they are made up of interrelated components; as such, the reasons for social regularities are found not in the intentions of individuals but in the structure of the social environments in which they are embedded. Moreno’s sociometry provides a way of making this abstract social structure tangible. Moreno’s sociograms are considered as the first examples of visualization of social networks, a hand-drawn image depicting friendship patterns between the boys and the girls in a class of school-children, presented at a medical conference in New York in 1933. Mapping the social affinities of a group of individuals, Moreno’s first sociograms visualized the relationships between pupils in a classroom. The question the pupils were asked was: Who wanted to be sitting next to whom? The children’s interpersonal choices were recorded in “sociograms” (Fig. 1) based on whom they chose to sit next to while studying or playing. Triangles were used to signify boys and circles for girls, with their initials in the middle. Sociological and mathematical interests of this work have already been discussed in thousands of publications, so we will avoid going into them here as well. It must be emphasized that from this point the fact that there is a “web-structure” within any group created tremendous opportunities for scientific research and, as we know today, for further opportunities (even that of getting rich) through modern social media that take advantage of the Internet. Through social media, a new era of social network analysis has been born, and so have new controversies. Coming back to SNA historical issue, a new and fundamental development in social network analysis was due to researchers from Manchester who pointed their attention at the effective configuration of rela362

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Figure 1. Moreno’s sociogram of a fourth-grade class

Source: https://slate.com/technology/2014/10/j-l-moreno-a-psychologists-30s-experiments-invented-social-networking.html (last access, 2021 feb. 27)

tionships deriving from power and conflict between individuals, instead of set up norms and institutions of a society (Scott, 1993). John Barnes, who first introduced the term «Social network» (Barnes, 1954), gave life to a remarkable formal development of the analysis of social structures. Developing Barnes and colleagues’ ideas, Nadel began his fundamental works, underlying the importance of a structural analysis rather than a contents analysis (Nadel, 1957) highlighting the concept of social role and the interrelation of independent networks. Mitchell introduced the difference between «total network» and «ego-centered» or «local network». Contemporary social network analysis got a huge burst of creative energy in the 1970s with the work of Harrison White and his students, including, among others, Ron Burt, Ron Brieger, Peter Bearman, Mark Granovetter, Kathleen M. Carley, Philip Bonocich, and Barry Wellman. These innovators revived the tradition of sociometry and infused it with a newfound theoretical and empirical rigor. Along with others in sociology, including Linton Freeman in the USA and Alain Degenne and Michel Forsé in UE, they developed the paradigms and the techniques that became the foundation of modern social network analysis. Authors such as Degenne and Forsé (1994) give a certain prevalence to the structural holistic

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paradigm of SNA, emphasizing above all the forms of sociability either formal and informal and the coefficients (such as homogamy and homophily) through which they can be measured. The techniques, in particular, were pushed further in the 1980s and 1990s at the University of California (Irvine Valley College), where social researchers refined, codified, and made the techniques publicly available. Most of the techniques developed by this new generation of network analysts has been captured in the classic text “Social Network Analysis: Methods and Applications” by Wasserman and Faust (1994), and have been translated into computer algorithms by Borgatti, Everett, and Freeman (2002) in UCINET. From this point the social network analysis is expressed and measured as patterns or in relationships among interacting units. After the expanding use of the Internet that created explicit awareness of social networks, scholars began to engage in network analysis with newfound enthusiasm (Watts, 1999). Computer science and software covered a fundamental role in this process, as they provided tools and techniques to automatize the process of gathering data from massive data sources and their analysis, at several levels.

SOCIAL NETWORK ANALYSIS: A QUALITATIVE OR QUANTITATIVE METHOD? Social network analysis is the branch of sociology where social analysts map and quantify the interconnectedness between actors in a network through a socio-mathematical approach. The main aim of this method is to understand the structural relations and to explain both why they occur and what their consequences are (Scott, 2012). The guiding sociological assumption of SNA is that the way used by the members of a group (individuals, institutions, organizations, cities…) to relate to each other affects some important characteristics of the interrelated groups, e.g. smartness, efficiency when performing a task, education, leadership (Knoke, 2008). This must be compared with the part of social sciences assuming that actors take decisions and act without regard to the behaviour of other actors. The oldest critical perspective about the approach is that this field lacks a theoretical understanding. It would be simply descriptive or just a methodological approach without a heuristic soul. It was above all Boissevain (1979) to point out how SNA is mainly focused on technical issues, rather than empirical research about social topics. On the contrary, Barnes and Harary (1983) have argued how graph theory is a powerful perspective still not used at its full capability. Researchers have made too little use of this theory and its full potentiality: «Graph theory uses two primitive, undefined terms, point and line; these two terms are mentioned in a small number of axioms, unproved statements assumed to be true. […] Its theorem consists of statements each of which can be derived logically either directly from the axiom system or indirectly by making use of theorems already proved. […] (A theorem) can be used with reference to any appropriate mathematical model of the real world that has been constructed with material from its axiom system. It than reveals real world implications of the model that might otherwise have not been noticed or utilized by the designer of the model». (p.239). Therefore, there is so much so to say about SNA that one of the main objectives of this chapter is to organize and simplify this flowering body of theory and methodological approach. First of all social network analysis emphasizes the importance of social structure as a way of characterizing the individual relationships, as we argued in the Introduction. Because social network analysts take these networks as the primary building blocks of the social world, they not only collect unique types of data, but they begin their analyses from a fundamentally non-mainstreamed perspective, moving away from that adopted by individualist or attribute-based social science. The patterns of relationships between individuals influence outcomes among members just as outcomes among members are influenced by 364

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their position within the wider network structure (Degenne & Forsé, 1994; Borgatti, Mehra, Brass, & Labianca, 2009; Krause, Croft, & James, 2007). This approach broadly depicts individuals as embedded in connections between network members. SNA seeks to understand the patterns in these connections (Scott, 1988), and social network data can be imagined as a social relational system composed of social actors and their interactions. The use of social ties is useful to answer to question such as: how densely are the network actors connected? Yet, how evenly are the network characteristics distributed? And, how are the personal attributes distributed in the space of network? As previously mentioned, social network analysis can occur at many conceptual levels of analysis, and thus, the network actors (also called nodes) can be individuals, families, communities, organizations, or even nations or states. Likewise, the interactions connecting actors can take a huge array of forms, including friendships, sexual partnerships, institutions and values that join families, business organizations, friendships… For example, a traditional approach to understanding smart cities (Sassen, 2000; Sassen, 2005) – cities that use technology to provide services and solve urban problems – would focus on the high levels of technological tools, artificial intelligence, education and “common skills” in the city context; characteristics of the relevant actors, organizations or individuals. By contrast, the social network analytic approach to understanding the same phenomenon would draw attention to the ways in which smart cities have create connections between organizations, technologies and individuals. Thus, people moving from one smart city to another one bring along collective smartness: ideas, expertise, knowledge, social capital and their social connections they have made within the urban context. This pattern of connections between cities, in which each city is tied through its inhabitants to multiple other cities, allows each to draw on diverse sources of smartness. Since combining previously embedded and connected ideas and technologies uses is the heart of the smartness, this pattern of connections – not just the single set of individual actors – leads to accelerating rates of innovation in the urban areas where it occurs (Corbisiero, 2013). Consequently, in any social network analysis the issue is whether to study a focal actor and the relations that surround that person (i.e., egocentric analysis; also known as personal network analysis) or to consider a population of actors and their interconnected relations (i.e., sociocentric analysis, or whole network analysis). The nature of SNA within discussions about quantitative, qualitative, and mixed methodologies is ambiguous. There have been discussions whether the SNA is a quantitative or qualitative method (Edwards, 2010; Hlebec & Kogovšek, 2006). Some authors describe the evolution from the qualitative approach in the early beginnings of the SNA, to the development of advanced mathematical methods and computer programs that led to a predominance of quantitative approaches, and finally, the mixed approach which combines both (Crossley & Edwards, 2016; Hollstein, 2014). Now, an increasing number of social network researchers makes use of mixed methods to generate their findings (Froehlich, Rehm, et al., 2020) because each of these methods has its own set of strengths and weaknesses (Crossley, 2010). Quantitative approaches map and measure networks by simplifying social relations into numerical data, where ties are either absent or present. Qualitative approaches, on the other hand, enable to consider issues relating to characteristics of social ties (Hollstein, Pfeffer & Soeffner, 2010). Although quantitative network analysis provides valuable information on social network trends and shapes and the role of influential individuals, it may not be sufficient in depth to uncover the subtle mechanisms by which social networks shape, emerge and influence social processes. The limitation of quantitative network analysis in providing comprehensive insight into underlying social processes has also been noted by other scholars (Neergaard et al., 2005; Corbisiero, 2007).

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Figure 2. Egocentric and sociocentric networks

Conversely, qualitative SNA often lacks an overview of the structural properties of a network. However, combining qualitative and quantitative network analysis in the form of a mixed-method design is still an emerging field (Salvini, 2007; Trobia, 2014). Every use of mixed methods in SNA has a particular aim for mixing, which is based on the researchers’ wish to raise the quality of their study (Schoonenboom et al., 2018). In Italy some authors (Forsé, Tronca, 2005) identified a particular combination of quantitative and qualitative methods, called «structural interactionism» applying it to the paradigm of social capital. Continuing on with this methodological review about SNA, one of the key undertakings of this method in the social sciences is the use of graph-theoretic properties that characterize clusters, cliques, structures, positions, and dyadic dimensions to explore the overall shape and interconnections of the network and for the calculation of many network structural indicators (Borgatti et al., 2009). Graphical presentation is a common practice in SNA and it often involves visual presentation of the network as dots connected by lines, called a “network graph” or “sociogram”. The reliance on mathematical and computational models and graphical imagery are now the building blocks defining modern SNA (Freeman, 2004). Drawing clusters or group answering questions such as: how are actors in a network clustered together? Are some actors more connected to each other? Who are the individuals with similar social positions? Once a social network is extracted from the original data source (smart phones, social media, surveys, communication networks, databases…), it must be stored in structured form so that automatic analysis, retrieval, and manipulation are possible. Many social networks exhibit community structure. Communities are groups of nodes that have different levels of connectivity within the groups. Communities roughly correspond to organizations and groups in real social networks. Graph exploration and nodes manipulation through SNA techniques (i.e. UCINET or NodeXL) is used for analysis and visualization of these communities. For example, it can be possible to program ties force-directed calculations by positioning the nodes in a two or three-dimensional space and by rearranging these nodes themselves as small magnets, where they repulse and ties attract (Jacomy et al., 2014). The SNA via graphical representation helps the social analyst to play with the software algorithms such as indicating shock or appealing strengths within the

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layout or also changing the colors, sizes, and shapes of the nodes and ties, to a point that the analyst is happy with how the chart tells their intended story most strikingly. Figure 3. SNA graphs layouts with different types of forces Source: Jacomy, Venturini, Heymann, Bastian (2014)

In SNA layouts certain nodes are labeled as “central” or “peripheral” based on the centrality of their positions in the network (Freeman, 1978), or “bridging” or “constrained” based on their roles in connecting or unconnected single nodes and clusters of nodes (Burt, 2000) or in the mutual help and social capital networks (Degenne & Lebeaux, 1991; Lin, 2001), as showed in fig. 3. Similarly, the network ties are labeled as “weak” or “strong” based on various qualities of the relationship or the peripheral/central position of the nodes. Granovetter’s analysis (1974) suggested differing levels of efficacy between strong and weak ties. Weak ties help find jobs that respondents found more satisfying than job positions found through strong ties. Still, the graphical criteria of SNA show that individuals sharing the same relations can be grouped even in the absence of shared links. In this case, social clusters are labeled as “cohesive” based on the level of connectedness, the extent of mutual connections, and the strength of the relations (Alba, 1973). Even though several mathematical techniques have been developed to assist with this labeling, interpretation often involves classification based on arbitrary cut-points on a numerical scale and subjective description of groups, as a result of a balance between sociological assumptions about underlying social dynamics and observed composition of network. At the level of node’s analysis, the most widely studied concept is “centrality” that is a group of node level properties relating to an actor in the social network. From the lens of online social networks this concept and its evaluation by SNA is fundamental. For example in the lifestyle of young generations it is important to socialize with the “real” influencers. Influencers have become a central part of social media for young people, and social media is an increasingly central driver of consumer decisions (Gillath et al., 2017). Measuring and quantifying the prestige of an influencer is relevant for a wide sets of social actors, especially scholars, because they may touch a large scale of audience with a very small cost and time (Friedl et al., 2010). In SNA technique centrality is such an important index as it indicates which node takes up critical position in a whole network. Central positions always get equated with remarkable popularity or excellent leadership in a social network (Krackhardt, Brass, 1994). As soon as the social actor gets a higher centrality, it mean

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Figure 4. An unconnected node

this actor gets closer to the center of network and has higher power and influence on the whole network. One of the best-known types of centrality is “Freeman’s betweenness”, which describes the property of frequently lying along the shortest paths between pairs of nodes (Borgatti, 2003, p. 271). This measure shows which nodes are “bridges” between nodes in a network. It does this by identifying all the shortest paths and then counting how many times each node falls on one. Some measurements of centrality have recently been developed and analysed to address the issues related to online communities. Marcos-Garca, Martinez-Monés (2015) recently used SAMSA – a software that combines SNA visualization with interaction analysis – to show how to automatically recognize various collaboration roles and then include a framework for supporting those roles that are customized to the participants’ needs. Using SNA to research the nature of online groups, Rabbany and Takaffoli (2011) classified active and inactive students, detecting students who are central to the flow of information in discussion forums. Their approach has been used to advise teachers and students of the surveyed schools on the course’s flow. Similarly, Bakharia and Dawson (2011) and Lockyer, Heathcote (2013) used SNA to identify interactive and isolated students within group projects in online classes. Additionally, their research extended to identifying the emergence and evolution of undesirable instructor roles during knowledge sharing where interactions are dominated by instructors albeit being expected to be distributed among participants. In addition, this research plumbed the emergence and evolution of unfavorable instructor positions during information sharing, where instructors dominate interactions despite the fact that they are supposed to be distributed among participants. These researches applied several SNA centrality measurements: “in-degree”, “out-degree”, “closeness”, “betweenness”, and “eigenvector”.

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FINAL OBSERVATIONS I have contended in this chapter that social network analysis is the natural way to refine and explore empirically several central ideas in the study of online social networks. To be sure, not all social networks and social media are supported or mediated through social networks themselves; social attitudes embedded in real behaviors or transmitted by socialization agencies may operate at least somewhat independently. However, as we have seen, a large body of scientific work through the SNA has developed out of efforts to give concrete expression to online relational data. The increased availability of large-scale, real-world sociographic data has ushered in a new era of social research and development in social network analysis. The quantity of content-based data created daily by Internet and traditional and new social media provides great opportunities and unique challenges for the SNA in the era of “Big Data.” The popularity of social networking sites, virtual communities, social network services, social network systems on the Internet, online social network…has made it possible to form online communities and share user-created content. Their main goal is to create, maintain and present social relationships to their users as well as match them with each other through self-expression (maintenance of personal profiles), including presentation of personal achievements, striking up relationships with others and mutual communication. To achieve it, they make use of some additional communication services like emails, chats, instant messaging, videos, blog, comments, testimonials, photo/movie album, etc. In the words of Boase et. al. (2006:2): «In a social environment based on networked individualism, the internet’s capacity to help maintain and cultivate social networks has real payoffs. Our work shows that internet use provides online […] a path to resources, such as access to people who may have the right information to help deal with a health or medical issue or to confront a financial issue. Sometimes this assistance comes from a close friend or family member. Sometimes this assistance comes from a person more socially distant, but made close by email in a time of need. The result is that people not only socialize online, but they also incorporate the internet into seeking information, exchanging advice, and making decisions». Typical examples of social networking sites are: Facebook for the college crowd or for Millennials, MySpace for the working crowd or LinkedIn for the business crowd. New generations’ social networks such as Instagram and TikTok are created to support youth or business actors. Overall, the more communication channels are served by the network, the better. This provides greater opportunity to create some new and maintain the existing relationships within the system. As such, SNA is suitable for the study and possibly monitoring of online interactions as it can automatically analyze interaction data, bringing a bird-eye view of the group’s social structure, its interaction patterns, as well as the mapping of all communications in the relational space. The sociogram depicts actors (nodes in SNA terms) as points and relationships (edges in SNA terms) as arrows originating from the source of the interaction and pointing to the target of the interaction. SNA may help to visualize the interactions among participants and may reveal who the main actors are in the interactions and who the isolated ones, which groups show dense interactions or sparse interactions that may need support. It may also reveal the active brokers who are participating and interacting within online communities, and the extent of their interactions in space and time. Although a brief overview such as this paper can only serve as an introduction to the complexities of social network analysis approach, we hope that readers will be inspired to join in this emerging use of social network analysis in the study of online social networks. Sociological surveys and qualitative researches about social media and online social networks should triangulate data from multiple Internet sources and collecting information through big data methods. The automated techniques and artificial 369

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intelligence tools can be used to transform even “unstructured” Internet data into (real) social network data. Social network analysis should ideally suite for those who are looking to explore avenues for social change, tailoring interventions to include a variety of network factors impacting social behaviors. This is an exciting area of sociological research and we can expect future studies to provide improved accuracy using the combined formula Web-SNA.

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Chiu, P. Y., Cheung, C. M. K., & Lee, M. K. O. (2008). Online Social Networks: Why Do “We” Use Facebook? In The Open Knowledge Society. A Computer Science and Information Systems Manifesto (pp. 67–74). Springer. doi:10.1007/978-3-540-87783-7_9 Collins, R. (1998). The Sociology of Philosophies: A Global Theory of Intellectual Change. Belknap Press of Harvard University Press. Corbisiero, F. (2007). Network analysis: metodi e tecniche. In A. Anastasi (Ed.), Reti sociali, regolazione e nuove forme della politica locale. FrancoAngeli Editore. Corbisiero, F. (2013). Comunità omosessuali. Le scienze sociali sulla popolazione LGBT. FrancoAngeli Editore. Crossley, N. (2010). Networks and Complexity: Directions for Interactionist Research? Symbolic Interaction, 33(3), 341–363. doi:10.1525i.2010.33.3.341 Crossley, N., & Edwards, G. (2016). Cases, Mechanisms and the Real: The Theory and Methodology of Mixed-Method Social Network Analysis. Sociological Research Online, 21(2), 217–285. doi:10.5153ro.3920 Degenne, A., & Forsé, M. (1994). Les réseaux sociaux: Une analyse structurale en sociologie. Sociologie du Travail, 38(4), 622–624. Edwards, G. (2010). Mixed-method approaches to social network analysis. National Centre for Research Methods. Flake, G., Lawrence, S., & Lee Giles, C. (2000). Efficient identification of web communities. In Proceedings of the Sixth ACM Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. Forsé, M. & Tronca, L. (2005). Interazionismo strutturale e capitale sociale. Sociologia e politiche sociali, 8(1), 7-22. Freeman, L. C. (1978). Segregation in Social Networks. Sociological Methods & Research, 6(4), 411–429. doi:10.1177/004912417800600401 Freeman, R. E. (2004). The Stakeholder Approach Revisited. Zeitschrift für Wirtschafts-und Unternehmensethik, 5(3), 228–241. doi:10.5771/1439-880X-2004-3-228 Friedl, D. M. B., & Heidemann, J. (2010). A critical review of centrality measures in social networks. Business & Information Systems Engineering, 2(6), 371–385. doi:10.100712599-010-0127-3 Froehlich, D. E., Van Waes, S., & Schäfer, H. (2020). Linking Quantitative and Qualitative Network Approaches: A Review of Mixed Methods Social Network Analysis in Education Research. Review of Research in Education, 44(1), 244–268. doi:10.3102/0091732X20903311 Gillath, O., Karantzas, G., & Selcuk, E. (2017). A Net of Friends: Investigating Friendship by Integrating Attachment Theory and Social Network Analysis. Personality and Social Psychology Bulletin, 43(11), 1546–1565. doi:10.1177/0146167217719731 PMID:28914161

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Moreno, J. L., & Jennings, H. H. (1938). Statistics of social configurations. Sociometry, 1(3/4), 342–374. doi:10.2307/2785588 Nadel, S. F. (1957). The Theory of Social Structure. Cohen & West Ltd. Neergaard, H., Shaw, E., & Carter, S. (2005). The Impact of Gender, Social Capital and Networks on Business Ownership: A Research Agenda. International Journal of Entrepreneurial Behaviour & Research, 11(5), 338–357. doi:10.1108/13552550510614999 Pappi, F., & Scott, J. (1993). Social Network Analysis: A Handbook. Contemporary Sociology, 22(1), 128. doi:10.2307/2075047 Rabbany, R., Takaffoli, M., & Zaïane, O. F. (Eds.). (2011). Analyzing Participation of Students in Online Courses Using Social Network Analysis Techniques. Educational Data Mining. Salvini, A. (2007). La costruzione dei dati relazionali nelle reti sociali. In Analisi delle reti sociali. Teorie, metodi, applicazioni (pp. 179–202). FrancoAngeli Editore. Sassen, S. (2000). The Global City: Strategic Site/New Frontier. American Studies (Lawrence, Kan.), 41(2/3), 79–95. Sassen, S. (2005). The Global City: Introducing a Concept. The Brown Journal of World Affairs, 11. Schoonenboom, J., Johnson, R. B., & Froehlich, D. E. (2018). Combining Multiple Purposes of Mixing Within a Mixed Methods Research Design. International Journal of Multiple Research Approaches, 10(1), 271–282. doi:10.29034/ijmra.v10n1a17 Scott, J. (1988). Social Network Analysis. Trend Report. Sociology, 22(1), 109–127. doi:10.1177/0038038588022001007 Scott, J. (2012). What is Social Network Analysis? Bloomsbury Academic. Simmel, G. (1955). Conflict and The web of group-affiliations. Free Press. Simmel, G. (1992). Sociology: Untersuchungen über die Formen der Vergesellschaftung. In O. Rammstedt (Ed.), Georg Simmel Gesamtausgabe (Vol. 11). Suhrkamp. Travers, J., & Milgram, S. (1969). An Experimental Study of the Small World Problem. Sociometry, 32(4), 425–443. doi:10.2307/2786545 Trobia, A. (2014). Web mining e Application Programming Interfaces: caratteristiche, strumenti, prospettive e limiti. In C. Corposanto & A. Valastro (Eds.), Blog, FB & TW. Fare ricerca quali-quantitativa online (pp. 67–104). Giuffrè. Wasserman, S., & Faust, K. (1994). Structural analysis in the social sciences. Social network analysis: Methods and applications. Cambridge University Press. doi:10.1017/CBO9780511815478 Watts, D. (1999). Networks, Dynamics, and the Small‐World Phenomenon. American Journal of Sociology, 105(2), 493–527. doi:10.1086/210318

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Wellman, B., Salaff, J., Dimitrova, D., Garton, L., Gulia, M., & Haythornthwaite, C. (1996). Computer networks as social networks: Collaborative work, telework, and virtual community. Annual Review of Sociology, 22(1), 213–238. doi:10.1146/annurev.soc.22.1.213

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Dear (Digital) Diary:

Evaluating the Audio Diary Technique as a Research Method Veronica Moretti University of Bologna, Italy

ABSTRACT This chapter investigates how individuals interpreted and considered the audio-diary technique, understanding the interaction between the subject and the medium and the potential of new technological tools (e.g., smartphone, social network) in producing data. The research is based on a previous study conducted during the COVID-19 lockdown in Italy, more specifically, the transition from phase 1 to phase 2. Each participant—11 female and 6 male, between 28 and 45 years old, and living in the northern part of Italy—was asked to register one audio per day for a week (7-13 May). After this period, the author undertook a final follow-up semi-structured interview to evaluate how much the audio-diary had an impact both on people’s daily lives and on their way of expressing information. The data collected suggest a number of advantages and disadvantages to the use of audio-diary to collect individuals’ experience. The author will briefly describe the steps of AD technique by using the collected material (interviews) and what has emerged from the analysis of qualitative data.

INTRODUCTION: DIARIES AS A SOCIAL PRODUCT The use of a diary as a form of personal tale comes largely from literary narratives, in which collections of thoughts support the description of historical or social contexts, such as in the Anne Frank Diary – one of the most well-known memoires, describing a young woman’s life in hiding during World War II. Gogol, a Russian writer, also used this form in his Diary of a Madman, which chronicled the increasing delusions of the main character. In sociology, diaries can be a valuable research instrument, since participants can accurately describe their relational and social circumstances in the first person. This was the case in Jack Womack’s Random Acts of Senseless Violence, in which, using vivid, cyberpunk prose, Lola – a teenager from a comfortable family, attending an exclusive private school – described DOI: 10.4018/978-1-7998-8473-6.ch023

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through her diary how she was suddenly forced to move to the deprived area of Harlem with her family. By reading Lola’s diary, it is possible to observe how sociocultural factors (income and urban context) can influence an individual’s life trajectory. The writing style, initially fluid and grammatically correct, progressively deteriorates through exposure to a poor and deprived neighbourhood. Based on such precedents, diaries constitute a fruitful object of investigation for social scientists since they “have the potential to yield rich qualitative data and, unlike methods which rely on retrospection, offer the potential to ensure that accounts are sequentially ordered and reduce the likelihood of feelings or events being forgotten” (Williamson et al., 2015, p. 2). Social research has used diaries in different contexts. In ethnographic research, field notes can provide important insights into how individuals and communities develop relationships and live in ‘their’ world. In clinical research, diaries can help to enhance memory or recall problems. Merton and colleagues (1957) showed in their book The Student-Physician: Introductory Studies in the Sociology of Medical Education that diaries can be a powerful tool for the education and socialisation of medical students. Through excerpts from written texts, the authors examined how students perceived and evaluated their situation, presenting the same event (such as an exam or an interaction with a patient) differently. Another study conducted by Elliot (1997) illustrated the role of the diary-interview method to investigate the demand for formal and informal primary care. According to the author, using diaries offered a means to capture behaviour that was inaccessible through participant observation. What is the potential of diaries within social research? Latham highlighted that a “diary becomes a kind of performance ... the methodological focus shifts to plugging into (and enabling) respondents’ existing narrative resources” (2003, p. 2002). The narratives in diaries, unlike interviews and focus groups, do not consider the physical and temporal spaces shared with the researcher. The diary creates a dimension in which the person can be alone and his/her influence affects the entire collection of data. Alaszewski (2006, p. 2) emphasised four main characteristics of diaries. Firstly, a diary must present regularity since it is organised around a sequence of regular and dated entries over a certain period. Secondly, a diary is personal because the entries are made by an identifiable individual who controls access to the diary while he/she records it. Thirdly, diaries are usually contemporaneous since the entries are made at the time, or close enough to the time, when events or activities occurred. This is particularly useful for avoiding problems of recall. Finally, all diaries present a record of the entries that are considered relevant and important by the person who is writing. These entries may include events, activities, interactions, impressions, and feelings. The records can be presented in different forms: written documents or, with the development of technology, audio or audio-visual recordings. Nowadays, technology offers new opportunities for diary keeping. Diaries can therefore be created in a different manner, as paper diaries, audio diaries, or e-diaries; for instance, various blogs and weblogs provide access to personal diaries, as the website https://blogs.warwick.ac.uk exemplifies (Alaszewski, 2006). The ability to generate audio diaries is also facilitated by the widespread use of new digital devices (including smartphones), which simplify the collection and recording of impressions. Among the main studies that have used the audio-diary technique, Bernays and colleagues (2014) analysed how hope was built and maintained by 20 people living with HIV in Serbia. The authors illustrated the methodological contribution that audio diaries made to understanding the complexity of experiences of chronic illness over time. Another study (Worth, 2009) employed audio diaries to report how visually impaired young people in Great Britain approached adulthood. Analysing passages from a set of 22 audio diaries, Worth examined three key issues relating to the technique: how audio diaries can capture narrative in unique ways, how the method can be employed within a participatory framework, and how audio diaries ad376

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dress complex issues about the nature of an audience. Hislop and colleagues (2005) examined the use of audio diaries as an approach to researching sleep, asking 35 women to record entries each morning after waking up. However, audio diaries, unlike written ones, are not commonly used in contemporary qualitative research. It is fundamental to consider how technology might shape social research, since high-tech artefacts can be considered as an integral part of our ordinary routines; digital tools are becoming an everyday activity, routinely performed, which is also enforceable and holds the subject. This unstoppable interaction (human/technology) provides a theoretical basis for understanding how nonhuman actors interact with each other and the interplay of diverse actors in networks. Additionally, the human-technology relation is not a passive, neutral or hostile one. On the contrary it can be defined as a partnership: acceptance of mutual shaping. Humans and technology can flourish together, throughout an ‘architecture of choic’. The aim of this research was to evaluate the steps, potentials, and limits of the audio-diary technique for collecting data in a particular historical moment when the ubiquitous COVID-19 pandemic was progressively reshaping our everyday lives. The research was based on a previous study1 conducted during the COVID-19 lockdown in Italy; more specifically, the transition from phase 1 to phase 2. Participants were asked to register a whole week’s audio-diary recording during this period, beginning on 4 May 2020 (just as Italy completed phase 1 of its nationwide lockdown and entered the phase 2 partial relaxation of lockdown measures). After receiving the audio recordings from participants, the researcher realised that follow-up semi-structured interviews would be necessary to evaluate how greatly the audio diaries affected people’s daily lives and their ways of expressing information. The interviews began ten days after the conclusion of the diary keeping. Basically, this research built on the hermeneutic work of Ricoeur (1984), which aimed to comprehend a text from its meaning, based on what the person intended to say. In this research, in which participants’ experiences both explained and created who they were and how they saw themselves, it was important for the researcher to consider what the texts said and meant in order to understand them (Worth, 2009). The study established a solid link between explaining and understanding. This article can be considered a complete methodological text, since extracts from the interviews, and reflections on the adopted method, will take the reader on a journey inside and outside the audiodiary technique.

THE STRUCTURE OF A RESEARCH AUDIO DIARY Preliminary Assumptions The aim of the first study (Moretti, Chakraborty 2020) was to investigate how individuals selected, consumed, interpreted, and absorbed media content during the COVID-19 pandemic. The initial research concerning the topic of media habits was pivotal to explaining, in a clear and exhaustive way, the crucial steps taken by the researcher. The analysis of the final interviews led to reflection on the audio-diary technique (ADT). To investigate this phenomenon, each of the participants – 11 female and 6 male, between 28 and 45 years old and living in the northern part of Italy – was asked to register one audio recording per day for a week (7–13 May 2020). The content of the audio diaries was categorised according to four dimensions:

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• • • •

The kind of media the participants used to consume information Whether the participants experienced ‘data fatigue’ The experts the participants turned to for certainty in these uncertain times (e.g. politicians, scientists, public administrators, and international and supranational organisations such as the World Health Organization and the European Union) Whether and how participants recognised fake news on the internet, moderating their perceptions about the risk and impact of COVID-19.

Nevertheless, only through the final interviews was it possible to grasp some aspects of their experiences, above all the interactions between the subjects and the ADT. Participants had in-depth experiences of the technique, highlighting its positive and negative aspects and providing insights improving the data collection method. One aspect that definitely had to be considered was the period during which the research was carried out: the COVID-19 pandemic. The choice to adopt the audio-diary technique to document how our respondents received and selected information about the pandemic during that period led to some effects that became evident in the final interviews. For some interviewees, the choice to consider this transitional period – when the media were dominated by COVID-19 – led to the development of severe ‘COVID exhaustion’ (Moretti, Chakraborty, 2020), which spilled over into the topics addressed.

Selection, Sample, and Recruitment The present research followed what is known as grounded theory, “which involves a phased selection of settings with each round of data collection and analysis generating fresh insights that lead to the selection of new settings in which to test the insights and to fill gaps in the data and theories through theoretical sampling” (Charmaz, 2000, p. 519). The interpretative categories that emerged were not based on predefined theories but arose through an inductive and systematic analysis of the data (Lomborg, Kirkevold, 2003). Since this was an exploratory study preceding the research, as suggested by Denzin and Lincoln (2000), there was no need to use a representative sample. In naturalistic research, the concern is to select cases or settings that will provide an opportunity to gain the desired insight rather than make statistical inferences. For the audio-diary data collection, a non-probabilistic sampling was used, in line with the purpose of theoretical saturation, according to which no new data appeared and all concepts of the theory were well-developed (Morse, 2009). Subsequently 17 adults (11 female and 6 male, aged between 28 and 45 and living in Northern Italy) who had previously been involved in the media-habits study were interviewed. In this AD study, the approach is purely inductive since the interest is to generate theories instead of testing them; hence, participants are the prime interest and focus throughout the research. Following Hammersley and Atkinson (1995, p. 38), “the initial sample may be relatively arbitrary and related to practicalities such as ease of access or convenience as it is the starting point for further selection of cases”. Table 1 shows the ADT trend (i.e. how many participants sent audio recordings, on which days, and in which language).2

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Participant code

07/05/20

08/05/20

09/05/20

10/05/20

11/05/20

12/05/20

13/05/20

Language

(01)

X

X

X

X

X

X

X

ITA

(02)

X

X

X

X

X

X

X

ITA

(03)

X

(04)

X

(05)

X X

X

X

X

X

X

ITA X

X

X

ITA ITA

(06)

X

X

X

X

X

X

X

ITA

(07)

X

X

X

X

X

X

X

ITA

(08)

X

X

X

X

ITA

(09)

X

X

X

X

X

X

X

ENG

(10)

X

X

X

X

X

X

X

ITA

(11)

X

X

X

X

X

X

X

ITA

(12)

X

X

X

X

X

X

X

ITA

(13)

X

X

X

X

X

X

X

ITA

(14)

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(15)

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(16)

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(17)

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Almost all participants (14) sent the requested audio recordings during the week; however, the author decided to interview those (3 participants) who did not complete the task to understand their decision not to register recordings. In other words, researcher tried to investigate the obstacles and factors that can influence audio-diary keeping and delivery. The reasons why some participants did not record their experiences differed. Interviewee 3, for example, said that he only recorded when he had something relevant to report: No, I recorded two or three ... The only difficulty I encountered was, more than anything else, that I could not find the motivation to do it daily. Let’s say that my judgment was not linked to a single news item but to the trend that I found in that particular period. (3) Interviewee number 5 also failed to make daily recordings, but in this case, because she found the task tiring: Having returned from work late every evening, when I was at home I was very tired and it was hard for me to sit there and check all the news that had been announced. Then I probably also made a mistake in the recording because, in the first one, I touched on all the topics, whereas I could have chosen to address maybe one topic a day and it would have been much easier for me … It wasn’t a real job, but it was a task that weighed on me. (5)

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One of the central aspects of the technique is the time that subjects must devote to recording their digital diaries. It can indeed become a challenging task that discourages the initially engaged participants. As reported by two interviewees: It is important to choose who to involve from the beginning because someone can easily lose motivation. It would be wise to involve a motivated group with whom to carry out this research, as it is not easy. (2) I also found it difficult to do the vocals because I had moments that I didn’t really want to share, I did it only to fulfil the commitment I had made. (15) This might also represent a constraint for researchers, since the selection of the participants must be weighed carefully to avoid an early dropout rate. According to Corti (2003), diary keeping requires basic competence in, for example, literacy or the use of audio equipment. Additionally, it is important “to ensure that recording is accurate, precise and regular. If the diarist does not understand the importance of such concepts, then it is likely that the experiment will be compromised” (Brewer, 2003, pp. 235-6). Lastly, as previously mentioned, the researcher must make efforts to maintain high motivation because it is likely to decline with the passage of time. One possible strategy in maintaining motivation is to offer incentives (often symbolic). Participants may then experience what Cardano (2011) defined as ‘cognitive gratification’; that is, the feeling of having contributed to scientific research. This aspect was captured by the words of interviewee 6: I liked [the research] a lot; it made me feel an active part of something important because, as I mentioned earlier, I saw a lot of value in this research. I think it’s a very interesting topic and I liked it a lot. Involvement can therefore offer positive reinforcement, enabling participants to feel that they have contributed to scientific work.

Providing Instructions A pivotal step within the ADT is participant training to provide guidance on using diaries. Instructions are crucial because their absence can compromise the entire diary-keeping process, and conversely, their presence can guarantee its success. During the research on media habits, the participants were given clear instructions on how to complete their diaries: • • • • • •

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General information about the research (main purposes, methodology, etc.). Reference period: the first days of phase 2 of the pandemic (which started on 4 May 2020). Schedule: each participant was asked to register one audio recording per day for a week (7–13 May). How to record: since the researchers were not able to meet the participants face-to-face to provide them with formal equipment, the respondents were asked to create audio recordings with their own devices. When to record: whenever participants preferred, during the daytime. When to deliver: participants were free to send their audio recordings individually, at the end of each day, or together at the end of the investigation period.

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• • • • •

Audio duration: participants had complete freedom to decide the duration of the audio recordings (and the duration of the audio diaries varied widely, ranging from 1.34 minutes to 34 minutes). Languages: participants were free to record in English or Italian, according to their preferences. Item checklists: events or behaviours to help jog the diary keepers’ memories. Researcher contact details: email addresses and telephone numbers. Final notes in which participants were asked to be natural and sincere, suggested expanding on the proposed ideas, and requested their participation in follow-up interviews.

The instructions were perceived as supportive by many of the respondents, providing a form of roadmap for the diary-keeping task: They were written clearly enough to remember easily. (01) It was all pretty clear. I very much followed the guidelines you sent us. (10) In other cases, however, participants found the instructions difficult: In the first recording, the instructions slowed me down a lot because I wanted to answer all those questions following a general logical line. After the first registration, instead, I started giving the information that I received in my daily life and this allowed me to no longer feel ‘guided by many instructions’ but by the salient points relevant to me. This helped me to respond very instinctively to the period I lived in during the research phase. (06) Sometimes it was necessary to understand the content well: I had to read the instruction a couple of times because there were so many points. (16) Initial contact is crucial since, during the audio recording, the researcher (unlike during an interview) is not present and therefore has no opportunity to clarify unclear passages or address the doubts of participants.

Analysing the Diaries: Content, Structure, and Emotions When completed, the audio recordings were sent to the researcher. As Acton (2003, p. 51) showed, the added value of audio diaries was valuable compared to written documents, since “pauses, silences, overlaps, laughter, applause, tone and volume are just some features that are transcribed in an attempt to capture not only the content of talk, but also the way in which it is produced”. Using Nvivo 12 software, the author formalised each characteristic of a text and developed coding categories through a process of constant comparison (Charmaz, 2000). The data was not fitted to preconceived standardised codes; instead, codes while studying the data were created. The same procedure was adopted for the semi-structured interviews conducted at the end of the research. The information was transcribed verbatim and analysed. The emotional aspect of the diaries followed a daily trend, and the historically profound uncertainty linked to the pandemic was captured by the participants. In fact, interviews would only have caught the

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subject in a single moment, whereas the daily recording provided a ‘thermometer of sensations’, as reported by interviewee number 12, who when asked if she felt comfortable during the recording, answered, Well it depends … sometimes more and sometimes less, but this depended on the mood I was in at that moment. (12) Interviewee number 17 stated: Sometimes I was very tired and, as I told you, I had the feeling of not speaking well and even forgetting the things I wanted to say. (17) The emotional aspect should not be underestimated. The experiences captured by audio diaries can, in fact, be seen as arduous or even intrusive if a person is going through a difficult period on a personal level. Interviewee 9 reported precisely, among many things, that the tone of voice can make us aware of this: The first day I did an audio message on WhatsApp and the same evening I sent it to you. I sent the rest of them all together because I had some difficult days with my boyfriend. I felt like telling at that moment what had happened to me daily … and in the audio it was obvious that I sometimes had trouble sharing my thoughts. (9) In this type of research, it is therefore important to pay close attention to ethical issues. At the beginning of the research, the researcher explained to each participant that their audio diaries would only be handled by the research team, and individual entries would be anonymised (Sargeant, Gross, 2011). Diaries can provoke ethical concerns and cause participants some distress because the act of recording and reflecting on events may prompt an emotional crisis (Smyth, 1998).

VERBA MANENT: RESULTS AND DISCUSSION ABOUT AUDIO DIARIES At the end of the data collection period, to understand the reliability of the methodology, semi-structured interviews were carried-out. Interviews helped to expand several elements relating to the audio diaries, by asking how much the instrument affected both the participants’ daily lives and their ways of expressing information. Self-reflexivity is central to the whole research processes. Through the interview and the formalization of sociological categories, knowledge was constructed and situated, calling the researcher to take responsibility for their own positioning and the participant to reflect on the experience. Understanding how self-reflexivity may be impacted by the characteristics and experiences of the researcher is of paramount importance, since the research moves from the position of an outsider to the position of an insider in the course of the study (Berger, 2003). Participants had extensive experience of the AD technique, providing insights into improving the method and noting the positive and negative aspects. Participation was therefore intense and immersive because the participants not only engaged in the research but also reflected (in response to the researcher’s prompts) on the way the entire research was conducted.

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In this sense, it can be recalled the work of Woolgar and Neyland regarding Mundane Governance, a concept introduced in 2013. It can be defined a set of processes, practices and outcomes of ordinary activities, which are now understood and apprehended by people. The authors started from the assumption that objects (stuff, to use a term of their own) that we didn’t pay attention to before are instead essential in regulating our activities and our behaviours. With the result that our lives are being regulated and controlled by such mundane objects and technologies. It is a common experience that more and more different aspects and subcategories of objects are coming to prominence (Woolgar, Neyland 2013). Considering their increasing use, the author contemplates the technological artefacts (smrtphone and social network) we use in communicating as an integral part of our routine, therefore as mundane dispositisf. Objects, processes and outcomes are given meaning through repetitive actions and thinking. Considering the daily use of digital technologies, it is possible to argue that devices operate as mundane objects and subject within our routine.

‘The Banality of Audio’ Among the main results, one of the first central aspect concerns the potential of audio versus written diary keeping. Audio diaries can capture experiences in real time (Worth, 2009) and offer a practical hands-free method of recording. Additionally, audio messages are a part of contemporary everyday life, since we are living in a society where technology and digital platforms allow us to constantly connect with others. The possibility of using audio recording instead of writing was unanimously welcomed by the interviewees. For many, the most important aspect was that writing a document might be less spontaneous, as reported by these extracts: It was enough for me to sit on the sofa, and while I was talking, it was as if I was hearing you on the phone or was sending you a vocal message. I see writing almost as an exam; I don’t know how sincere one can be while doing a written diary. This way [audio] is absolutely spontaneous and sincere. (01) With the written diary, I would certainly have encountered many more difficulties as I do not have a personal habit of writing down my daily thoughts. I would have found it more difficult to write. Also, with the vocal form, you have more freedom to express yourself, while in the writing you must necessarily pay attention to the grammar, syntax, etc. (02) In writing, in my opinion, you suppress even more the possibility of really letting out all your thoughts, precisely because it requires greater effort to concentrate on both the form and the content and you are therefore less spontaneous. (16) Another aspect concerned the practicality of using audio, as reported by interviewee 3: ‘It is certainly more practical to make an audio recording than to turn pages. At least, it works for me’. According to some participants, audio recording can be done when and how a person wishes. In this regard, it is worth mentioning the importance of the setting. Since they were able to record in any location that suited them, the participants were comfortable in their environment (often the house in the evening):

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Yes yes! It is certainly better than having to write a page about a topic. It is much more comfortable to record audio, with the freedom to do it when you have time and something to say. (17) Writing can create barriers by imposing filters or ‘self-censorship’: Even though I love writing, I think there are fewer filters when you keep an audio diary because you can delete it if you don’t like it, but when you start talking, you keep doing it. With writing, I would probably have had more filters and I would have been less authentic and genuine. (06) Only two informants reported that writing was more desirable than an audio mode. One participant, on the last day of the recording period wrote down her impressions and then read them out: I found it a bit difficult to improvise off the cuff. I felt that I repeated things too much, so in the audio on the last day, I preferred to write what I had to say first ... It came out much better ... If I write my thoughts, I can be more organised and more precise. (05) I personally prefer to write than to speak. (10) This constituted a form of ‘writing out loud’.

Auto-Action Research In the final interviews, participants showed that they completely interacted with the AD technique, providing insights on how to address its positive and negative aspects. With this in mind, it was decided to name this section auto-action research, inspired by the first-person action research methodology, which can be defined as the way in which groups of people organise the conditions under which they learn from their own experience and make this experience fruitful for developing new skills (Lewin, 1946; McTaggart, 1997; Kemmis, 2009). Essentially, there is a link between knowledge and action, empowering the subject; such research, therefore, aims to contribute directly to changing a specific situation. In this context, the research modified some aspects of people’s everyday lives (in relation to information consumption) and supported critical reflection by the participants. In some cases, the participants declared that the technique influenced their way of presenting the information they collected: This experience made me change the formalisation of my thoughts, not the quantity of news I consumed daily. (04) On one occasion, it even influenced the acquisition of new habits with respect to information: I paid much more attention to it [information]. Normally, I don’t watch the news during the whole week; for example, I only want to hear it to obtain a precise report. Yes, these audios have influenced the way I acquire information’. (14) Audio diaries could support the maintenance of good habits, as claimed by interviewee 7:

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I also wanted to be more informed and to have richer content, which would otherwise have been a copy of the day before. This habit, however, I am not going to lose. I want to keep my daily informative interest higher. (07) Habits could even a stimulus for change: ‘It changed me in this respect; in fact it changed my daily habits’. (16) This was connected to a sense of awareness that the subject acquired through the periodicity of the audio recording. For some, this research prompted a ‘restitution of the self’ in the third person – a sort of vocal mirror through which to understand aspects of themselves: Yes, that’s right. I almost did a self-analysis, realising that maybe the past months I could have informed myself more or from more sources than I used to do. (1) I believe that this research was, for me, a remarkable mirror; it was how, in fact, I reflected myself in the everyday life I was living. I think this was very helpful in understanding myself better and getting to know myself more. (06) As Zimmerman and Wieder (1977) noted, participants involved in general diary-based research function in two analytically distinguishable roles: naive performer and reflective informant. As a performer, a person ‘moves through his or her normal activities “as if” the observer were not present, which is to say, “naturally”’. The person is also an informant, however, because he/she reflects on their own and others’ performances. Sometimes, such awareness can generate negative feelings, especially if the restitution of our image is unsatisfactory: Probably what struck me most was the fact that today, at 30, I remember that I used to read more news. Now I realise that I have narrowed down the things I read every day and become used to only going on certain sites. I realise that if this practice of being informed refers to having a plurality of sources, I am not practicing it today although, in my defence, I have already chosen where to acquire information: I do not want to look for new sources. (11) To paraphrase a famous passage in Kafka’s Diaries, one advantage/disadvantage of keeping a diary is that you become aware with reassuring clarity of the changes that you constantly suffer.

Limits Usually, the limitations of a study are a disturbing element that the researcher notes as a weakness. In this case, the limitations prompted important critical reflection, considering that the ADT is not yet widely used in the social sciences. The first critical point referred to repetition, which was probably linked to the historical period in which the research was conducted. COVID-19 has, in fact, monopolised and affected every corner of our daily lives, as shown by the words of this interviewee: Actually, many times the information that arrived was repetitive, so you can’t help but be repetitive too, in the content if not in the tool. (02)

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In my opinion, this historical period, given the extremely important event of COVID-19, has monopolised everything in the world of information. In fact, I was unable to find news reports for more than 20 days in a row: everything was obscured by the pandemic. (16) In addition to the topic addressed, the time available for respondents to record could also influence the repetition of topics: To see if the mechanism of creating these audios actually results in repetition, the research should be done again on a different subject, just so you would see if it is precisely the fact of speaking daily or for a minute or 10 minutes on a certain topic that causes repetition. I often found difficulties precisely with this subject and how the information relating to it was presented. (16) In some cases, the technique was perceived as strange, as reported by interviewee 4: Well let’s say that, more at the beginning, I was a bit embarrassed because, in fact, I was talking to myself and it felt strange … It happened at the beginning; as I told you, I would freeze while I was talking and could not remember what I was going to say; consequently, I have too-long pauses and then interrupt the recording to make a point a little more fluently. (04) In one case, this was almost disturbing, since it reduced the person to a mere recording, de-personalising him/her and causing detachment from his/her emotions: a sort of ‘I record, therefore I am’ sensation: Mainly because, in the evening, I was tired and in reality did not want to start thinking about all these things of research interest, it often happened that I wanted to have a person nearby to share these topics with and talk about them together, instead of only doing the recordings. This upset me a little. There were only audios of all my emotions, and I felt alone. (13) As happened in many cases during the empirical research, some respondents stated that they had adapted their way of acquiring information to live up to the researchers’ expectations, in line with ‘social desirability’: Because, maybe, I also thought a little about the judgment of you: thinking of sending you the audio of what I had read, when maybe you were expecting something else, made me feel a little fear of your judgment, even if it was short term. In fact, I thought, for example, that news acquired on Facebook was probably not up to par. (08) Let’s say that I tried to speak well and clearly because I knew that this was research material, so I tried to speak at least decently. (10)

CONCLUSION This article was an attempt to explore the potential of the audio-diary technique as a research method. Based on a previous study, in which participants were asked to keep a diary about their media habits 386

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during the COVID-19 pandemic, the final interviews were carried out with the same people to reveal different aspects related to the methodological aspects. The collected data suggested some advantages and disadvantages of using audio diaries to gather individuals’ experiences. Using this technique proved fruitful because the authors received detailed descriptions of the lives of certain individuals and could identify some trends in their hopes, desires, fears, and emotions. The value of the diary-keeping method proved not to be in its sole use, but in its use alongside other methods. First, among the strengths, it is worth mentioning that audio recordings allow individuals to provide data and researchers to collect information in real time, thereby reducing the effort required because audio recordings are ‘hands-free’. As underlined by the participants, audio provides added-value to diaries compared to written documents. The advantage of this is twofold: for the researcher, since pauses, silences, tone, and laughter are audible, they become part of the analysis; however, it is also perceived as convenient by participants, since keeping a written diary requires more time and different skills. A second topic is the potential of digital tools for keeping diaries. Nowadays, technology simplifies data entry, which can be conducted whenever and wherever convenient. Unlike in a focus group or interview, participants have total control of their data: they can decide what to register, how to do it, and where to do it. Additionally, participants stated that this technique was ‘easy’ and ‘user-friendly’ because audio messages form a part of everyday life today. Messaging applications such as WhatsApp allow people to send vocal messages in real time and develop a widespread digital network. Nowadays, the modern storytelling is produced by podcasts, a series of spoken word and audio episodes, all focused on a particular topic or theme. The ‘voice industry’ is also growing thanks to the proliferation of smart assistants such as Siri, Alexa or Google Home. These virtual supporters work to perform tasks or services for an individual based on vocal commands or questions. A third and certainly interesting aspect was the ‘change of self’ that some subjects reported. The method therefore supported a type of auto-action research that could have great potential in the context of, for example, the prevention or promotion of healthy lifestyles, though this aspect deserves more investigations. Regarding the drawbacks, participants pointed out different negative aspects of the ADT. First, some individuals felt uncomfortable with self-narration, deleting and recording the audio many times before sending it. In some cases, the initial instructions restricted subjects who felt too-rigidly guided or, conversely, were somewhat confused about the task. Appropriate training and support for the duration of the period of data collection should definitely be provided. Another problem related to participants feeling self-conscious and/or that their accounts were becoming repetitive. This certainly related to the chosen topic for the narration: the COVID-19 pandemic. The majority of diarists affirmed that they slowly loosened their grip on being constantly informed about the pandemic, precisely because, as time went on, they realised that they were bombarded day and night by the same type of information. This infodemic – an over-abundance of information – led to a repetitive loop in their recording of thoughts and impressions. Lastly, regular recording of entries over time may lead to participant exhaustion and ‘data fatigue’. Some people perceived the task as a tiresome additional commitment, while others highlighted the intrusiveness of the method, especially during days that were difficult on a personal level. Still considering the limitation of the study – small number of participants, topic selected and the reduced interaction with interviewed due to the pandemic – the method reflects the tendency of our contemporary society, largely based on vocal interaction.

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Certainly, AD technique needs to be integrated with other traditional method (such as interviews) to fully capture self-reflexivity both of the participant and the researcher.

REFERENCES Acton, C. (2003). Conversational analysis. In R.L. Miller & J.D. Brewer (Eds.), The A–Z of Social Research (pp. 48-53). Sage. Alaszewski, A. (2006). Using Diaries for Social Research. Sage (Atlanta, Ga.). Berger, R. (2013). Now I see it, now I don’t: Researcher’s position and reflexivity in qualitative research. Qualitative Research, 15(2), 219–234. doi:10.1177/1468794112468475 Bernays, S., Rhodes, T., & Jankovic-Terzic, K. (2014). Embodied accounts of HIV and hope: Using audiodiaries and interviews. Qualitative Health Research, 22(5), 1717–172. doi:10.1177/1049732314528812 PMID:24667100 Brewer, J. (2003). Content analysis. In R.L. Miller & J.D. Brewer (Eds.), The A–Z of Social Research (pp. 43-5). Sage. Cardano, M. (2011). La ricerca qualitativa. Il Mulino. Charmaz, K. (2000). Grounded theory: objectivist and constructivist methods. In N.K. Denzin & Y.S. Lincoln (Eds.), Handbook of Qualitative Research (pp. 509–535). Sage. Corti, L. (2003). Diaries, self-completion. In R.L. Miller & J.D. Brewer (Eds.), The A–Z of Social Research (pp. 69-74). Sage. Denzin, N. K., & Lincoln, Y. S. (2000). Introduction: the discipline and practice of qualitative research. In R.L. Miller & J.D. Brewer (Eds.), The A–Z of Social Research (pp. 48-53). Sage. Elliott, H. (1997). The Use of Diaries in Sociological Research on Health Experience. Sociological Research Online, 2(2), 38–48. doi:10.5153ro.38 Gibson, B., Mistry, B., Smith, B., Yoshida, K., Abbott, D., Lindsay, S., & Hamdani, Y. (2013). The integrated use of audio-diaries, photography and interviews in research with disabled young men. International Journal of Qualitative Methods, 12(1), 382–402. doi:10.1177/160940691301200118 Hammersley, M., & Atkinson, P. (1995). Ethnography: Principles in Practice (2nd ed.). Routledge. Kemmis, S. (2009). Action research as a practice‐based practice. Educational Action Research, 17(3), 463–464. doi:10.1080/09650790903093284 Latham, A. (2003). Research, performance, and doing human geography: Some reflections on the diaryphotograph, diary-interview method. Environment & Planning A, 35(11), 1993–2017. doi:10.1068/a3587 Lewin, K. (1946). Action research and minority problems. The Journal of Social Issues, 2(4), 34–46. doi:10.1111/j.1540-4560.1946.tb02295.x

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Lomborg, K., & Kirkevold, M. (2003). Truth and validity in grounded theory – a reconsidered realist interpretation of the criteria: Fit, work, relevance and modifiability. Nursing Philosophy, 4(3), 189–200. doi:10.1046/j.1466-769X.2003.00139.x PMID:12969449 McTaggart, R. (1997). Participatory action-research. International Contexts and Consequences. State University of New York Press. Merton, R. K. (1957). Some preliminaries to a sociology of medical education. In R.K. Merton, G.G. Reader & P.L. Kendall (Eds.), The Student-Physician: Introductory Studies in the Sociology of Medical Education (pp. 3-79). Harvard University Press. doi:10.4159/harvard.9780674366831.c2 Moretti, V., & Chakraborty, A. (2020). Media Habits and Covid-19. Using Audio-Diaries Technique to Explore “Official” Information Consumption. Fuori Luogo, 1, 97–104. Morse, J. M. (2009). Exploring transitions. Qualitative Health Research, 19(4), 431. doi:10.1177/1049732308328547 PMID:19299750 Ricœur, P. (1984). Time and Narrative. University of Chicago Press. Sargeant, S., & Gross, H. (2011). Young people learning to live with inflammatory bowel disease: Working with an ‘unclosed’ diary. Qualitative Health Research, 21(10), 1360–1370. doi:10.1177/1049732311407211 PMID:21525239 Smyth, J. M. (1998). Written emotional expression: Effect sizes, outcome types and moderating variables. Journal of Consulting and Clinical Psychology, 66(1), 174–184. doi:10.1037/0022-006X.66.1.174 PMID:9489272 Williamson, I., Leeming, D., Lyttle, S., & Johnson, S. (2015). Evaluating the audio‐diary method in qualitative research. Qualitative Research Journal, 15(1), 1–28. doi:10.1108/QRJ-04-2014-0014 Woolgar, S., & Neyland, D. (2013). Mundane Governance. Ontology and Accountability. Oxford University Press. doi:10.1093/acprof:oso/9780199584741.001.0001 Worth, N. (2009). Making use of audio diaries in research with young people: Examining narrative, participation and audience. Sociological Research Online, 14(4), 77–87. doi:10.5153ro.1967 Zimmerman, D. H., & Wieder, D. L. (1977). The diary Diary-Interview Method. Urban Life, 5(4), 479–498. doi:10.1177/089124167700500406

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The research was carried out in collaboration with Anwesha Chakraborty and entitled Media habits and COVID-19: Using the audio-diary technique to explore ‘official’ information consumption. It was published in the scientific Journal Fuori Luogo. To understand and interpret our data, the researcher transcribed verbatim the information that emerged from both the audio diaries and interviews, and then translated the Italian ones into the English language for the purposes of this article.

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Delphi MIX for the Construction of Future Critical Scenarios in Social Distancing Antonio Tintori National Research Council, Italy Giulia Ciancimino National Research Council, Italy

ABSTRACT In the era of digital society, social research must devise innovative and adaptive methodologies in relation to new forms of communication and social interaction. The social distancing measures aimed at containing the spread of COVID-19 have produced the need for social sciences to face new research challenges by making the best possible use of information technology and tools. The researchers’ aim is to present an innovative method of remote participatory social research, which can be framed in the context of future studies. This method, called Delphi MIX, has been developed by CNR-Irpps researchers since 2003, and its last adjustment has been designed as a consequence of the coronavirus crisis. Delphi MIX is a method for participatory strategic planning. It can be understood as a political agenda that aims to a desirable and achievable future.

INTRODUCTION The Social Planning in a Future Perspective The complexity and rhythms of modern societies expose us to choices that are often too conditioned by the present. This diverts the attention from medium and long-term objectives, without which it is not possible to build up future in a complex way, meeting the most concrete needs and desires of citizens and public administrations. The fact that the planning of the future suffers from the present and its problems, rather than foreshadowing objectives that point at an innovative future, is often a political limit. Public DOI: 10.4018/978-1-7998-8473-6.ch024

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 Delphi MIX for the Construction of Future Critical Scenarios in Social Distancing

administrations are called upon to provide immediate answers to problems which would require longterm planning. This is due to the search for immediate electoral consent, but also because of the lack of specific methods and competences addressing problems that cannot be approached in a general way. As we know, the spread of coronavirus, during the first months of 2020, has generated repercussions on all fields of human activity. Therefore, interpersonal relationships have been strongly conditioned and limited by physical distancing in order to prevent the spread of the virus. This unpredictable condition has produced important repercussions on the psychological and relational health of the population. Serious interventions are needed to support the welfare system that must be defined and provided at the national level (Cerbara et al., 2020). COVID-19 was an unexpected and highly intrusive event, which fully involved citizens’ lives as well as politics. National governments and local authorities have been called upon to provide immediate answers to new problems. Often, however, because of the emergency situation, but also because of organisational limitations and lack of specific competencies, we have been faced with partial and inconsistent choices. These choices have proved not to be futureproof, also when compared to what has been implemented in other countries or geographical areas. What has been lacking most in such a time of crisis was an expert and organic judgment based on which it is possible to identify priority objectives to be achieved in a predefined timespan. Probably, this is one of the reasons why in Italy, several months after the start of the pandemic, the essential scientific and health recruitments have not yet been carried out or validated. At the same time, intensive care departments have not been substantially strengthened, teachers have not been trained for distance learning nor have sufficient measures been provided to support parenthood. This situation applies to the context of a country where many people, especially women, spend their own time for guaranteeing essential care services to their relatives, including the elderly. Such an unremitting effort acts against their work and the national economy as a whole. As is so often the case, there has therefore been a lack of an innovative vision of the future capable of rethinking specific human dynamics in a constructionist perspective. It seems, in fact, that we only have responded to the emergency at present, without having a prospect for the next steps to be followed in the medium-long term. This seems to be the result of an action that waits for the future, rather than shaping it. When the future is perceived as a coherent extension of the present, planning, which is among the most visionary components of the political action, then becomes lacking. In the absence of this perspective, the possibility of making a break with the present is withdrawn; a break that would be crucial in conceiving any possible future development through a multi-perspective approach. Scenario planning studies are aimed at defining political and organisational interventions in social and economic sectors, and more generally in all fields of human action, starting from the strategic planning of the future. As foretold, however, these investigations are exposed at the risk of giving up to the temptation of believing in a logical evolution of time. The result of this evolution would directly derive from the characteristics of the present, but most importantly from its weaknesses. This approach outlines a future dimension which is radically conditioned by the problems of the present, rather than its strengths and opportunities (Cooperrider, Whitney, 2005). The exercise of competence, combined with a visionary capacity for addressing problems, can instead produce long-lasting constructions of the future. In this process free will plays a key role in overcoming a multiplicity of social conditionings laying under the socio-cultural identity. To what extent can external factors influence free will and political or individual choices? There is no simple answer to this question. However, given the intrusiveness of social conditioning on human attitudes and behaviours, it can be said that free will is often strongly curbed, especially at government level. Therefore, it can only be preserved through the exercise of an “informed will” based on competent and critical judgment (Tintori, 2012). Therefore, we believe that the construction of the 391

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future is first and foremost a visionary process linked to an informed judgment (Dalkey, 1968). But we do also believe that this process, given its complexity, must be guided by a rigorous methodology. Looking at historical pioneers, the first real method for defining future scenarios was born during the Cold War. In 1952, the U.S. Government commissioned a Department of Defence think tank, the Rand Corporation, to gather the best scientists and technicians available with the aim of predicting the industrial objectives that the Soviets would target in a possible nuclear attack. To accomplish such a delicate and innovative task, the Rand Corporation designed and patented a method, called Delphi, which has been inherited from social sciences. By the middle of the last century, studies of the future gradually became a common practice among governments, institutions and organisations. The “Report on the limits of development”, better known as the Meadows report, still represents a text with large diffusion and great relevance in the field of futurology. This report, commissioned by the Club of Rome to the Massachusetts Institute of Technology, was published in 1972. It had a significant impact on public opinion when it foreshadowed a scenario concerning the years immediately after 2000: the predicted scenario showed a significant contraction of fundamental raw materials, in particular the crude oil market, and therefore a halt in economic growth with the consequent decline of the population. Furthermore, the report was the first one highlighting the problem of planet pollution. The report’s prediction has not been disregarded and its fame has shifted the attention towards the physical limits of the planet, and therefore considering the risks induced by irresponsible human action. The Meadows report shed light on the importance of a conscious vision of the future, which should be free and not conditioned by the thought of the present. It is very likely that the spread of Covid-19 will give a new impetus to the expansion of the digital society, at least as regards the spread of the use of IT tools and virtual communication. As Toffler wittily guessed, with the “self-media” the population will increasingly become the main character of communication. He spoke of “prosumer”, thinking about the possibility of being producers and consumers at the same time (Toffler, 1967). And this is precisely what could happen due to the greater diffusion of the use of digital media as a consequence of the physical distancing produced by the presence of Covid19. The hypothesised possibility lies in the growing diffusion in the use of digital communication tools. In this way the spectator of the past could now truly transform himself into an actor, by producing information; he is both a passive and an active user. This perspective, in the past, had also been outlined by other authors (De Kerchkove, 2014). However, today every citizen can become an interpreter of the collective future in the context of an approach of participatory democracy. This chapter deals the “Delphi MIX” scenario planning method. This method, originating from the traditional Delphi, aims to use the information communication technologies to produce innovative future scenarios in the field of the population. In the following paragraphs we will discuss about the involvement of citizens and of the panel of experts, the mediation of their communication by using a scientific method, the search for a consensus as broad as possible on the main elements of the future and the principle of social desirability. The text will address the issue of the construction of future scenarios from an epistemological point of view, exposing also the heuristic approach and the main components of the Delphi MIX method. Furthermore, the research phases will be described underlying the importance of the role played by the panel and the interviewers. The chapter will conclude considering opportunities and limits of digital interaction in building future population scenarios, despite the scientific mediation.

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EPISTEMOLOGY OF THE CONSTRUCTION OF FUTURE SCENARIOS Future studies, as a typology of participatory studies, have as a final outcome a product of group thinking. In general, the results of a research are a collective product, but in the case of participatory methodologies they represent the result of a work coming from a specifically selected and heterogeneous group which is composed of experts, stakeholders, and ordinary people. In this case, the role of the scientist is always to reach a result that solves the initial problem. At the same time, in this specific research methodology, it is not the scientist who produces knowledge, but a group of experts, whose internal communication is methodologically mediated by researchers. The participatory studies aimed at the definition of future scenarios can therefore be understood as the result of a collective effort that, on the basis of the chosen method, approaches the future by splitting into two different ways of research. The first path starts from the present, following a linear trajectory towards the future (the prediction of future scenarios), the second one abstracts itself from the present to define a desirable future, taking into account elements of desirability and feasibility. The first of these approaches is based on trend curve-related projections, probabilistic analyses based on time series, or, for example, simulations from econometric models. Otherwise, the second research method, in line with Delphi’s epistemological approach, is based on informed judgments made by groups of experts (Mitroff, Turoff, 1973). In recent years, the call for participation and collective sharing of social objectives has become increasingly common. Even at political level, citizens are sometimes called upon to contribute to the choices of administrations in social and economic fields. Scientific practice comprises several widely recognised participatory methodologies. Among the most famous we can find the focus group, useful to solve specific problems by deepening the discussion within small groups. There are also methods aimed at facilitating the convergence of opinions. The most famous is undoubtedly the Delphi method of the Rand Corporation: the method consists in consulting a small group of people (panel) who are interviewed individually and repeatedly at different times until a convergence of opinions is reached, as well as the widest possible sharing of objectives (Linstone, Turoff, 1975). Over time, the Delphi method has been enriched by several variants including: the Mini Delphi, the Shang method, the Nominal Group Techniques and the more widespread Delphi Policy, in which the search for consensus takes place within a larger and heterogeneous group than the one adopted in traditional Delphi (Rowe, Wright, 1999), and it is aimed at defining future scenarios in the administrative field. The future reality, as well as the present one, is the result of social construction, and therefore a product of human activity and its dialectical processes (Berger, Luckmann 1969). It should therefore not be identified through a forecast based on the linear projection of the present, but idealised and designed, thus encouraging a potential change or problem solving through alternative routes. As Beckert suggests, the future should not be understood only as a rigid calculation that moves from the present but limited by the always possible intervention of irrelevant variables that can hinder or modify the definition of our future societies (2013). Indeed, the point is understand how to identify innovative and desirable future images, as free as possible from the limits of the present. In this sense, the concept of collective will can be expressed as dialectical will, which acts in favour of a more complex reality, compared to the one conceived by an individual will. When it comes to the social future, reality should be understood as the result of a social construction: in the twentieth century, social constructionism placed particular emphasis on the importance of language for the construction of reality. Sociologists Berger and Luckmann drew attention to the interaction carried out through language and therefore to human actions, that are part of 393

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the building process of a common reality, subjectively recognised and experienced as objective. Unlike Garfinkel’s ethnomethodological approach (1967), a great advantage of constructionist theory was to look at both the subjective and objective spheres of our existence. According to that assumption, the meaning attributed to constructed and publicly recognised reality included a perspective of both micro and macro social analysis. Nothing should therefore ever be taken for granted and considered unchangeable. Reality is indeed crystallised through continuous verbal transmission that is part of interpersonal communication, but it is primary and secondary socialisation that most prominently settles a culture. This socialisation process gives depth to ideas and offers ways of reading the present and the future, as well as opportunities for emancipation and change. An emblematic example of the objectification of realities are stereotypes, which represent a cognitive distortion of things, but also a simplification of the world and images that are easy to internalise. A stereotype can reflect any social element: an individual or a group, a culture or a religion. In relation to the recalling element, the stereotype plays a catalysing function in the cognitive system, which categorises and recognises, often in a prejudicial way, the characteristics of an object. Ideas supported by rigid and widespread stereotypes are strong ideas, perceived as concrete and apparently immutable over time. These suggestions allow the inference of an individual’s behavioural traits at group level and, conversely, the identification of an individual’s action with the dynamics through which a group is recognised. If present is perceived as unchangeable, it becomes impossible to imagine alternative and innovative futures. Stereotypes are an interesting example of the limits of an imagination which is called to design a desirable and achievable future. That projection is not simply the logical consequence of a present in which everything is taken for granted: stereotypes offer a false, but simple and schematic reality, which is highly embedded in a culture to the point of being shared and becoming consequently true. As stereotypes are resistant to the counterproofs of their existence, in the same way our present appears to us as immutable, limiting our prospects for innovation in the future. The constructionist theory, which is part of the heuristic set within the innovative Delphi methodology that will be exposed in the following paragraphs, examines three phases of the dialectical process. During this process, through social interaction, subjective meanings become objectively recognised facts, and therefore a reality understood as natural, reified, subsequently transforming individuals from producers to products. Specifically, these three phases are: externalisation — where, within a society, individuals are physically and mentally defined by action and activity — objectiveness — when reality, which is socially constructed and part of culture, acquires an apparent autonomy that makes it appear as predetermined — and internalisation — when, through the different phases of socialisation, the objective reality gains legitimacy. Social constructionism is opposed to ontology, a warning against the legitimacy of what is taken for granted. What we want to emphasise is the relativity of beliefs, customs and cultures, producing an infinite variety of realities which can be experienced in the context of social interaction and language. Within the framework of participatory research methodologies, the use of experts, otherwise called qualified witnesses, is a real chance for the definition of future scenarios not spoiled by the present. This peculiarity is affirmed through the implementation of a scientifically mediated strategic dialogue that puts groups of experts in contact, encouraging the overcoming of individual limits. Even the most enlightened scholar, indeed, can be influenced by the organisation which he is a member of, or become the bearer of instances which do not purely represent his thought. Limiting the influence of conditioning and distorting factors, such as those related to relational and leadership dynamics within a group, is crucial for the implementation of a scientifically structured dialogue. This can be configured as an 394

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effective method to achieve products of highly intellectual profile which are capable of driving the evolution of a society, supporting the well-being of a population, but also promoting a regional economy and answering to health and political emergencies or any government-level issue with concrete proposals. The spread of Covid-19 could help us to overcome the concept of “inevitability” of the future, by offering us a boost towards the exercise of consciousness about the potential of society digital communication. That would make it possible to prefigure alternative and innovative images of our future reality. Within the digital society we could all be researchers and producers of the collective future, since we could aspire more to change, identifying the way out of the trapped positions that often appear as inevitable (Appadurai, 2013). This is possible through a dialectical and iterative process of communication, made up of research and co-construction, (Markham, 2021). To avoid the risk of falling into the utopia of future images, however, a greater balance should be found between possibilism and social determinism.

THE DELPHI MIX METHOD: MAIN COMPONENTS AND HEURISTIC SET The Delphi MIX method, which will be described below in its main components, is the result of about 20 years of experimentations within the CNR-Irpps in Rome. This method has been applied and perfectioned over time, in the context of international and national projects, and it has had private and public, national and supranational commissioners (Palomba, 2005; Palomba et al. 2005). Fixed a time horizon with a future studies perspective, this method aims to define operational scenarios that exhaustively foreshadow the necessary steps for the construction of a desired and achievable future. The investigation process feeds on a collective definition of the future reality and it is to be considered as a process of dialogue and co-construction. The method is based on an approach that integrates and elaborates the traditional Delphi Policy, the Appreciative Inquiry theory and the SWOT Analysis. The creation of a scenario begins with the enhancement of the positive elements of the present, and then evolves into an elaboration of the future. This evolution is carried out on the basis of desirable and achievable elements which can help its implementation, going together with the observation of social and economic factors which can limit or promote its realisation. The investigation process of the Delphi MIX method is structured in order to create operational outcomes and it can be considered as a policy agendas maker with objectives to be achieved within a predefined time threshold. Indeed, as the most detailed political agendas, and regardless of the topic covered, the future scenarios produced by the Delphi MIX method consist of Policy Objectives (PO) and Key Success Factors (KSF), which are linked to each other by a cause/effect relation, but also rely on context-related recommendations necessary for their applicability. The Policy Delphi, which is mainly inspired by, is a technique aimed at seeking consensus within a group of experts in order to define future scenarios in relation to one or more specific problems. The Appreciative Inquiry theory, which drive the building process of a scenario, is based on the idea that social systems can evolve in the direction of what they have built positive (Cooperrider, Whitney, 2005). This theory strongly characterises the heuristic set of the Delphi MIX method and it has the function of stimulating the identification and enhancement of strengths at the time of the Delphi study occurs, with respect to the state of the art of a phenomenon. The assumption of the underlying hypothesis, which is linked to this theory, aims to animate an articulated reflection about the present, which is necessary to proceed with the definition of the future reality on the basis of the “4D cycle” (Cooperrider, Srivastva, 1987), which represent the four phases discovery, dream, design, destiny. 395

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The first of these phases includes the identification of the positive elements of the present, the second corresponds to the building of a first image of the future based on recurrent positive elements, the third establishes the process of imagining a desired and achievable future, the fourth in depth designs the scenario. The Swot Analysis, which is the last founding element of the method, consists of a technique developed in the economic field during the 1970s. Also known as Tows matrix, it is a tool used in socio-economic research for the definition of business strategies, as it allows to collect ordered information about the elements of endogenous and exogenous influence concerning a phenomenon. Within the Delphi MIX method, the SWOT analysis defines the “strengths” and “weaknesses” of the future scenario, and therefore the environmental “opportunities” and “threats” that could influence its implementation; these elements can be of both endogenous and exogenous nature or related to a specific context, from a social, cultural and organizational point of view.

THE PANEL AND INTERVIEWERS IN THE DELPHI MIX METHOD As for the traditional Delphi method, Delphi MIX presumes the establishment of an investigation panel. The rules for setting up a good panel are manifold and must be thoroughly observed. The panel must be multi-disciplinary and composed of experts representing different social groups (institutions, policy makers, bureaucrats, scholars, entrepreneurs, the information and communications specialists, industry associations, stakeholders, interested citizens etc.). The selection of experts is a crucial aspect, and it takes place using causality nexus. It is a researchers’ responsibility to make a reasoned choice with particular precautions, since the result of the survey will be linked to the quality of the panel composition. The group of experts will have to be balanced with respect to gender, being composed of subjects directly and indirectly involved in the starting phenomena or problems. Each panellist will have to be carefully identified and motivated in order to minimize the risk of drop-out. In this phase it is important to clarify that the weight of each expert is of great importance in a scientifically mediated co-construction process, and that each member of the panel has the same entity in the future scenario building. In order to build an advisory, representative and bottom-up tool, the Delphi MIX method has introduced an interesting innovation: the submission to the panel of the community’s orientation and the wishes of citizens regarding the topics covered by the research. In the scenario-building process this information is as methodologically influential as is the judgment of an expert, who is consulted in different ways and times from the rest of the panel. This further “external” expert acts as a bearer of attitudes and opinions of the community which are found in a specific exploratory survey, of both qualitative and quantitative nature, usually preceding Delphi research. If a Delphi panel usually consists of a number of experts ranging from 8 to 30 individuals, the ideal panel within this method is composed of 12-15 people. This quota guarantees the creation of a working group that can fully respect the above characteristics. The Delphi MIX was initially conceived on the basis of a direct submission of survey questionnaires, mediated by professional interviewers, consisting of two interviewers for each individual meeting with experts. This choice entails both long implementation times and important costs. The first are due to the time required for contact with experts to define interview appointments - taking into account the fact that the time available to an expert approaching a Delphi survey is always limited. The latter is related to the cost of interviewers, also taking into account the transfers and expenses of food and accommodation. 396

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Over time, some applications of this method have foreseen the remote interaction between research group and experts; such a factor assumes a crucial importance considering the need for respecting physical distancing in order to avoid the risks of Coronavirus contagion. The need to implement a serious scientific scenario-building process involves the elaboration of complex Delphi detection questionnaires on innovative computer platforms that allow an effective interaction within the panel. As for the elaboration of new techniques, this application has the target of integrating the standard method with the excerpts missing from face-to-face communication, whilst building new paths for carrying out participatory research activities. Despite of being pushed by constraining external factors, such a new way of interacting can represent a development opportunity in terms of connecting experts worldwide regardless of their individual contingencies. Those accountable for the direct collection with the panel must always have proficiency in the topics covered, as well as methodological competencies; they must therefore be able to maintain an active listening approach free from influences using neutral expressions, also in terms of body language. Despite the fact that in the case of Delphi we are dealing with experts, hypothetically conceived as hardly influenceable individuals, the interviewed/interviewer interaction may still be subject to the problem of acquiescence: an issue exposing the respondent to the risk of a priori agreeing with the proposed statements. The competence of the interviewer, as well as the one of the panel, is crucial for the application of the method. The interviewer must have confidence in his interlocutor’s skills, who, on the other hand, must perceive the recognition of his own authority, in order to be prepared for reasoning, dialogue and confrontation.

THE DELPHI MIX METHOD The phases of the Delphi MIX method’s investigation are based on the following methodological principles: • • • • • • • •

the experts involved never meet; none among the experts knows the composition of the panel; the interaction between the interviewer and the experts is remotely mediated by the research group; each round of interviews allows experts to express opinions without knowing those of the others; the information collected in each round are aggregated, processed and synthesised in order to be proposed to the next round anonymously; at each round, experts can remodel their ideas; the dialogue process and co-construction continues until clear positions and indications are defined; the whole process is aimed at seeking consensus on the objectives to be achieved.

The principles of this method are similar to those of traditional Delphi, being the first a variant of the Rand Corporation’s work. It is planned to activate an iterative process, which involves the detection of opinions on a phenomenon within several rounds of interviews. This process is conceived in order to allow each expert to examine the position of the other ones, thus favouring the critical remodulation of individual points of view. Within Delphi MIX, this procedure is articulated through asynchronous communication with experts: the panel interacts both at different times and individually with the interviewers and the research team. A key aspect of the method is anonymity: in fact, the panel’s composition must 397

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be secret until the end of the investigation. During the scenario-building process, experts are never aware of the identity of the other experts within the group. Based on a review of the 4D cycle of the Appreciative Inquiry model, this method starts from the in-depth study of the phenomenon to be investigated, according to which the future scenario must be built. This is the “exploration” phase, which includes: the detailed definition of the problem and its size of analysis, the causal choice of the panel and the collection of narrative contributions among experts that are useful for the definition of the first structured round of investigation. The subsequent phases of “definition” and “construction” are preparatory to the scenario building, which through the last step of the research, the “design”, embeds the “SWOT recommendations” necessary for its implementation. The Delphi MIX provides for the consultation of a panel in four research phases, with different methods of collecting information. As said before, the future scenario starts with the identification of political objectives and critical success factors, representing the type of intervention to achieve the objectives, which can be political, economic, financial, social or cultural. Policy objectives and critical success factors are complementary elements, as they include goals and tools useful for achieving the end. Each goal can involve several tools, since an objective can be achieved by following different strategies. Therefore, in Delphi MIX method, the future scenario is to be understood as an achievable dimension through the accomplishment of defined objectives (PO) that can be realised through the application of specific interventions (KSF). The timespan between the end of the Delphi process and the time horizon set for the implementation of the scenario corresponds to the implementation frame of PO and KSF (Table 1). The scenario is therefore theoretically built within the Delphi framework, while its practical implementation is put back in the hands of those who hold the decision-making power, both public and private. Among the different phases of the survey, 4 rounds of interviews are outlined on the basis of a questionnaire-feedback-questionnaire cycle. This structure is functional to the progressive measurement of consensus within the group of experts. Unlike the traditional method, which usually provides for a preliminary collection of qualitative data that becomes usable only in successive phases of the survey, this method involves the collection of qualitative and quantitative information at the same time. The first step of research establishes the meeting between interviewer and respondent: this is the most important moment as it establishes a link between the panel and the research team. The collection phase of Delphi MIX starts from the round Zero. This activity follows the study of the phenomenon, which is carried out by the research team, and the definition of specific survey dimensions. Once the panel has been selected and all the experts are willing to cooperate, round Zero consists of a preparatory phase for the construction of the first survey questionnaire, which involves the e-mailing of structured open questions aimed at gathering free information on the subject of the future scenario. Through the growing digitalization also stimulated by the spread of Covid-19, with the Delphi MIX method it will be possible, in this phase, to introduce popular orientation at the end of the definition of the first survey questionnaire. This can be detected as part of round Zero by means of a special survey in which interested citizens can participate. All the answers and popular orientations collected during round Zero are a valuable contribution to the construction of the first survey questionnaire. In this way it is possible to take into account the point of view of the experts from the start of the co-construction process. However, this moment is also the only one allowing the panel to freely give indications, as we are in the exploration phase. In the case of specific research needs, when the subject of study is particularly complex, it is possible at this preliminary stage to consult an “enlarged panel”. Such a group is composed of experts selected for the survey with the addition of other qualified witnesses in order to 398

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Table 1. Phases and related activities of the investigation process of Delphi MIX method

Exploration

• systematisation of the study material on the basis of several elements: the state of the art of the phenomenon, the scientific literature, the knowledge and skills of the research group; • definition of the phenomenon and its analysis dimensions; • detailed time planning of the survey; • collection of information and guidance from the population concerned by means of a survey; • panel selection; • remote contact with experts, request for participation, assessment of their interest and willingness to cooperate; • collection of useful contributions to the questionnaire’s construction for the first round of interviews (round Zero); • planning of interviews with the panel.

Definition

• qualitative analysis and elaboration of the panel’s contributions collected during round Zero; • construction of the questionnaire for round One of interviews; • upload of the first questionnaire on an electronic platform (round One); • remote dissemination of the first questionnaire (round One) and assistance in compiling (where necessary); • statistical analysis of the results of the round One of interviews.

Construction

Design

• construction of the round Two questionnaire of interviews; • upload of the second questionnaire on an electronic platform (round Two); • remote dissemination of the second questionnaire (round Two) and assistance in compiling (where necessary); • statistical analysis of the results of round Two of interviews. • definition of interventions (PO and KSF) characterising the future scenario; • construction of the future scenario in a narrative form; • collection of SWOT information about the future scenario (round Tre); • qualitative analysis of SWOT information; • drafting of the final research report; • public discussion of results (consensus meeting).

Source: Tintori, 2015.

enrich the material and the quality of the contents. On the basis of this, it will be possible to set up the first survey questionnaire using a structured type of pre-codified answers. During this phase the opinion on the phenomenon under study expressed by the community can come into play. This opinion can be collected during an ad hoc exploratory research which is carried out before the Delphi investigation. The decoding and systematising of all qualitative information expressed during the exploration phase, such as those deriving from the open question of round Zero, is carried out through the technique of content analysis (Nobile, 1997). This technique examines a text and translates it into descriptive categories in compliance with the principles of the fundamentum divisionis, exhaustiveness and mutual exclusivity of categories. The asynchronous communication and the iterative process start along with the definition phase of Delphi MIX, during round One. The expert-researcher interaction involves two interviewers for each meeting, which takes place remotely. Both researchers have the task of facilitating the collection of information, stimulating the reflection of experts on the topics discussed and urging respondents to exploit what is recognisable as positive in the present. Once the information material necessary for the construction of round One questionnaire is collected and processed, then the and the areas of intervention (sections of the questionnaire) and the topics to be covered are defined. The collection is carried out remotely on the basis of the schedule of appointments agreed with the panel. The first questionnaire represents a moment of great complexity of the research, as it is part of the content that defines the future scenario. The round One questionnaire is divided into a number of areas of intervention related to research needs,

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providing for 10 to 15 policy objectives for each of these, and detecting for each policy objective from 10 to 12 critical success factors. On average, a Delphi MIX outlines a future scenario based on at least 35/40 policy objectives and 350/400 critical success factors, representing all the elements on which the panel’s consensus is measured during the investigation process. The structuring of round One questionnaire is the longest and most complex phase of Delphi MIX. The definition of PO and KSF develops as part of a communication process that takes place in the research group on the occasion of numerous brainstorming sessions. These meetings encourage the emergence of latent factors and dimensions, which will be added to the information previously collected. The final material is translated into PO and KSF. The interviews of round One take place with the help of traditional tags, translated into electronic format that is uploaded on a computer platform - usually resident on CNR web spaces. The platform is tailored to the purpose of the investigation and suitable for the collection of information in CAWI mode. Experts are then urged and assisted in order to self-compile within defined time limits. For each policy area, experts are asked to choose, according to their desirability, 3 policy objectives, and for each of these 4 critical success factors. The results of round One of the interviews are placed in an Excel spreadsheet. The round Two questionnaire is the product of the elaboration of the aggregated results of the first round of interviews, usually composed of policy objectives and critical success factors chosen by a significant portion of experts. Like the first questionnaire, the second questionnaire is organised in the same research areas. However, in this case they contain fewer PO and KSF, considering that a certain number of elements that do not exceed predefined consent thresholds are excluded, classifying them as unwanted objectives. This is the construction phase of the method, which will end in the definition of the PO and KSF lists characterising the future scenario. Like the previous one, the round Two questionnaire is also uploaded on a computer platform. In this phase of research, specific indicators, necessary for measuring consensus within the panel, come into play. For each policy area, all policy objectives are assessed through a desirability scale and a feasibility scale. For desirable and/or achievable objectives alone, each expert is called upon to give his or her opinion on the related critical success factors, by the means of a scale of importance. The desirability scale is intended to measure what each expert considers desirable to achieve a political objective. The feasibility scale provides an estimate of the actual feasibility of the objective and refers to factors of a socio-economic nature that can support, be influential or hinder the concrete implementation of a goal. The scale of importance refers instead to the emphasis of every critical success factor in achieving a goal. Finally, in order to construct a scenario that provides detailed information about the interventions contained in it and accompanied by the implementation order of the latter, it is possible to adopt an ordinal scale of priority. Before the judgment is attributed to PO and KSF, each Delphi expert is instructed about the meanings of the scales and how to respond to them. The elaboration and analysis of the results of round Two of interviews is carried out on the basis of predetermined panel consent thresholds, verified and measured through scales of desirability, feasibility and importance. The outcome of this survey phase, i.e. the aggregated results of the second round of interviews, is the definition of the scenario content. Usually, the PO and KSF reaching a high level of consensus within the panel do converge in the scenario. The method tends to adopt very high thresholds for the transition of interventions from one stage of the survey to the next, as well as to the scenarios. The product of the research process thus guarantees a high representativeness of the panel, as the strong consensus of the questioned experts converges on the factors.

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The last phase of Delphi MIX - the drawing - involves the description of the scenario in narrative form for the communication purposes of the Delphi product. The narrative scenario, together with the relevant PO and KSF lists, is transferred to the IT platform that was previously used for the realisation interview rounds. Furthermore, experts are invited to read and critically analyse the contents, and then fill out an electronic form for the collection of recommendations useful for its realisation, on the basis of the SWOT analysis model (round Three). This last round of interviews involves the detection of experts’ opinions on the strengths and weaknesses of a scenario (endogenous factors, characterising the PO and KSF). At the same time, it also considers the opportunities and threats characterising the social, economic, cultural, historical and political context in which a scenario can be realised (exogenous factors). The results of round Three are analysed and processed in accordance with the principles of content analysis. They are the recommendations for the realisation of the scenarios. The communication of the results of Delphi MIX provides for the drafting of a research report and the subsequent production of scientific publications. However, it is desirable to communicate a scenario and its recommendations also in the context of a consensus meeting, which can optionally be the last activity within the method. This moment consists of a public meeting in which the Delphi product is exhibited and discussed with policy makers, stakeholders, industry experts and with the same panel involved in the research process. It is an occasion that is functional to the dissemination, enhancement and visibility of research results. The consensus meeting can provide for the targeted involvement of the press in order to disseminate the scenario at the territorial level. At the same time, it is also crucial for raising the awareness of policy makers towards the opportunity of adopting medium and long-term planning strategies with respect to the phenomena covered by the research. The consensus meeting integrates the research results with the points of view and critical issues that decision-makers and administrators can express, reducing the lack of availability about the use of scientific knowledge in strategic social planning programs.

CONCLUSION The method here exposed is to be considered as an evolution of the traditional Delphi. It stands as a communication process remotely carried out by researchers in order to open up future prospects by using high competences. Delphi MIX method aims at the social planning of the future with the on-line platforms support, representing an opportunity when physical proximity is a risk. The greater use of virtual communication, produced by the spread of Covid-19 due to physical distancing, the domestic confinement and the swich of the social interaction from the real to the virtual sphere, is a new chance to enhance from this perspective. Furthermore, at a time of crisis such the one we are experiencing, it is crucial to design political actions that foresee medium and long-term solutions. Over time, the epistemological debate about the scientific objectivity has involved famous thinkers, drawing the attention to both the application purpose of Delphi and the opportunities that the planning of the future offers to the decision-making process. The possibility of such a remote information exchange between social scientists and experts, and between social scientists and citizens, is the added value for planning policy interventions. However, there are some limits in virtual communication. These are mainly detectable in the reduced communicative empathy, which inevitably distinguishes this form of interaction, and which could also compromise the same visionary creativity about the future. However, the advantage of investing in 401

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digitalisation and digital skills seem to outweigh the limits of virtual interaction. For example the ever wider participation of the population in political choicescould also make the decision-makers more responsible, since they are involved in a continuous, interactive and rapid process of sharing with the basis of every perspective on the future. Therefore, the purpose of this essay is to raise the awareness about the importance of investing skills, experiences, and creativity, making use of the tools and culture of the digital society, in order to formulate judgments with a broad popular consensus, and therefore innovative solutions for the future construction. Indeed, the main goal of Delphi MIX, is to provide detailed ideas on future policies and their effects to the decision-making process, taking into account the multiplicity variables coming into play. According to the insights of Dalkey (1969), if we think about the three components of a single segment, in Delphi MIX method the informed judgment corresponds to an intermediate area of intuitions and competences, between opinion and knowledge.

REFERENCES Appadurai, A. (2013). The Future as Cultural Fact: Essays on the Global Condition. Verso Books. Beckert, J. (2014). Imagined futures: fictional expectations in the economy. Theory and Society, 42(3), 219-240. Berger, P. L., & Luckmann, T. (1969). La realtà come costruzione sociale. Il Mulino. Cerbara, L., Ciancimino, G., Crescimbene, M., La Longa, F., Parsi, M.R., Tintori, A., & Palomba, R. (2020). A nation-wide survey on emotional and psychological impacts of COVID-19 social distancing. European Review for Medical and Pharmacological Sciences, 24(12), 7155-7163. Cooperrider, D. L., & Srivastva, S. (1987). Appreciative Inquiry in organization life. In A positive revolution in change. Academic Press. Cooperrider, D. L., & Whitney, D. (2005). Appreciative Inquiry: A positive revolution in change. Berrett–Koehler Publishers. Dalkey, N. C. (1968). Predicting the Future. The Rand Corporation. Dalkey, N. C. (1969). Analyses from a group opinion study. In Futures. The Rand Corporation. De Kerchkove, D. (2014). Psicotenologie connettive. Egea. Garfinkel, H. (1967). Studies in ethnomethodology. Prentice-Hall. Linstone, H. A., & Turoff, M. (1975). The Delphi Method: Techniques and Applications. Addison–Wesley Publishing Co. Markham, A. (2021). The limits of the imaginary: Challenges to intervening in future speculations of memory, data, and algorithms. New Media & Society, 23(2), 382–405. Mitroff, I., & Turoff, M. (1973). Technological Forecasting and Assessment: Science and/or Mythology? Technological Forecasting and Social Change, 5(2), 1. doi:10.1016/0040-1625(73)90027-9

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Nobile, S. (1997). Credibilità dell’analisi del contenuto. Franco Angeli. Palomba, R. (Ed.). (2005). Il tempo è dalla nostra parte. Scenari per l’Italia al 2030. Quaderni di Demotrends, 5. Palomba, R., Dell’anno, P., & Tintori, A. (2005). Methodological approach. BIB, 2, 113–116. Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: Issues and analysis. International Journal of Forecasting, 15(4), 353–375. doi:10.1016/S0169-2070(99)00018-7 Tintori, A. (2012). Metodo Delphi e politiche per lo sport. SDS – Scuola dello Sport, 93, 3–13. Tintori, A. (2015). Scenari futuri e giudizio informato. Un innovativo metodo Delphi. Aracne Editrice. Toffler, A. (1967). La terza ondata. Sperling & Kupfer.

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Chapter 25

Mixed Methods and Off/ On-Line Research:

A Case Study – Fitness, Smartphone and Devices, and Well-Being Eugenio Bagnini Università di Bologna, Italy Giovanna Russo Università di Bologna, Italy

ABSTRACT The chapter proposes a methodological consideration on the use of mixed methods and the social opportunities of digital technologies in sports and wellness practices. The research carried out tries to answer the following question: What are the social repercussions and body care practices allowed by digital technologies in the field of sports and physical activities for well-being? The contribution investigates the relationship that is established between practitioners of individual fitness and wellness sports activities, mainly in gyms, and the changes attributable to HTI (human technology interactions) with digital devices (apps and participation in online groups). Through a qualitative-quantitative methodology approach, the multifunctionality of the aforementioned digital tools (on a mediatic, playful, and technological level) were observed in order to verify whether the convergence between digital and sports social worlds is an instrument of only subjective well-being or may indeed prove as a new collective way of sharing, participating in, and adopting healthy practices.

INTRODUCTION: DIGITAL SOCIETY AND SPORTING BODY The debate on the status of bodies on the web is very heated today: in the face of the complexity and quality of information, it appears intimidated by the speed, precision and power of new technologies. The most current visual features of the body are the result of the objective transformation that biomedical technologies have introduced in the last decades, accompanied more and more often by bioethics, DOI: 10.4018/978-1-7998-8473-6.ch025

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as well as by the much more consolidated and widespread practices of bodybuilding, fitness, body art and cosmetic surgery. However, according to the most recent research developments in this field, the training and exercise of the body are still the subject of radical experiments in performance as well as a setting for observing innovative experiences through the use of personal digital devices (smartphones, specific apps or wearable device) in the health sector (Maturo, 2012) and in sports. In both cases, the idea that is reinforced is that of the body as an object, resulting from choices and options (Giddens, 1991: 8) towards an ideal image of well-being, which considers sport and physical activity to be the privileged tools for a healthier life – both from a psychophysical point of view, and from a relationship with others and with the environment. The construction of one’s body today is considered as a reflective project, a real task on oneself, argues Zygmunt Bauman (Bauman, 1999: 127-149) who, in the triumph of everyday life, sees in this personal project an absolute concern, as well as the most important pastime of the “post”-modern individual. The extraordinary diffusion of services for body care (cosmetics, surgery, dietary regimes, gym activities) is the most evident confirmation of this, so much so that nowadays it is the exercised, athletic body that is configured as a universal canon of beauty and harmony. The image of the sports body has become the subject of dreams that come true, of projects that translate into activities and exercises that give physical shape to the ideal. But also, as a place of transformation of these same ideals. The “fit” body therefore appears as a universal ideal and allows for a double glance. From within the sport field, it is useful for reading the transformations made in physical activity practices; from the outside, it identifies – among the various emerging practices – those that characterize the web society (Cipolla, 2015) expressing both a generalized attention to the concept of wellness culture (Russo, 2018) and the growing diffusion of a model of social behavior which refers to the contemporary ideal of sporty man (Bausinger, 2008). The social implications related to the use of digital devices and social media networks in the world of individual sports practices of body building, fitness and wellness are therefore the subject of these reflections. In the context of the digital society in its offline and online multi-life dimensions (Boccia Artieri, 2012) we have tried to understand the cultural artefacts’ nature of digital technologies in the field of sporting health practices (Lupton, 2014). The aim was to observe the many effects that these tools have not only in terms of body discipline and “behavior changing techniques” (Yang, Maher, & Conroy, 2015) of the single individual, but also in terms of virtual social spaces and the forms of relationality that they explore. The role of digital media has been observed in various aspects in the evolution of fitness culture, trying to realize how technologies contribute to developing the social, cultural and economic contexts in which they operate. As Manuel Castells (Castells, 2002) argued, from the 1990s onwards, the computers and the use of the Internet have created a real techno-cultural revolution of communication. As a virtual place, social space and cultural artefact, it is in fact able to model the production and sharing of contents of “produser” individuals, that is users-producers (Bruns, 2008) for the activity of contemporary and shared participation both to the same communication rule and in the production and validation of personal content. This is what characterizes apps and wearable devices. As Deborah Lupton affirms, they are “digital objects produced by human decision-making wills, supported by tacit hypotheses, norms and discourses already circulating in the social and cultural contexts in which they are generated, marketed and used for specific purposes” (Lupton, 2014: 606).

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In the field of physical activities, care and improvement of the body, the now widespread use of these devices is to enrich individual and social experiences of sport, health and well-being. Following the mobile-ization process (Rainie & Wellman, 2013), fitness and wellness activities are also affected by the presence and continuous influence of digital technologies, which, associated with the evolution of software (apps) and devices (sensors and wearable devices), enter the daily life of each individual, shaping their activities, social relationships, even the ways of understanding reality. In a cultural context that rewards private and individualistic needs (Beck, 2000) and while the speed of progress is imposed as a value, personal apps and devices combine these principles, giving the user the opportunity to independently pursue personal goals. Furthermore, these tools, if understood as sociocultural artifacts (Lupton, 2014: 606), are capable of influencing and developing the reorganization skills of individuals in daily action, creating an efficient model of physical and health practices – a sort of disciplining techniques of the body. Through controlled diets, structured training protocols, continuous monitoring of users’ performance (self-tracking) or augmented reality services, apps can emulate the presence of actual professionals from the sports sector while offering detailed information to all those who use them. In this way they increase self-consciousness and self-improvement, enhancing the efficiency of their physical activity through the analysis of their data gathered and visualized by the smart devices. Antonio Maturo (Maturo, 2014: 62) affirms that selftracking coincides with a very extensive series of self-measurements that can be carried out through a smartphone, a tablet or other devices equipped with sensors. Various types of such data include sports performance, current physiological states, behaviors, feelings, vices; finally, these data are processed, compared and evaluated in order to improve one’s own life and athletic achievements. Many studies also testify to the positive combination of the use of apps and devices – namely, an increased participation in physical activities and an improvement in people’s health. In particular, the effect noted is that of fitness apps helping users increase motivation to practice sports by modifying their behavior in everyday life through the use of fun and/or gamification, the involvement of their social relationships, or other specific supports (e.g. virtual coach, food diary). To simplify, the fulcrum of these tools lies in the ability to influence personal motivation for physical activity and forced control of one’s body. The possession of smart objects and the use of apps, through personalization and flexibility, can produce and encourage a culture of movement and health (Brabazon, 2015), as well as develop motivational dynamics aimed at greater commitment towards oneself on one hand, and towards others in sharing activities in specific communities on the other. Therefore, fitness is increasingly social, since the practice of sharing personal data strengthens personal goals, while the informally taken social commitment (Lupton, 2017: 615) also broadens the perspective of wellness culture when it presupposes involvement of the community and the environment. The analysis reported here therefore develops along this continuum, which attempts to answer the following research question: what are the social repercussions and body care practices allowed by digital technologies in the field of sports and movement activities for well-being? This contribution investigates the relationship that is established between practitioners of individual fitness and wellness sports activities, mainly in gyms, and the changes attributable to HTI (human technology interactions) with digital devices (apps and participation in online groups). To reach this goal, mixed methods have been considered and used in field work to gather different type of data. In specific, through a qualitative-quantitative research carried out in 2019 (in Emilia Romagna region – Italy - and online through social media networks), the multifunctionality of the aforementioned digital affordances were observed, in order to verify whether the convergence between digital and sports social worlds is 406

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an instrument of only subjective well-being, or may prove to be a new collective way of participating and sharing healthy practices. In this sense, the results of the survey reveal that the use of digital technologies in fitness/wellness practices is a vehicle for dual intentions between “intimacy” and “estimacy” (Bauman & Lyon, 2015). On the one hand, the use of such devices can produce a “narrative” and “interactive” discourse concerning the users themselves; on the other, practices of narcissism and self-esteem may emerge (Tisseron, 2008), aimed at the ideal representation of one’s person, appropriately constructed through digital tools and a specific selection of shared contents. This means that the pursuit of personal fitness goals, self-discipline and knowledge of objective data of one’s own body are accompanied by practices of sharing and participation in communities, in search of an increasingly social fitness oriented towards health and well-being goals (Russo, 2013, 2018). Following Deborah Lupton (2017), we can talk about social fitness when the sharing of personal data makes it easier to achieve both individual goals and allow the user to fit in the community as an ideal citizen who guides the private interest of its own fitness and wellness activities with the public good of health.

EPISTEMOLOGY, QUESTIONS AND MIXED METHODS APPLIED TO A CASE STUDY This research was born in order to deepen the role of digital media in sports culture and individual practices of fitness, health and well-being, while trying to study their influences and social repercussions, the conversation they create and the sharing of personal information and content on social media networks. Specifically, we wanted to investigate the relationship and opportunities related to the use of digital devices from the users of digital technologies and devices in fitness and wellness practices. The intent was to understand whether the convergence between social worlds and sports worlds could prove to be a new collective way of participating and sharing good health practices with communities of interest such as ones with healthy lifestyles and well-being oriented, digitally reconstructed. The aim of exploratory research and bottom up approach, i.e. inductive starting from the data obtained, is to analyze and delineate the phenomenon through an open method, which helps to understand human behaviors, relationships and the different types of empirical information as well as to provide an explanation as faithful as possible to reality. We opted for a combination of qualitative and quantitative methods at an epistemological and methodological level (Creswell, 2003), after considering the appropriateness of the choice of this method for a case study (Mauceri, 2017) in which we can identify a complex and multidimensional research object – digital devices and social media, body discipline and related social dynamics – as well as the population of enthusiasts without well-defined sociologically boundaries. In addition, we considered the importance of the relationship between sport and wellness, as well as the relationship between digital technology and the “online world”, the present sociological research has been based not only on the combination of qualitative and quantitative methods, but also on integration of digital methods (Lupton, 2015; Marres, 2017) with the ethnographic (Kozinets, 2010; Varis, 2014) and traditional sociological approach, in order to access both cultural, textual and multimedia contents shared by users of social media networks, and in order to expand the reference set through a survey using web tools.

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“Mixed Methods” alludes to an arising research approach that advances the orderly combination, or “mixing” of quantitative and qualitative information inside a structured research program (Wisdom & Creswell, 2013: 13). This method is a supplement to, rather than a substitute for, quantitative and qualitative research methods (Johnson & Onwuegbuzie, 2004), and the goal is the creation of a single study that answers questions about the relationship between the qualitative assets of the phenomenon and the quantifiable factors from the perspective of the participants (Amaturo & Punziano, 2016). Particularly, the Mixed Methods approach is guided by a complementary, constitutive and pragmatic intent of the methods in research, such that “the set of strategies for combining quality and quantity should be considered a possible tool to improve methodological rigor and theoretical scope and/or pragmatics of results” (Mauceri, 2017: 42). The choice of this approach in the case study analyzed focuses on the intention to provide a more complete description and a deeper understanding of the motivations and behaviors of all social figures that can be identified in the population considered, overcoming the limits and the most fundamentalist positions of the single different approaches and, at the same time, facing problems and difficulties of an integrated approach (Amaturo & Punziano, 2016). In this work, the qualitative data (face-to-face interviews and digital ethnography) were collected based on having information about issues on topics such as fitness, technology and sports, and then they were investigated through a quantitative method (survey) to demonstrate the numerous information and to extend or generalize it to a broader and sociologically relevant set of people. Moriarty (Moriarty, 2011) argues that qualitative research is beneficial to social care because it studies social phenomena from the perspective of participants. We chose the qualitative methods (observation and use of mobile Apps, in-depth interviews, interviews, and finally analysis of online user-generated contents on social media networks) in order to show the experiences and the strong feelings of the fitness practitioners on proper relations with technology and well-being practices. On the other hand, quantitative methods aim to obtain accurate and reliable measurement data to quantify information and apply it to statistical analysis to support or deny “alternative knowledge assertions” (Creswell, 2003: 153). Looking for explanations that will affect other people, the authors’ goal is to “establish, confirm, or validate correlations, as well as build generalizations that add to theory” (Leedy & Ormrod, 2001: 102). Creswell (Creswell, 2003) explains how to use this hybrid strategy to meet various needs. For example, case studies and grounded theories research investigative processes and activities, while ethnographic research examines broad cultural sharing behaviors of individuals or groups. Both quantitative and qualitative methods are then designed to answer research questions, and both analyze different sociological characteristics. The qualitative approach allowed the researchers to comprehend the complexity of digital and social fitness, whereas the quantitative method gave them an objective evaluation of reality (Williams, 2007: 70). Given the strong relationship with digital technologies, we also chose to carry out the research phases both through direct interactions and through computer-mediated communications. It should be remembered that the quality of the information collected through social media networks, computers and smartphones is of an inorganic nature. Moreover, the web, by “hiding” users behind various second screens, makes the differences in behavior on the part of the users lawful. Nonetheless, digital data is a cultural product with its own significant social dimension, especially in online groups and social media networks; this is because the internet is no longer perceived as a channel, but essentially as an inhabited social space capable of hosting typical practices and even cultures capable of increasing the possibilities of sociality of individuals.

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Observing textual and audiovisual content online can therefore lead to opinions or personal productions that meet the needs of voyeurism, esteem, hedonism or marketing; in these digital and natural data, in any case, we can recognize the cultural attitude and ethos of a group and of the individuals who are part of it. This research method, in our case, is particularly suitable for analyzing and deepening the theme of the relationship between fitness, digital technologies and well-being, and its social consequences in terms of sharing, participation and dissemination of the well-being practices supported – or practiced through – by Apps, devices and social media networks. The integration of traditional methods with digital methods took place in the ethnographic and qualitative phase (Padricelli, Punziano, & Saracino, 2020), while in the quantitative phase of the research it was decided to administer questionnaires through an instrumental use of digital technologies and social media networks, therefore through a transposition of traditional methods in the digital context. Considering the context of the discovery in which this research has operated, the objective of the quantitative phase was to deepen and support what emerged through the qualitative phase (Mauceri, 2017: 49), in particular the consequences and social repercussions of the uses of digital technologies in daily fitness and wellness activities. The set of empirical “digital users-fitness enthusiasts” interviewed here was based on election criteria including: knowledge and practice of sports activities, attendance of fitness centers or spaces dedicated to physical activities or wellness, use of smartphones, knowledge of digital technological devices for sport (not necessarily used) and social media networks. The data collected through interviews and questionnaires were finally processed through the use of software (Nvivo, Spss and R) or the lexical analysis of the contents and correspondences, carrying out a study of the profiles of the recorded cases, with the aim of understanding the social dynamics and cultural in the world of fitness/health and the use of digital technology between virtual and real spaces. The research of this case study was carried out during January and February of 2019, according to the following 4 phases: 1. Observation and Study of Apps and wearable devices in the Fitness and Health field 16 Apps1 were considered and studied, chosen among the top choices in the online market of smartphone apps. At the same time, several of the most popular wearable devices on the market for fitness tracking were also observed. The analysis of apps and wearable devices for fitness and wellness highlights the following features: self-tracking, life logging and social support activities. Collectively these allow users to monitor, record and manually enter personal physiological data and parameters, foods consumed and physical activities performed, as well as to geolocate and carry out the same activities together with other people. It is also possible to share information and personal content with users of the same app or through social media networks and thematic groups. Alongside these main activities, there are other accessory functions: coaching, gamification and ludification, or the possibility of having a trainer/digital expert able to provide advice, create activity protocols, with game modes through virtual challenges and awards if the expected result is achieved. The multiple functions related to the use of these devices were investigated at all stages of the research to understand the opportunities and social effects on the concept of active well-being specific to wellness culture (Russo, 2018). 2. Investigation through in-depth interviews and semi-structured face-to-face interviews. 409

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Eight significant witnesses were chosen for the in-depth interviews; specifically: 4 personal trainers and 2 sports doctors working in Bologna (Italy), and 2 professionals (one from Milan and one from Pisa) in the field of App development for Sport and Health. The interviews were aimed at probing the following areas of investigation: • • • •

Uses and social effects of the web and social media on fitness/wellness; Uses and social effects of smartphones and mobile devices; Social interactions between off/on-line practitioners; Changes in fitness/wellness and opportunities for social fitness.

Subsequently, 20 semi-structured interviews were carried out with expert fitness and wellness practitioners in Emilia Romagna, Italy, at the same time users of apps and social media networks for these motor activities. The interview outline covered these topics of discussion: • • • •

Fitness/wellness activities practiced; Use of smartphones/wearable devices, websites and social media for fitness/wellness activities; Sharing activities and off/online social interactions; Fitness and wellness changes. 3. Digital ethnographic research through social media networks.

This further phase of qualitative investigation, of a digital and exploratory ethnographic nature, involved the use of personal computers and the observation of contents on social media networks (Facebook, Instagram and YouTube), thematic groups and user profiles characterized by frequency of gym visits, fitness and wellness activities, use of digital technologies and apps. Specifically, user generated content was analyzed, i.e. posts with personal contents, discussions of the aforementioned topics, sharing of multimedia content with particular attention to quantifying selfactivities and wellness practices in order to show a continuous balance between online and offline, as well as the culture shared among many of the fitness/wellness practitioners. 4. Quantitative online survey for “sports digital practitioners” In this phase, carried out by administering web surveys through Facebook, Instagram and WhatsApp, virtual data (Padricelli, Punziano, & Saracino, 2020) were collected on a significant set of users, whose elective characteristics were knowledge and use of digital technologies, social media and the practice of sports and wellness activities. The questionnaire outline was aimed at exploring the following topics: • • • •

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Sport, fitness and wellness: general information relating to the practices carried out by the respondents; Use of digital technologies: attention to the use or lack of digital apps and devices for physical wellness practices; collection of data and information obtained from the app and self-tracking; Activities and sharing with friends: interest in group activities and sharing or lack of information and personal contents; Activities on social media networks and communities: focus on activities carried out by respondents on personal profiles and in groups/communities through Facebook and dedicated sites;

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•

Socio-demographic information of the interviewees.

FINDINGS: OPINIONS AND BEHAVIORS OF THE FITNESS COMMUNITY – BETWEEN TECHNOLOGY AND WELLNESS The Experts’ Points of View During the background analysis we proceeded to bring out the point of view of 8 experts – personal trainers, sports physicians and developers of dedicated apps – who highlighted relevant aspects in the use of specific technological devices for motor activities and self-care. First of all, doctors and trainers use and advise apps in a moderate and marginal way with respect to their field of action, relegating them mainly to supplementary utility programs to better complete the activities and guarantee the customer a tool to use independently, while following a protocol (health, medical or training) structured by an expert. The features with the greatest interest were the aforementioned self-tracking and life-logging (food diary, pedometer, heart rate monitor and calorie counter), but also support and coaching, as well as pop ups and reminders of physical activities to be carried out or personalized training protocols. According to most experts, users can increase their skills through the use of devices and the analysis of personal data obtained. The group unanimously agrees that most people generally have few skills in fitness, health and wellness. Rather, a presumption of knowledge emerges which is also corroborated by the use of the App and the use of content found through the Internet. In this way, however, the role of professionals in the sector is made less authoritative. The user who goes to search on the internet searches “at random” and finds what he finds and decides that it is true, while the user who relies on the Facebook page of a professional, at that point, has more possibilities, if the professional is a trustworthy person, to find content. [Expert 1, personal trainer, F] An influential aspect of the phenomenon investigated is the perception of free time together with the need to maximize efficiency – both typical characteristics of the current individualistic model of selfcare. According to experts, the apps are chosen mainly for their ability to empower users, making motor activities possible at any occasion of free time, significantly reducing costs compared to the requests for services of professionals in the sector. [Mainly interested in] the fact that they are in quotes at no cost, so the fact of saying “I can” or even the fact that I can do it from home, because many apps also give you activities that you say “you can do at home without Go to the gym”. [Expert 4, personal trainer, M] On the other hand, the relevance of the concepts of well-being, health and athleticism are increasingly changing the perception of sport, physical activity and self-care, alongside a model of commitment and “self-entrepreneurship” of the subjects that also pervades leisure. The social presentation of the self is thus an immediately appreciable result, a visible expression of one’s body or sharing of one’s activities for the purpose of socially awaited approval (Salisci, 2016) and governed by established aesthetic standards. These acts of “estimacy” (Tisseron, 2008) also partly justify “social” practices, when people are 411

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posting images, statements and opinions in the guise of repeated controlled displays, presentations of the self and testimonies of their individuality, as if responding to the mottos “seen, ergo sum” (Bauman & Lyon, 2015) and “share, ergo sum” (Turkle, 2011). According to the interviewees, it is possible to combine the opportunities provided by digital tools and enrich one’s social speech through the dissemination of personal content - user generated content - produced during fitness and wellness activities. Everyone likes to brag or otherwise show others how good we are, how strong we are or if we have improved, if we have set a new record in an exercise rather than a run, so yes, surely sharing is important. Some of my clients want or even ask me to be filmed during training! And what do they do with the movies next? Ah, they share them on their social media networks, they send them to their girlfriend, to their boyfriend, they show that they are working in the gym; just as one publishes the photo of the day at the beach on Facebook, they also publish training in the gym. [Expert 2, personal trainer, F] An experience lived alone does not make sense, basically, so if you enjoy doing certain things, you also enjoy sharing them. [Expert 7, app developer, M] Sharing with one’s own friends is a sort of extension of the single experience aimed at satisfying the natural human predisposition to social interactions. In this way, people observe themselves and observe others at the same time, confirming shared relational contents. Social media networks used in fitness therefore become an additional tool in the hands of users who are content curators of themselves, expressing how in a society of individuals each must be an individual (Bauman, 2008: 4) distinct and similar to the others. These aspects also emerge from the perspective of the interviewees, who see the possibility of sharing activities and personal data as an essential specificity for the diffusion of technologies, as well as for fitness and wellness activities. The places of socialization are certainly changing because now […] Facebook is part of everyday life and part of the relationship, in the sense that there are entire communities that live on Facebook and that maybe in everyday life they don’t even know each other. [Expert 3, personal trainer, M] Before, maybe you were alone in a gym where you did your workout, you talked to the people around but you weren’t with them, and instead with social media you can share the workout with someone else who is not in the same place where you are, but it does the same things. [Expert 7, app developer, M] At the same time, opportunities for gamified activities are highlighted as relevant aspects of “social fitness”. Gamification occurs when physical activity is organized by the apps in a sort of game/competition (with the App itself or with other users or friends), or it is aimed at obtaining support or responding to publicly made commitments. The competitive spirit and the desire to obtain support is accompanied, albeit in a contained manner, by the desire to promote health practices with the aim of encouraging forms of social fitness and collective well-being. 412

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For some individuals the intention of promotion and dissemination of information for their own sake are modest, but these increase considerably when they are associated with the desire to exercise social influence, to show oneself or, above all, to promote personal commercial activities. Few trainers do it for others, to stimulate others, unless ... they do it only, in my opinion, for a marketing aspect or to show that they are the best. [Expert 4, personal trainer, M] [This] has become the only possible line of healthy marketing that leads to the sharing of good content. [Expert 1, personal trainer, F] It is clear that in order to utilize the multiple opportunities offered by technologies and social media, the interviewed group remains critical of their unchallenged use, confirming instead the need to consult serious professionals for body care and for carrying out healthy activities.

The Point of View of Practicing Users The interviews carried out with the 20 users, practicing physical and health activities and with knowledge of the technological possibilities, revealed the complexity of meanings and the individual experiences related to the instrumental use (minimum or maximum) of devices. In the face of highly differentiated opinions and uses, it is possible to think of a continuum that goes from a minimum and instrumental use of digital technologies, to a maximum and fundamental one for carrying out physical activity. I have good interactions with people who train with me, sometimes we do the same things and in any case we spend a lot of time chatting and joking, otherwise training would also be less enjoyable. If I have to do the math, I almost always train when I know there are friends too. [User 1, M] Smartphones have changed interactions a bit, because it is easy to establish them through the social media network after you have met briefly live, because you break the shyness, you often talk in chat, you can see photos and personal information without asking and immediately having an idea of ​​the person you talk to. [User 2, F] Almost all the users interviewed make intensive use of social media networks and the web to search above all for information and content of a health, food or sports nature. Secondly, to share contents. On the other hand, when they find themselves evaluating the contributions of other users, they manifest a highly critical propensity, particularly towards forms of protagonism or in the presence of incorrect, irrelevant, or strictly personal information. With respect to digital devices, almost the entire interviewed group expresses itself in two ways: on the one hand, functionality is appreciated, on the other a critical vision emerges, linked to the consideration of “fashionable” gadgets, therefore tools not strictly necessary for sports. With regard to the technical functionalities of apps and devices, most of the interviewees believe these tools are useful for increasing the knowledge and consistency of users, qualities that never replace the experts in the sector. This increases the awareness of users about their own body and offers the possibility to carry out analyzes and discuss their data with other people (Quantified Self) live or in online groups, both for technical purposes and for playful/interactive purposes. 413

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It is useful, as it can help even the less experienced; it is, however, also dangerous for those who cannot use it with the right precautions: it is useless to use an application to train and which provides any type of card, where, however, the user does not know the correct execution of the exercises. [User 5, M] I read the data that the device and the app gives me carefully, I keep a history of my workouts and I also make statistics with the past years to see how my training changes and how my body’s response changes; I definitely feel more experienced, because I give exact numbers and values ​​to my feelings and my results, so that I can then try to understand what influences my performance the most. I definitely feel more complete, and when I talk to my trainer about this data he gives me his opinion and together we decide how to improve training and nutrition. [User 7, M] For most of the users interviewed, however, the possibility of providing and obtaining immediate social support remains fundamental. If the request for support and motivation relates to the individual egoistic dimension, it may happen that the effects (by direct will or as a side effect) also translate into social and collective promotion. We motivate each other, we give each other strength especially in my sport where it is not immediate to see results. Personally, I also need moments for myself during training. [User 4, F] Photos and videos are useful not so much to identify with other people, but to get ideas and stimuli from them to increase your physical and health status. The sharing of one’s results and activities, although I rarely practice it, could be a stimulus both for themselves and for others, who could be pushed to do more. [User 8, M] Finally, the interviewees underline how the use of social media networks is an expression of a noncommunity sociality, often characterized by narcissism and voyeurism (Jin & Muqaddam, 2017). As Di Gregorio argues, the single individual, today, in the age of the smartphone, participates and, in some ways undergoes, a complex and multilateral interaction in which this interaction is promoted to satisfy a need to appear and be a protagonist (Di Gregorio, 2017: 15). This sometimes also happens for groups and online communities based on common interests. The health and sports orientation does not escape these dynamics, even when it manages to create interpersonal relationships, sharing content and information oriented to wellness practices through social media. When I train with my friends we often take selfies together for fun and then we post them on Instagram and even Facebook. Sometimes we take selfies even by ourselves and we challenge ourselves to see how many likes they put in. [User 15, F]

The Points of View of Users Through Personal Contents on Social Media Networks Seeing the boundaries between the virtual and real blur, the testimony on social media networks of health and wellness activities carried out in daily life becomes an important element, not only in the processes of sharing and building understanding, but also of personal identities.

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The netnographic analysis of the posts on social media networks was carried out by the manual collection and analysis of speeches in thematic Facebook groups (in particular, Malati di Palestra Gruppo Ufficiale) or through hashtag searching on Facebook and Instagram (#fitness or #quantifiedself, for example), both in Italian and English language. This analysis revealed the experience of people and the culture expressed in places of informal interaction – such as gyms or outdoor spaces – where sports or physical activity are practiced. If it is true that in social media the risk of excess advertising of intimate capital appears high, it is also true that the virtual space that characterizes it shows itself to be a “natural” habitat, thus necessary for the presentation and narration of the Self. These activities are developed in order to acquire, share information and develop social relationships starting from the physical activities carried out by the participants. If it is possible to recognize in this communication modality a social support and the promotion of benefits linked to sport – so as to talk about social fitness – on the other hand it is necessary to carefully evaluate the social risks associated with symbolic representations that emerge in environments without regulatory or control references. As mentioned by Zani, 1995, within the latter aspect there exist “naive theories, typical of common sense, [...] in which profane representations are privileged, produced by ordinary people, often distant or even in contrast with scientific representations, often spread easily” (Zani, 1995: 486-487). In that context, the role of influencers is gaining huge momentum – often considered worthy of authority solely on the basis of the number of followers and shared content. Through apps and devices for sport, the world of fitness and wellness appears to take on an individualistic dimension. Through self-tracking and life logging practices, this trend is further configured by technologies that enable the building of the Quantified Self. In other words, this means giving one’s body performance and physical activities numerical and objective measurements through the surveys, further increasing self-awareness. Data confirms it, #SexIsGreat! Even if it cuts into some of my sleep time. In my case, it boosts wellness indicators, including HRV: the holy grail of wellness by 46%. Hmm, apparently, I need to optimize my stress level, too low at the moment so I’ll get some “eustress” not “distress”, so that means a good gym session is in order this morning! This surely will take me to #peak zone, where I need to be to tackle the long and exciting day ahead of me. #BeYourOwnScientist #QuantifiedSelf #KnowThyself [Posted on Facebook, 23/05/2018] Although this is accompanied by a private and individualistic welfare model, on social media networks and online groups the phenomenon assumes connotations of dialogue and shared creation of knowledge, but also of competition, support and motivation. In an unusual incident, a #FitnessTracker ended up saving the life of a 73-year-old woman with large blood clots in her lungs - by indicating a spike in heart rate which allowed her to call for help in time. [Posted on The Financial Express, Facebook, 09/04/2017] Back on with my misfit flash fitness tracker. Anyone have one and fancy being friends on it, let me know:) I need some fitness scores to keep me motivated. #fitness #healthy #fitnesstracker #healthylifestyle [Posted on Facebook, 05/10/2017]

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Omg [Oh my God] I can’t believe I just found fitbit challenges. I am soooo competitive. I’ll bet this is going to help me kick it into high gear like never before. Fun fun fun!!:) #gamifiedfitness. [Posted on Facebook, 27/01/2015] Here too, however, one should take into account a wide spectrum of risks, including: the testimony of a hedonistic and retributive sense present in many user comments; attempts to build social identity or participation in tribes of interests through the use of experiential goods. Can’t let the rain stop me doing my #10000steps! Hope you’ve managed yours today x x #faceoil @ iamsairakhan #walk #running #fitness #fitbit [Posted on Instagram, 05/06/2017] Power of will! -20kg from august to December 11th! special thanks to @maiullarinutrizionista from @ strategicnutritioncenteritaly who help me with my nutrition program and to @per4mnutrition_europe who provide me all the supplements that I need #per4m #per4mnutrition #strategicnutrition #gym #motivation #IFL2018 [Posted on Facebook, 12/12/2017]

The Web-survey The fourth phase of the study was aimed at investigating the insights that emerged in the qualitative research part, namely the multidimensionality of the phenomenon investigated in its meanings, practices, and rituals through the opinion of a significant number of users. In response to this need, a dissemination was prepared via e-mail, WhatsApp, Instagram and Facebook; a total of 321 valid questionnaires were collected. The respondent group consisted of 170 females (53%) and 151 males (47%), with a mean age of 31 years. The subjects were generally distributed in the following age groups: 19 respondents (6%) were 50 or older; 36 (11%) were between 40 and 49; 86 respondents were between 30 and 39 (27%)); 173 were between 20 and 29 (55%); and finally, only 7 were aged 19 or less (2%). Among the interviewees, two main groups and two subgroups were distinguished in relation to the types of physical activities practiced: 27 people (8.4%) carried out team or competitive activities (9 also carried out individual activities in the gym), and 294 (91.6%) carry out almost exclusively individual amateur activities. Of the latter, 121 (41.2% of amateurs) predominantly carried out outdoor activities individually or in small groups and 173 (58.8%) - in gyms, fitness and wellness centers, in the company of other people. The major portion of the interviewees pointed out “attention to health” and “improvement of physical shape” as the leading motivation to carry out sports activities. It is important to note that the answer centered on “health” (in a question that included a maximum of 3 choices), was provided by all as the first option. This was aligned with the affirmation of “sports health”, found in sector studies (Waddington & Smith, 2018), as well as in the European-wide surveys on the growing importance of sport and physical activity for health purposes. As second and third choices, the answers “physical improvement” and “well-being” had the highest preferences, followed by “aesthetics” and “weight loss”. This confirms the concept of health as a socially constructed notion: on the one hand, it is individually redefined on the basis of the canons of aesthetics and body tone typical of the fitness model; on the other, it is reinvigorated by the most recent wellness standards (Russo, 2011, 2018). 416

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All respondents were familiar with digital devices (smartphones and wearable devices) and apps for Health and Fitness, but not all used these technologies during their body care activities. Within the research group in question, as many as 125 people (38.9% of the total) did not use devices or apps, preferring a more traditional routine in relation to fitness and wellness activities. It is interesting to note that within this set, two groups were distinguished: a first sub-group, made up of 50 people (40% of the group, with an average age of 29), who also use smartphones during their activities, physical and well-being, mainly to listen to music, call, or write to their friends. The second, was made up of 75 people (60% of the group) that never use smartphones during their activities. The profile of this group was average age of 34 years (the highest of all the groups highlighted), with 9 people over 50. 51 people (15.9%) reported the use of a smart device, but not the apps; the group being distributed as follows: 25 females (average age 30 years) and 26 males (average age 32 years). When speaking of “devices”, these participants referred to smartphone stopwatches, heart rate monitors or bracelets, used without a separate app and focused on monitoring heart rate, calories consumption, and training time. Those who, on the other hand, used the apps but not the digital devices during their physical or wellness activities, were 16 people (5.0%) – 11 females and 5 males, whose average age is 30 years. Seven people did not use their smartphones at all, while 9 used them during their activities. The latter made manual entries of personal data (mainly in food diaries), used life logging services, or the app coaching services. They also enjoyed sports centered videos/images from social media, but they did not use smart services and self-tracking activities of wearable devices. The remaining 129 respondents (40.2% of the total with an average age of 31) used both wearable devices and apps for Health and Fitness; only 21 users did not use the smartphone during training compared to 108 who, in contrast, used it. Compared to the totality of respondents, 56.1% use digital devices, 45.2% use the App; to use both, however, is 40.2% of the reference set. Within the investigation the relationship between physical activities and digital technologies, the macro group of those who use at least one digital tool (app or device), consisting of 196 people (61.1%), stood out from the group of non-traditionalists users of the same technologies (i.e. those who, at most, use their smartphone to make phone calls, for instant messaging services or to listen to music) of 125 people (38.9%). To the question “How much do you think digital technologies useful”, it was possible to see a double position of the interviewees: if a generally favorable opinion emerges for use in everyday life (82.3%), only 45.2% of respondents consider these devices useful or very useful for sport activities, while 54.8% consider them of little or no use. To back up this observation additionally, the testimonies given clarify the positions of some interviewees about the changes following the introduction of digital technologies and apps. They have made information and communication simple and quick at hand. [User 112, M] Possibility for everyone, at low cost and with absolute simplicity and immediacy, to keep every possible parameter under control (obviously, with less accuracy than more professional tools/methods). [User 130, M]

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People become more aware of what they are doing during a training session. They become “personal trainers” of themselves, in the case of individuals who know how to make the most of it. Also, greater involvement with other individuals. [User 144, F] Many tend to replace the real figure of the graduate in motor science or in any case of the personal trainer with apps that calculate a training program that is not always correct on the basis of little data collected and with poor results in performance related to the lack of supervision by a professional. [User 179, M] Greater control and monitoring of health. [User 255, F] Overall, the interviewees paid specific attention to the essential functions of apps and devices, such as self-tracking and self-awareness tools (37.4%) - ones that are capable of providing support and facilitations in achieving their personal goals (20.7%).), as well as to allow access to additional information (13.8%). To give a negative or neutral opinion to these aspects among those who use them, are respectively 7.5% and 11.5% of respondents. By eliminating from this analysis the group that didn’t use devices or even apps, the frequency distributions changed: 87.3% consider digital devices useful or very useful during the activities of daily life, while 64.3% consider them useful or very useful during sports or wellness activities. In both cases, the share of those who do not consider them useful was reduced. People who reported to not use these technologies mainly refer to the use of smartphones, and the non-use of specific apps involves considerations simply related to daily life and non-direct knowledge. App users, on the other hand, express a different relationship with these technologies, which appear to be tools for daily support in the search for physical and active well-being. The e-coach function constitutes an undoubted element of interest for users for their activities, but it raises important questions about its actual usefulness: 41.4% use it as a substitute for trainers or experts, while 58.6% use it as a supplement to expert knowledge. App and virtual training are also an important element of motivational support: 60.6% of respondents are “quite” or “very” happy after having completed the training set by the virtual trainer, with 54% of those responding that support satisfaction decreased if they fail to meet the established objectives. 90.4% of those who use apps and devices observe the data and the performance: it’s a relevant answer, related to improvement, self-awareness and wellness activities, also comparable to the typical practices of professionals. Of those, who did not use digital technologies, only 31.7% kept track of their performance. All those who claimed to have attempted improving themselves autonomously in the future have provided this answer as a first choice: this confirms the value of the liberal individualistic model (Lupton, 2015) and of the self-reflexivity activities (Beck, 2011) such that it happens, through widespread technologies and knowledge, a re-invention of cultural and health practices (Elliott, 2013) typical of late modern culture. The activities of life logging and comparison with previous data obtained a preference of about 1/4 by users, followed by the monitoring of data relating to health, nutrition and personal weight and the search for correlations (more typical of the Quantified Self model) between rest, nutrition and workouts. All these activities fall within the liberal individualistic model, while the practices of sharing personal data are carried out only by 7%, thus opening questions on the ability of devices to be socializing tools or directly beneficial to the community. This is based on the consideration that social utility is the result of an externality of the individualistic use of devices and social media networks. 418

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In general, with regard to actual, perceived usefulness or simple motivational support, it emerges that those who use these technologies practice more physical activity (Table 1) and tend to have healthier habits, including controlled diets. Table 1. Frequency of physical activities in relation to the use of apps and devices     No

Use of Apps and devices – Sport and fitness activities

    Yes

    Less than 1 per week

    5%

    6%

    1/2 per week

    14%

    10%

    3/4 per week

    14%

    24%

    5 or more per week

    5%

    12%

    No answer

    6%

    4%

With a more specific reference to those who use apps and devices, it can be noted that the use of digital technologies is for the majority of those interviewed a relevant and decisive element for their improvement during the performance of physical health and wellness activities. It was also interesting to observe that there is a different percentage distribution in the items observed: with regard to constancy, as many as 7 out of 10 people have changed and improved the frequency of their activities through technologies, like those who have improved their perceived well-being. As for consistency, it is easy to see that there is a correspondence with the appearance of pop-up notifications from apps or the social pressure that is experienced through participation in groups and communities of interest. As regards the concept of well-being, there is greater difficulty in separating or evaluating the real improvement of one’s well-being from external conditioning and remuneration forms, such as the pleasure of using specific and fashionable technologies, or participation in social activities and positively approved. As regards the evaluation of performance and health, about 6 out of 10 have noticed an improvement: this is to be explained by a consideration regarding the meaning attributed to the terms and to the evaluation skills of the interviewees, since, if you do not have certain knowledge or previous experience in sports or health, people tend to underestimate or overestimate any progress and improvements. Furthermore, people may not be able to interpret the data that emerge from the use of the devices or to make the correct changes, especially if the use of such technologies replaces the advice of professionals and experts, relying on their previous habits and knowledge. The appreciation of digital technologies focuses more on the technical functions (81.4%) and on the ability to carry out self-tracking and self-monitoring (89.0%) of their activities, while the explicit interest in sharing functions on social media networks or in communities (9.0%) and interest in pop-up recommendations and notifications (19.3%) is lower. Here a further reflection is necessary: ​​since the group was very varied but relevant in quantity, it was not possible to understand what the personal meaning of what each one attributes to the usefulness of the individual categories investigated was. Nevertheless, it emerges that each subject seeks technological answers to every need (material or not). Secondly, apart from a group of users, sharing through technical apps does not appear relevant either for a sense of personal satisfaction or for a desire for relational involvement in forms of health and well-being.

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The sports and wellness activities carried out in groups by the interviewees reveal particular uses and activities, with a very interesting distinction considering the group of those who use apps or digital devices during the activities. People prefer an individualistic use of their personal technological devices (7 out of 10 declare that they are not competitive); on the other hand, 4 out of 10 claim to confront each other and 6 out of 10 to give themselves support and motivation during training. With regard to social caring, 4 out of 10 people promote (very often or frequently) good eating and physical activity practices, while only 1 in 10 favors the use of apps or devices. From the perspective of this survey, it is clear that, for the purposes of promoting “social fitness” (Lupton, 2017), personal digital technologies are not decisive, while facilitating users in sharing, comparing and information on health practices. At the same time it is useful to remember that social media networks, online communities and peer groups act as activators of self-observation mechanisms that allow the development of a medial-based reflective attitude (Boccia Artieri, 2012: 108) capable of contributing to orientation and development of innovative behaviors. It is no coincidence that 67.6% of respondents declare that they are part of groups or communities, a share that grows to 73.5% if we exclude those who do not use apps and devices during sports and wellness activities. Furthermore, 40.1% of the interviewees participate in online groups and communities, whose main themes are fitness and wellness: excluding those who do not use apps and devices, the share rises to 52.8%. The use of digital technologies therefore correlates with an increase in digital sociality through participation in collective online discussions related to self-care. This is mainly done through dedicated apps, computers, sharing their experiences or searching for information, contents and discussions of their own interest.

CONCLUSION Observing the multiple uses and opportunities of digital technologies in the field of sports practices oriented to the health of individuals, in the context of a general ludification process of contemporary culture, contributes to the increasing knowledge and aspects of the increasingly widespread wellness culture (Russo, 2018). The research path illustrated above, albeit with the limitations of the case, is an exploratory study of the relationship between health and individualism through apps and digital tools, an expression of a broader socio-cultural tendency to improve health, lifestyles and greater responsibility of the concept of well-being, as indicated for some time in the main directives of the World Health Organization of 2014. Respectfully to these objectives, the use of digital technologies in the field of sociological research, as well as the combination of qualitative methods – in the first part of the research – and the quantitative survey – in the second part – exponentially increases the descriptive abilities of sociology with the aim of understanding cultural attitudes, social relationships and behaviors of specific social groups. Considering the ambivalence of today’s social life, that is a web society (Cipolla, 2015) with widespread boundaries that see forms of mediated communication and hybridization with technologies and virtual environments (Longo, 2005), the development of interpersonal relationships is oriented according to the channels of me-centered relations (Castells, 2006) and networked individualism (Rainie & Well420

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man, 2012). This network sociability, in particular for the social media networks, leads to a continuous production and sharing of personal content and constant participation by users (Porter, 2008). Often, however, the contents respond to the needs of self-presentation, fear of missing out (Reagle, 2015), narcissism, estimacy and the desire to show oneself (Tisseron, 2008). Starting from the polysemy of the personal contents, their considerable deconstruction and the most diverse motivations for the expression of personal contents, the authors realized the need to integrate the interpretative qualitative approach with a descriptive statistical analysis of the reference set, before building an interpretative grid for content analysis. This need refers in particular to the context of digital ethnography applied to small or significant groups on social media networks, forums and online communities, or through the search for textual/visual content or key-words, hashtags, channels and thematic groups, which lack the possibility of generalizations and references to a wider population. Supposing the online reference set produces spontaneously unstructured UgC (User-generated Content, such as images, videos, posts, etc., found on communities and social media networks), a sociographic survey of the set of people considered by the researcher is required in order to have a view of the population and to search for any homogeneous subgroups, and then proceed to the analysis of the visual and textual contents. On the other hand, if the online reference set produces content on request (intrusive approach), it is necessary to support the investigation with a survey (sociographic and scaling of attitudes/opinions relating to the survey topic) in order to describe the population and segment it, aiming to understand and correctly evaluate the contents, or to eliminate any errors or viciousness. At this point, an important method to reduce the errors of analysis and interpretation related to the data collected through digital and online tools – with particular reference to the content generated by users on social media networks (status, post, personal images, relink, etc.) – is to combine the analysis of the contents (textual and visual) with a statistical and sociographic analysis on attitudes/behaviors of the population. Furthermore, using a mixed method approach, it is possible to develop two important analyses: •

•

Analysis of the population in subgroups, through models of group aggregation (minimum intra group and maximum inter group variance) obtained by means of discrimination with the uniqueness of the answers to pre-established variables chosen by the researcher, which can thus allow a specific subdivision of the population in homogeneous groups and liken it to social reality. Correspondence analysis, in order to process descriptive information to capture any regularities in the groups and in the information (integrated through secondary variables obtained from the content analysis), with the creation of secondary data and factors typical as trends, characteristics of the group, particularities, etc.

This integrated method allows a more complete interpretation of the amount of quantitative data obtained in the digital research phase, as well as the correction of the interpretative grid or the elimination of improbable cases, unable to fit into a homogeneous group. Following this, therefore, it is possible to reduce the margin of error in the phase of analysis and re-elaboration of the information and to improve the quality of sociological information. There are many studies that show a positive correlation between the use of apps and wearable devices, an increase in physical activity and an improvement in the global health and well-being of the population, since, in addition to greater observation and care of the body, these tools favor and enrich individual and social experiences in the fields of sport, fitness and health. In addition to supporting an 421

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ideal of a healthy and productive worker typical of the hyper-individualist project of modernity (Beck, 2000), these devices – through the logic of interactivity, sharing, augmented experience, etc. – actually support a program of social relationality. Precisely in free time practices such as sport and physical activity, they are transformed into places of conversation, of daily narration, of promotion of the individual, as well as areas of inclusion and socialization. This is how the Australian sociologist Deborah Lupton expresses herself in defining the renewal of fitness practices through the use of social media. In fact, with the term “social fitness” she refers to those practices of sharing personal data in order to facilitate motivation and the achievement of personal goals (Lupton, 2015: 8). Sharing, however, encourages individuals to unite with other people, maintaining the social commitment formally taken as apps and social media networks stimulate the individual to act, share and conform through online relationships (however unstable and temporary). Such a social fallout of digital technologies therefore implies an increase in the perspective of social wellness programs, that is, the expansion to a collective form of a discourse of the ideal citizen that combines private objectives with the public good, the self with the community, configuring the ideal of a socially fit citizen, in a new and digitized form of bio-citizenship (Lupton, 2015: 14). This means that the multiple devices used in sport today allow the development of a culture of participation (including civil), which finds its most innovative stage in physical-sporting practice. In particular, the use of digital technologies in the practice of fitness/wellness proves useful not only for personal well-being, but also by virtue of a collectively oriented goal. Therefore, a prerequisite for a global well-being that contributes to the construction of the health resource when personal choice is exercised in order to guarantee the collective good, and vice versa, personal satisfaction becomes an essential element for the collective good (Soper, 2007: 215).

ACKNOWLEDGMENT The chapter is conceived by both authors; E. Bagnini is author of par. 2 (Epistemology …), 3 (Findings …); G. Russo wrote Introduction, par. 3.1 and Conclusion.

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Jin, S. V., & Muqaddam, A. (2017). “Narcissism 2.0! Would narcissists follow fellow narcissists on Instagram?” The mediating effects of narcissists personality similarity and envy, and the moderating effects of popularity. Computers in Human Behavior, 81. Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26. doi:10.3102/0013189X033007014 Kadushin, C. (2012). Understanding Social media networks: Theories, Concepts and Findings. Oxford University Press. Kozinets, R. V. (2010). Netnography. Doing Ethnographic Research Online. Sage. Krippendorf, K. (2018). Content analysis: An introduction to its methodology. Sage. Leedy, P., & Ormrod, J. (2001). Practical Research: Planning and Design. Merrill Prentice Hall and SAGE Publications. Longo, G. O. (2005). Homo Technologicus. Meltemi. Lupton, D. (2012). Digital Sociology: An Introduction. University of Sidney Press. Lupton, D. (2013). Quantifying the Body: Monitoring and Measuring Health in the Age of mHealth Technologies. Critical Public Health, 23(4), 393–403. doi:10.1080/09581596.2013.794931 Lupton, D. (2014). Apps as Artefacts: Towards a Critical Perspective on Mobile Health and Medical Apps. Societies (Basel, Switzerland), 4(4), 606–622. doi:10.3390oc4040606 Lupton, D. (2014). Self-Tracking Cultures: Towards a Sociology of Personal Informatics. In Proceedings of the 26th Australian Computer-Human Interaction Conference (OzCHI ’14) (pp. 77-86). New York: ACM Press. 10.1145/2686612.2686623 Lupton, D. (2015). Health promotion in the digital era: A critical commentary. Health Promotion International, 30(1), 174–183. doi:10.1093/heapro/dau091 PMID:25320120 Marres, N. (2017). Digital Sociology. The reinvention of social research. John Wiley & Sons. Maturo, A. F. (2012). La società bionica. Saremo sempre più belli, felici e artificiali? FrancoAngeli. Maturo, A. F. (2014). Fatism, Self-Monitoring and the Pursuit of Healthiness in the Time of Technological Solutionism. Italian Sociological Review, 4, 157–171. Maturo, A. F. (2014). M-Health e Quantified Self: Sviluppi, potenzialità e rischi. Salute e Società, 13(3), 161–170. doi:10.3280/SES2014-003012 Mauceri, S. (2017). L’avvento dell’era dei mixed methods. Nuovo paradigma o deadline di un dibattito? Sociologia e ricerca sociale, 113, 39-61. Moriarty, J. (2011). Qualitative methods overview. NIHR School for Social Care Research. Padricelli, G. M., Punziano, G., & Saracino, B. (2020). Virtual vs Digital: Examples of Netnography and Digital Ethnography in Tourism Studies for a Comparison between Methods. Athens Journal of Social Sciences., 8, 1–20.

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Yang, C.-H., Maher, J. P., & Conroy, D. E. (2015). Implementation of Behavior Change Techniques in Mobile Applications for Physical Activity. American Journal of Preventive Medicine, 48(4), 4. doi:10.1016/j.amepre.2014.10.010 PMID:25576494 Zani, B. (1995). Salute, malattia e processi psicosociali. In L. Arcuri (Ed.), Manuale di psicologia sociale. Il Mulino.

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MyFitnessPal, Endomondo, Under Armour Record, Runtastic Running&Fitness, Runtastic Results Corpo Libero, Nike+ Run Club, Nike+ Training Club, Strava GPS Correre Ciclismo, Sworkit, Sfida Fitness 30 giorni, 7-Minutes, Google Fit, FitBit, Sports Tracker, Conta-passi e Perdita Peso, Samsung Health. The Apps were observed in December 2018.

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Chapter 26

The Relevance of Relational Ontologies in Driving New Methodological Approaches in Virtual and Digital Contexts Monica C. Scarano FGES Université Catholique de Lille, France

ABSTRACT Qualitative methods are traditionally roots in the anthropological person-centered field. In a virtual and digitalized society, the presence and the agency of technology and devices need to be considered as well. The purpose of this chapter is to contribute conceptually to the reflection of a different ontological perspective in qualitative research. After presenting some qualitative methods centered on humanist ontology and its limits in a virtual and digital society, the author explains the interest to adopt relational ontologies to adapt some qualitative methods in order to overcome the previous limits. This chapter deals with emerging qualitative methods linked to relational ontologies that move away from the individualistic vision of the consumer and also focus on the technological object.

INTRODUCTION In the age of Big Data, thick data from qualitative research continues to be of interest (Thompson, 2019). Often rooted in the anthropological and ethnographic tradition, the qualitative methods used in social sciences have been inspired by humanist ontology, which focuses on the human as a source of knowledge (Levy & Hollan, 2014). However, in a digitalized and virtual word, social personal interactions as well as formal and informal relationships are often mediated by technology. Moreover, social phenomena are not only anchored in a geographical space as people move and both personal and professional connections can be networked, sometimes offline and sometimes online. This perspective is a challenge for qualitative methods. For example, Law (2004) states that to understand a fluid or networked word, ethnography needs to work differently. For example, computer-mediated communication is incorporated DOI: 10.4018/978-1-7998-8473-6.ch026

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 The Relevance of Relational Ontologies in Driving New Methodological Approaches

into every aspect of social and daily life, so technological development increases the range and scope of social spaces and the time and forms of participation. And so, in a digitalized and virtual society, spatial relationships cannot be considered as distinct (Dholakia, Reyes, & Bonoff, 2015) and temporality represents a challenge to conducting netnographic research (Lugosi & Quinton, 2018). Recently, several consumer researches have begun to study fluidity, mobilities and networks (Thompson, 2019) with a specific attention to the technological mediator (Epp, Schau, & Price, 2014) that becomes an active actor (Morgan-Thomas, Dessart and Veloutsou, 2020). Researchers who focus on movement networks of people and objects in both geographical and virtual spaces (Arsel, 2016) use different theories (e.g. assemblage, actor-network or mobility turn) that support an object-centered approach (Campbell & McHugh, 2016) linked to relational ontologies. This is a different perspective characterized by the primacy of relationships human-object over entities. That is to say, objects, understood as nonhuman entities (such as technologies) became more and more important, for example to mediate relationships and consumption (Epp, Schau and Price, 2014) or to generate human practices (Morgan-Thomas, Dessart and Veloutsou, 2020). Different sociologists explained what are nonhuman entities (Callon, 1999; Latour, 2005; Law, 1987). This term is used to denote entities as diverse as texts, natural phenomena, animals, material structures, transportation devices, tools and technical artifacts, economic goods (Sayes, 2014, p.136). However, if more and more researchers use theories derived from relational ontologies to understand human and nonhuman relationships, little is done to adapt qualitative methods to relational ontologies. This chapter invites the reader to re-examine the qualitative methodologic challenges in conducting researches in a networked, virtual and digitalized context to overcome some limits of methods linked to humanist ontology. First, the author presents some qualitative methods human-centered, largely used in consumer research and underline theirs limits in a digitalized and virtual context. Second, it is introduced the perspective of relational ontologies to overcome these limits and to shift from a human-centered perspective to an object-centered perspective to consider nonhuman agency, non-linear temporality and networked social spaces. In relational ontologies the term ‘object’ is intended as all that is not human, such as technology, devices, artifacts, images, information (Lucarelli and Giovanardi, 2019). In this chapter the author use the term ‘object’ or ‘technological object’ to specially refer to devices, technology, platforms, artificial intelligence (AI), robots. Third, some adaptation of existing qualitative methods under the perspective of relational ontologies, such as netnography, interviews and videography are explained. Finally, the author proposes some possible adaptations of others qualitative methods, such as object observation, object-centered autovideography and AI interviews as suggested future opportunities.

Qualitative Methods Human Centered and Theirs Limits in a Virtual and Digitalised Society Qualitative methods used in marketing and consumer research are largely anchored in social and anthropological tradition (Levy, 2006). These methods include different techniques such as personal interviewing, focus group interviewing, projective techniques, visual methods, ethnography. Linked to different theoretical perspective such as phenomenology, symbolic interactionism, or ethnomethodology, these qualitative methods are centered on the persons to understand their everyday life, their point of view, their subjective and social constructs of their world (Flick, von Kardoff & Steinke, 2004). The nonhuman entities, such as technologies or artifacts, emerge in the research based on the experience which human actors have and express. For example, Zhong, Balagué and Benamar (2017), studied the usage of connected objects using semi-structured interviews. In their research, the authors used a content analysis

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of 18 interviews to identify five modes of uses linked to the appropriation process. This example shows that the technological object and the relationship between human and nonhuman is not always directly observed and analyzed. In consumer research, practice observation is another technique to play attention to socio-material arrangements. The use of technologies by consumers has often been studied focusing on human actions, because in practice theory human activity remains central (Schatzki, 2010). Gannon and Prothero (2018) studied the practices of youtubers and bloggers using interviews and their blog posts and videos. Nonetheless, the place of technology was not the central focus in analysis. Moreover, socio-material practices are often multi-spatial. A way to follow objects and observe practices can be done by the multi-sited ethnography that ‘examines the circulation of cultural meanings, objects, and identities diffused in time-space’ (Marcus, 1995, p. 96). It is conducted by one or more researchers, and the multiplicity of site observations makes it possible to trace their link as an interconnected system of localities (Ekström, 2006). Marketing researchers have used multi-sited ethnography to study practices of the cultural consumption of ethnic communities (Peñaloza, 1994) and to assess the circulation of objects in real and virtual sites in collaborative networks of geocaching players (Figueiredo & Scaraboto, 2016). Even if this method gives attention to the multi-spatial context, the analysis once again focuses on human actions. All these examples show that in qualitative research technology and objects are not an equal central point of analysis as humans. The role of materiality in ethnographic and anthropological work has been studied during decades (Appadurai, 1988; Miller, 2005). Artefacts and more recently technological objects have symbolic and practical functions, and the social practices in which they are entangled are studied to understand how status and value are displayed and focus on transactional relations. The most important attention has been given to the function of materiality: cultural, social, political, and economic (Lugosi & Quinton, 2018). The limit of conventional ethnographic and anthropological methods to study objects is twofold. Firstly, objects haven’t got a central active place in the research as it is often considered as passive, while attention is given to human actions, feelings and experiences with the objects. So, objects, as well as all the other nonhuman entities, are the ‘missing masses’ of the social science (Latour, 1992). Secondly, objects are often considered as artefacts, while nonhuman actors can be not be limited to artefacts but include for example technology. A society that is virtual and digitalized means that human and nonhuman actors (such as technological objects) have a role in the networked sociality. That’s why it is important to examine the interaction of people with technology and their socio-material practices. Algorithms, bots, affordances, interact with humans and influence them in their social life (Marres, 2017). A human centered perspective doesn’t focus enough on nonhumans and its agency. Scholars and professionals often transposed the classical social research method in order to be implemented online, but the growing pervasiveness of the digital in our lives requires the development of more suitable methods to address the challenges of studying the digital society. This means that the human ontology doesn’t give an important place to nonhuman entities such as technology. Moreover, a digital and virtual society is geographically networked and interactions are multi-temporal and multi-spatial due to the nature of internet and technology-mediated sociality (Lugosi & Quinton, 2018). To overcome these limits, it is important to adopt a different ontological perspective in order to move away from the individualistic vision of the consumer (Askegaard & Linnet, 2011), and to also focus on the nonhuman. In the era of Big Data an object-centered approach is important also in qualitative research (Thompson, 2019).

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RELATIONAL ONTOLOGIES: THE IMPORTANCE OF THE TECHNOLOGICAL OBJECT Relational ontologies are worldviews characterized by the primacy of relationships over entities. This is a different vision of reality, compared with objectivist and substantivist ontologies that are based on the primacy of entities (Lucarelli & Giovanardi, 2019). A common aspect of relational ontologies reflected in a sociomaterial approach is the relationship between humans and nonhumans (DeLanda, 2016; Latour, 2005; Sheller & Urry, 2006). A socio-material approach is used to analyze such relationships and understand how it emerges (Latour, 1991). To do this, it is important to give attention not only to humans but also to objects. Several theories are linked to relational ontologies (Lucarelli & Giovanardi, 2019) and everyone offers an interesting overview on objects. For example, the sociological mobility turn (Sheller & Urry, 2006) considers that society is mobile and structured in networks. Multiple mobilities of people, information, images or objects can be intertwined. So, a technological object (e.g. devices, technology, robots, AI, etc.) can be studied during its physical or virtual mobility, but also because it is an anchor that allows the fluidity of other types of mobilities (such as information or images) (Urry, 2007). Another theoretical approach is about assemblages (Delanda, 2016). Human and technological objects are seen as a part of an assemblage. A change of one of the components of the assemblage can change the assemblage. So, to understand social phenomenon it is important to consider not only humans, but also, all the other types of components of the assemblage. And so, in this case, the research attention is given to nonhumans, as well as to humans. Why is it important to consider technological objects in a research? For Deleuze and Guattari (1987) assemblages are structures traversed by power relations and desires. For DeLanda (2006) ‘properties emerge from the interactions between parts’ (p. 9). In this sense, a technological object that frames our everyday life, actions and relations doesn’t have fixed properties but emerges through relationships. Studying relationships means to focus on: 1) how the object relates to people or other objects and how people relate to the object; 2) how are the relations happening now (Woodward, 2019). Following the appropriate theory, this means to focus on the process of interaction, practice and on the observation of emerging and moving actors (human and objects) (Latour, 2005); how relations are created, maintained, and what effect they have (Delanda, 2006). To study a complex, multi-spatial and multi-dimensional conception of the world is important to acknowledge the role of technology and materiality in shaping social phenomenon (Lugosi & Quinton, 2018). As word is human and material, technological objects mediate people’s relationships but also the environment, the culture and the society we live in. This is why the active role that technological objects play in this process needs to be analyzed. They are not passive, but can challenge, resist and surprise. This is why, agency can also be considered for objects. Agency is the relative capacity to influence events (Fernandez, 2015). It is linked to the notion of agencement (Deleuze & Guattari, 1987), or ‘arrangements which are endowed with the capacity to act in different ways’ (Çalişkan & Callon, 2010, p. 9). This notion emphasizes the connection between the subject and the object (Phillips, 2006). Following different theoretical approach, object agency can act in relationship with humans (DeLanda, 2006) also by their moving (Woodward, 2019). Some different theoretical approaches exist about object agency but to synthetize agency it is useful to think about: 1) the effects the object can have; 2) how these effects emerge from the connection between people and objects; 3) how we can attune to the active role played in the world from the objects (Woodward, 2019).

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The Theoretical Approach on Human and Object Relationship in Consumer and Marketing Research Several researchers have used rationality and rational materiality in their works. Bettany (2007) and Seregina et al. (2013) place human and objects on the same ontological plane. Their work explores object agency. For example, Seregina et al. (2013) investigate the agency of a pen, following its circulation during a day in different physical spaces and with different people (e.g. a seller, a worker, a student). Epp and Price (2010) studied a table used in a family which had been moved to another house of the family and how this object had become singularized. These researches are interesting because they don’t assume that artifacts have fixed use, meaning or an isolated existence. Under relational approaches they give emergence to the capacity of human and object elements to enact, to create or change relationship. This means that they assume the distributed nature of agency (Bettany & Kerrane, 2011; Hill, Canniford & Mol, 2014; Martin & Schouten, 2014) between the different elements entering in relationship. The object is not seen in opposition to the subject but as having a mediating capacity. Researchers in marketing and consumer research that focus on relations, examines the configuration of interactions to generate particular outcomes. Often, they give attention to the process and practices linked to the uses and perceptions, but influenced not only by humans but also by nonhuman actors (Canniford & Shankar, 2013; Thomas, Price, & Schau, 2013). Consequently, the challenge of the research is to account for the ways in which human and nonhuman shape the outcomes of relations (Canniford & Bajde, 2016; Canniford & Shankar, 2013; Parmentier & Fischer, 2015). This challenge is not only theoretical but also methodological. Lucarelli and Giovanardi (2019) affirm the importance of relational ontologies as a source of inspiration for the methodology. They emphasize methodological discussion and relationship operationalization and they note that each theoretical approach requires an appropriate methodology. Accordingly, for relational ontologies, an appropriate research method arguably could provide a perspective on object agency, in accordance with the linked theoretical approach.

ADAPTING SOME EXISTING QUALITATIVE METHODS TO RELATIONAL ONTOLOGIES To overcome the limits of traditional qualitative methods of research and adopt a perspective linked to relation ontologies, some researchers recently began to bring some adaptations to existing methods. These examples offer some solutions in dealing with the challenges presented in the preceding section and open to other possible applications offered in the following section. Three types of techniques are summarized and explained in this section: more-than-human netnography (Lugosi & Quinton, 2018), object-interview (Woodward, 2020) and screencast videography (Kawaf, 2019).

More-Than-Human Netnography More-than-human netnography is an adaptation of netnographie to go beyond the human, as the term indicates. Lugosi & Quinton (2018) brought out some of the limitations of netnography such as the large attention played to human interactions online, with a little attention to the place of technology and device in shaping these interactions. A technology platform can impact the way in which people have

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to interact and participate in a community but the relationship between people and technology and the technological object is not considered in the conventional netnography. Moreover, the multiple participations of consumers in several social medias, do emerge multi-spatial and multi-temporal dimension to consider about interactions. Multi-spatial dimension is linked to the practice of surfing between social media spaces and platforms to interact. The multi-temporal dimension is linked to the content on a specific platform which is temporally cumulated at different moments. Pointing out that the current use of netnography is mainly oriented to the collection and analysis of textual data, the authors underline another limitation of the method. Netnographic data are usually quantified (e.g. the number of Tweets analyzed, the threads that are followed in a blog) and the procedure is defined and standardized, while information that could derive from a flexibility of the method including human and nonhuman factors/ actors is lost. This consideration is also valid for the analysis part. The use of analytical software, as a strategy for total or partial analysis, the use of analyses done by other researchers, or the use of ‘member checks’ by members of the community are all practices that can be applied. To overcome these limits, the researchers apply the theoretical perspective of ANT (Actor-Network Theory, Latour, 2005) to introduce new elements to analyze in netnographic method. ANT is one of the theories linked to relational ontologies (Lucarelli & Giovanardi, 2019). This theory questions actions, actors, processes and relationships and the impacts they generate. Three themes of ANT are transposed in netnography: performativity, enactments and translations. Performativity means that actors, human or nonhumans, have a role in creating, transmitting, transforming, restricting, etc. For example, in social media the interactions create trending phenomena. Enactment is the creation of outcomes and effects from human and nonhuman actors. Posting, liking or ignoring content shapes trending phenomena consumed by other members of the community and act on discriminating algorithms that distribute information or are programmed to suggest other content. Finally, translations are practices and processes that deploy human and nonhuman actors in networked relations that create effects and outcomes. For example, consumers can use the capacity of a device to produce and consume social media content to influence trends, as well as Platforms and devices shape how consumers view, interpret, store or access social media content. So, more-than-human netnography becomes a new perspective that includes all these features. It gives less emphasis on procedure. It is more flexible in scope and focus of research, questioning of performative qualities of humans and nonhumans. Agency is not only human but also nonhuman. More-than-human netnography includes the performative capacity of materiality and technology, for example questioning on their enactment and it considers the place of devices, technological systems and algorithmic evaluation of practices. Not only textual and visual data are studied but also computational, technological and material dimensions of practices that produce texts, images and videos. It means that netnography also includes other acts and actors. To explain, for instance, the meaning of a site’s buttons or a platform, to decipher the chattering of bots or interpret the hidden exclusions of algorithms (Kozinets, 2020). For example, Kozinets, Ferreira and Chimenti (in press) used this method to analyze platforms and not only consumers posts, to find the ability of a platform to offer consumer empowerment. Finally, the temporal and spatial dimensions are considered. This method accepts activities across time and nonlinear interactions. Moreover, the research context is not fixed (e.g. site or forum), because it is important to understand evolving phenomena that are spatially dispersed. These principles of more-than-human netnography has potentially the possibility to create novel research questions, innovative techniques of data collection and analysis.

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Object Interview Object interviews is a type of a so called ‘material methods’, that focuses on objects (Woodward, 2019). Concretely, this means that researchers have to introduce the materiality of the objects during interviews (Abdelrahman, Banister & Hampson, 2020). This can be done not only by asking responses about the relations to this object, but by introducing physically the object during interview. For Woodward (2019) object interview can be conducted in a specific context where there are objects with which a researcher would study relationship with respondent; or in another place where objects are introduced by the researcher or chosen by the respondent. To generate a conversation about objects different questions centered on the object can be suggested to the interviewee, about his history, its evolution, its use, its origin. Observation of interaction (using also photos or video records of the interview) is an important source of analysis. For example, Abdelrahman, Banister and Hampson (2020) that used this method, asked participants to introduce their possessions during the interview and talk about them in order to better understand participant’s experiences and their relationship with objects. They developed a conceptualization of curatorial consumption around the themes of object histories, curatorial practices, future imagination and transference. This method is linked to relational ontologies for two reasons. First, because objects (technological or not) are considered to generate a response from people, so they have agency. Second, because the relationship between human and object is observed, so objects are physically introduced during interviews. Object interview is different from elicitation used during interviews, because an object is not only evoked by photos or presented to the respondent to solicit his narratives in relationship to the object. In object interview the object is physically present and the respondent can engage with it (touching, using it). There is not the narrative about the object that is analyzed, but interactions that are both verbal and material. The central analysis of the object interview is what role objects have in generating responses, silence and dialogue. Also, if questions can be different in order to explore the place of the object in the experience, narrative or activity of people, the role of the object and the effect it may have are two important aspects of this method. Relationship can be externalized in different ways (e.g. tactile, sensory and embodied ways) and also affected in how respondent talk after smelling, touching or physically engage with objects. Interview may be not only a method to assess what people think, but a space of interaction and connection, or where narratives emerge.

Videography and Screencast Videography Videography is an interesting field that can be linked to relational ontologies. This method has usually been used by consumer culture researchers to collect thick data. As for the other visual methods, videography is often rooted in interpretativism (Cova & Elliott, 2008; Tadajewski, 2006) and the analysis can vary from grounded theory in the hermeneutic cycle, either holistically or through a more formal frame-to-frame procedure (Belk & Kozinets, 2005). In video analysis, videos are treated as a text and a sequence of images (Rokka & Hietanen, 2018). However, a more relation ontology can be applied to videography to move from a representational perspective of reality, often used in anthropological methodology, to a non-representational perspective, where the video can be an ‘illusion’ (Rokka & Hietanen, 2018). Video allows ‘the productive intersection of a form of content (actions, bodies and things) and a form of expression (affects, words and ideas)’ (Deleuze & Guattari, 1987 as cited by Buchanan, 2015, p. 390). So, videos are ways of expressions (Deleuze, 1989) that can produce new realities based on the

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‘selective expression’ of the subjective position (Hietanen et al., 2014, p.2020). From this expression can emerge desire due to the passionate creative energy (Deleuze & Guattari, 1987; Kozinets, Patterson & Ashman, 2017). Using the assemblage theory of Deleuze and Guattari (1987), Kozinets, Patterson and Ashman (2017) explored consumer desire emerging from videos of food that circulated online. Movement and the possibility of non-linear chronological time are two features of videos that make it powerful and agentic (Hietanen & Rokka, 2018). For example, video can be used for theorization and presented to an academic audience eliciting more emotional participation that a written text. Under a relational ontologies’ perspective, videography as a research method has to consider coding movement and not only images in sequence (Hietanen & Rokka, 2018). Movement is not only the body one. Another type of movement is what we do online. In digital space consumers do shopping, socialize, search for information, play, navigate online. They move virtually in multiple digital spaces and have dynamic and visual experiences. This often happens in private moments and places, that are not easy to observe for researcher. For these reasons, Kawaf (2019) has proposed to use a novel method called screencast videography, to digital record these types of movements and so analyze digital experiences and interactions. Screencast videography could allow to better understand interactions between consumers (human) and technology or A.I. (nonhuman) observing how consumers move in the digital space. Knowing by moving is the ontology of videography in the Hietanen and Rokka’s sense (2018), in which this method is anchored (Kawaf, 2019). Analysis of data from screencast videography should not only consider information from images and audios separately, but also from sounds and temporal relationships of speech to visually depicted events and actions. It means that all the elements of the ecosystem in the digital space could potentially enact, having agency. So, during analysis attention could be also drawn to dynamic actions such as movements of the mouse pointer. For example, Kawaf (2019) used this method to explore the online fashion shop experience. She observed the dynamic overview of the experience and detailed mapping of the interaction involved. Participants could choice a familiar web browser and they were instructed to visit their preferred fashion shopping websites. A software was used to record and edit the screencast videos without time restrictions for participants. She highlighted the process of the shopping experience and the main incidents that influence decisions (Kawaf, 2014). This third example of qualitative methods adapted under the perspective on relational ontologies show an initial effort by researchers to open up to understanding the complexity of phenomena, considering that it arises from a multitude of elements that are not just human.

USING RELATIONAL ONTOLOGIES PERSPECTIVE TO ADAPT OTHER QUALITATIVE METHODS The adaptation of some existing qualitative methods to relational ontologies can expand to other methods. For the future, we offer three possible adaptations for qualitative methods: object observation, object-centered auto-videography and AI interview. Object observation means to give attention to the objects, how they enter in relation with humans and what are the outcomes. If observation is traditionally conducted in contexts that are studied by researchers, what is observed are often people’s actions and the objects are the passive part of human actions. Recently, another perspective linked to relational ontologies has emerged. It considers the digital ecosystem as composed of technology (e.g. platform, devices) and non-technology actors (brands, brand communities and consumers) where technology is not a mediator but an active generator of new kinds of practices (Morgan-Thomas, Dessart and Veloutsou,

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2020). These socio-technical perspectives that give attention to the enactment of technology for human actions need a new way of observing the object (such as devices or platforms) shifting from a human centric to a technological centric one, emphasizing the role of technology in practice. The method of observation (participant or non-participant) should be theoretically oriented to this capacity that objects have to generate actions, seeing objects as actors. A question that could lead the observation is: “What does the presence of the object produce around it?”. For example, the presence of a camera with the facial recognition in some shops has created new practices and communities of consumers that use hairstyles and make-up to bypass systems. Instead of avoiding entering the store these customers use new practices and have a different shopping experience. The second possible application of relational ontologies perspective to qualitative methods is in autovideography, when a participant manages the camera to record a video about his world of consumption (Belk & Kozinets, 2005). If the video diary has been largely used in qualitative research to record consumer experiences and feelings, it was normally focused on the participant and often in the same place (e.g., filming the preparation of a family meal in the kitchen). An object-centered perspective should be adopted to focus on the objects to record them and their circulation in private and professional settings, where the researcher observation is difficult to do. Object circulation is an interesting approach to understand value creation (Figueiredo & Scaraboto, 2016; Scaraboto & Figueiredo, 2017) or the modification and structuring of a consumer network (Scarano, 2020). Any qualitative method that explores how objects circulate, engage people in practices, create informal roles of consumers and change meanings traversing different cultural frontiers, needs more attention and analysis centered on the object and his story. For example, following by autovideography the itinerary of a videogame device from a country to another (ex. offered as a gift-giving or used in the second home) could help to explain the different cultural perceptions about it and how the same object could take on more or less value due to the geographical passage. The third suggestion about qualitative methods is linked to a theoretical post-humanist perspective. In the future, researchers could be confronted to a self-learning Artificial Intelligence that is able not only to have relationships with humans but also to have a programmed self-learning capacity to enact intentionally. The perspective of the object could become central and interviewing AI could be possible in the future. For example, we could have robot sellers with self-learning AI that interacts with customers in a physical shop, but also virtual sellers in the shops online. If a seller could be interviewed as an expert (Bogner & Menz, 2009), there will be the possibility to integrate AI interviews. Researchers should be able to explore human and nonhuman relationship from the perspective of a narrative human and a narrative nonhuman. Conducting an AI interview will not be without some operational and ethical problems (e.g. informed consent of AI) that will need legal solutions. Anyway, it is a frontier towards which the digital and virtual society will be confronted. These three possible applications should consider data analysis (table 1) linked to their theoretical support. For example, consumer culture theory researchers might use assemblage analysis, according to this theoretical approach, to trace the lines of stratification by which power relations emerge (Arnould & Thompson, 2015).

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Table 1. Observable information under relational ontologies           Example of some analytical elements to be observed • The consumer and technological object relation that can be create, maintained, finish • The social places and spaces (geographical and virtual) in which interactions take place; information and images pass and are transferred • The words and gestures that accompany relation for humans and for AI robots • Support elements that allow or block relation (e.g., material, technical, physical, personal, legal) • Possible constraints and diversions • The use of other resources to overcome blocking elements • The different activities that component of a human/technological network engage in (e.g. human and AI) • The effects the object can have • The emergence of these effects from connection between people and objects • The active role played in the world from the objects • The way the object evokes ambiguity or ambivalence during time or space passages • Object circulation physically and virtually between people geographically dispersed (the types that circulate alone or with a constellation of other objects; the stages of material circulation)

CONCLUSION The aim of our conceptual contribution is to offer a discussion on the ontology of methods. The author argues that shifting from a humanist to a relation ontology can overcome some problems of existing methods. However, even if relational object-human ontologies have been increasingly drawn upon by consumer researchers, and become a source of inspiration for the methodology (Lucarelli and Giovanardi, 2019), little attention has been given to the adaptation of the ethnographic methods under this approach except for some studies (Lugosi & Quinton, 2018; Woodward, 2020) that incorporate additional elements such as technology and objects to explore more directly their relationship with humans without using only a person-centered approach (Levy & Hollan, 2014). To remedy this, the author suggests to increase adaptation of existing methods to relational ontologies with some examples: object observation, object-centered auto-videography and AI interviews. These proposals show how the object becomes explicitly relevant and allows researchers to focus also on objects, leaving behind a merely anthropological view of the object as a passive role. The environment of consumers offers a large presence of new or vintage technological objects in their personal and professional life: mobile phones, computers, automatic cash machines and virtual mirrors in stores, connected home appliances, chatbots in e-shops, tourist app, videogames and so on. More and more, the new technological objects are equipped of AI which enriches the interaction and impacts on consumer empowerment and engagement. Moreover, semi-autonomous and autonomous machines (e.g. cars, drones, retail clerks) will be increasingly relied on in our social life. This horizon requires considering the object according to an appropriate ontology from which coherent methods are derived. The central point for all the future adaptations of qualitative methods to relational ontologies is that the technological object has to be considered as an active actor in the relationship and should have a specific place in data analysis. However, the presence of the technological object in the analysis, as well as its use as a tool in qualitative research, reveals several problems. If it allows to know some forms of knowledge of the social world, following the ‘digital traces’ (Marres, 2017) and spreading from a personal to multiple socio-cultural domains (Bucher, 2017), it is also accompanied by some critics such as the perception of technological surveillance (Zuboff, 2019), infringement of privacy (Sholtz, 2001) or

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the power of algorithmic technologies to discriminate users (O’Neil, 2016). All these aspects should be considered in the ethical research protocol, for example by using an informed consent. This research received no specific grant from any funding agency in the public, commercial, or notfor-profit sectors.

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KEY TERMS AND DEFINITIONS Digital and Virtual Society: Contemporary society caracterised by the presence and use of technologies in physical spaces and via the internet. Human-Centered Ontology: Vision of reality where the human entity is predominant. Material Methods: Qualitative techniques of research that focus on things, objects and material properties. More-Than-Human Nethnography: A specific approach to netnography, linked to ANT theory, that consider nonhuman agency and materiality as dimensions to examine. Netnography: Qualitative research technique to study people interactions online (e.g., blogs, brand communities). Object-Centered Interviews: Qualitative research interviews to people using object elicitation. Person-Centered Interviews: Qualitative research interviews to people based on people narrative about a topic. Qualitative Methods: Several research techniques to study in depth an object of study producing thick data, deliberately leaving aside the quantitative aspect. Relational Ontologies: Vision of reality as a relation between human and nonhuman, where any entity is predominant. Videography: Qualitative research technique using a camera to film. It can have different purpose, such as theorization (e.g., presenting a research at a scientific congress) or data collection.

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Using Video Diaries in Social Science Research: Reflections on Past, Current, and Future Ethical Trends Amanda Vettini University of Edinburgh, UK Ruth Bartlett University of Southampton, UK

ABSTRACT The focus of this chapter is the use of video-diaries in social research. The aim is to examine and reflect upon the particular ethical terrain and situated ethics of using visual diary method in social science research with different participant groups who arguably present specific ethical concerns, including children and older people, people with disabilities (either physical, cognitive, or psychiatric), and older people. The authors present a discussion of the specific ethical considerations arising from the use of this method due to the particular type of data it generates, namely audio and moving visual data. As such, the process of creating a video diary and the procedures involved in collecting and analysing video diary data are fundamentally different from a paper-based (non-digital) diary. For these reasons, it is important to step back and reflect on the situated ethics, including the digital ethics encountered when using this method.

INTRODUCTION This chapter will discuss how visual research, and in particular, video diary method has been, and is used in social science research, and to endeavour to forecast future trends for this method. Part of this will involve examining and reflecting upon the situated ethics of using digitalised visual diary methods in social science research in varying contexts and with different participant groups, including, for example, in remote environments and with children and older people. Video diaries generate a huge range DOI: 10.4018/978-1-7998-8473-6.ch027

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of ethical and practical considerations due to the digitised nature of the audio and visual data they generate. For example, how to engage with these data effectively, analyse and communicate findings from them whilst mindfully adhering to appropriate ethical procedures of informed consent and anonymity. As such, the process of creating a video diary and the procedures involved in collecting and analysing video diary data are fundamentally different from a paper-based (non-digital) diary. For these reasons, it is important to step back and reflect on the situated ethics, including the digital ethics encountered when using this method.

Evolution of Diary Method Asking participants to keep a record of their thoughts, actions, behaviours, or feelings about a topic (i.e. diary method) is a method of data collection that has been used by researchers for more than a century. One of the earliest diary studies was conducted in London by the Fabian Society. In this research, 43 women living on a low-income were asked to keep a record of their expenditure for one week (Reeves, 1913: 11). Although the term ‘diary method’ was not used by researchers, record/diary keeping was the basic design. Since then, the term ‘diary method’ has evolved and thousands of diary studies have been conducted in a wide range of disciplines, on various topics and in various ways with different research diary types; for example, structured diaries collecting numerical information (such as expenditure) as well as unstructured or semi-structured diaries more typically situated in qualitative research. It is beyond the scope of this chapter to discuss in any detail how diary method is used in social science research; besides, strong accounts are already provided in the literature (see, for example, Alaszewski, 2006; Bartlett & Milligan, 2015). The focus of this chapter is on the evolution and use of video diary methods in social science research.

Video Diary Method Video diary method, asking participants to maintain a record of their thoughts, feelings and actions using an electronic filming device (such as a smartphone or flip cam) over a certain period of time, has been growing in popularity among social scientists in the last twenty years. As a research instrument, video diary method is an integral part of the qualitative researcher’s toolkit. The self-disclosing element and storified content means it has close connections to narrative methods. Video diary can be characterised in terms of a ‘participant-generated video account’ of a given topic or experience (Gibson, 2005: 35) – that is, participants are in control of data collection, rather than the researcher. For example, in one video diary study of open water swimming, participants (who were sea swimmers) were asked to wear a GoPro camera and make a video diary entry before they swam, during the swim, and after they got out (Bates and Moles, 2021). One participant made much longer diary entries than the others. Whatever the topic or experience, video diaries are defined by the following methodological characteristics: regularity – participants are asked to organise their entries over a certain period (e.g. once a day for a month, or twice a day for a week); private: a video diary is typically generated by an individual on their own; contemporaneous - the recording is made at the same time or very close to the time that events occurred; in situ – the recording is made in the same place that the experience occurred (Bates and Moles, 2021); multisensorial – video diaries provide data that are visible and moving in both a changing and arousing sense. Integrated: video diaries are often combined with other methods, such as semi-structured interviews or focus groups, to ensure the fullest picture as possible is gained of the topic 443

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or experience. Video diaries are a valuable research technique, then, but also an increasingly technical one given the digital age in which we live. Several video diary studies were published in the early 2000s (e.g. Noyes 2004; Gibson 2005), and of course, visual methods generally have been around much longer than that. (For a good introduction into visual methodologies, see Rose, 2016). In recent years, the use of video diary method and associated publications have been growing in number, progressively gathering pace over the last five years, revealing the increasing digitisation, flexibility, and ethicality of this method. For example, in a study by Nash & Moore (2019) participants were asked to use their digital camera/smartphone to keep a video diary of their experiences of working on a science ship in the Antarctica; the researchers acknowledged that they were unprepared for the level of self-disclosure in the video diaries and underestimated the burden of research participation in such remote and extreme circumstances. Studies like this fuel this chapter’s interest in and exploration of the ethical terrain and situated ethics of using digitalised visual diary method in social science research.

Burgeoning Technologies in Video Diaries Video diary method is changing fast. In the past, participants were typically given a hand-held camera to make their video diary (e.g. Buchwald, Schantz-Laursen and Delmar, 2009), nowadays, and highly relevant to our digital age, participants are more likely to be asked to use their own smartphone or utilise one of the many video diary recording or journaling tools or online apps such as FlexMR, MindLogr, LiveJournal or 1-second every day. Existing commonplace social media and chat apps such as SnapChat and TikTok may also be used by researchers and participants to record their diaries. In the future, it is likely that a plethora of new apps and tools will be added to the existing smorgasbord of possible software for this method. Depending on the precise functionality of new apps and tools, and the types of video data they offer the potential to capture, new ethical challenges may spring forth. For example, access to, and familiarity with, digital technologies can raise issues of access for those who are less affluent and those whose physical, sensory, or cognitive limitations may require specially adapted equipment (Bartlett and Milligan, 2015). At the same time, a perennial ethical challenge associated with visual and diary methods – that is, privacy, is likely to continue and change in nature. Finally, the ways in which video data may be linked with other data types, either within the same research study or across different studies, presents another trend, which the authors will comment upon. All of which adds to the layers of ethical complexity to this research method.

Caution on ‘False’ Innovation and Interdisciplinarity Video diary method clearly offers great potential in the hyper-digital age. The relative newness of video diary methodology arguably places it within the camp of innovative or creative research methods. The authors offer a word of caution regarding the unfettered move or claim to innovation in research during the digital age, without appropriate methodological underpinning or consideration of ethics. As scholars have noted, with increasing calls for inter-disciplinarity, innovation and creativity in research and the associated increased likelihood of attracting research grant funding and / or becoming published, some academics may feel a pull to allege truly innovative and ground-breaking methods within their research (Wiles, Crow and Pain, 2011). Yet the reality may be more a case of adapting what existed rather than true novel creation. Wiles, Crow and Pain (2011) maintain that among the nearly 60 published studies 444

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they reviewed claiming methodological innovation, relatively few were completely unique and instead modified existing methods. The authors will not be arguing that there is anything wrong in principle with a range of levels of innovation in research methods, merely that statements regarding ‘innovation’ should only be used when relevant.

Situated or ‘Lived’ Ethics A key focus of this chapter is situated or ‘lived ethics’ - these are the dilemmas that researchers face in the field after they have gained ethical approvals, whilst recruiting participants and collecting data. Typically, situated ethics are relational and not foreseeable. For example, a young female researcher being asked out on a date by an older male participant; or a junior researcher having to deal with repeated requests from a senior colleague to share diary data. As Carter (2017) points out: ‘the core precepts of consent, choice, and rights can be made more relevant and humane when they are not just external edicts but are cultivated as a living ethic within the relational system they operate in’. Situated ethics are discussed in this chapter in relation to using video diary method, particularly with participants commonly considered ‘vulnerable’ by ethics committees, notably children. Research with children generates epistemological and ethical challenges due to the contrasting power dynamics, between them and the researcher (Punch 2002). Drawing on the authors’ experiences of using diary method, as well as published work of other diary researchers, they plan to discuss the situated ethics of using video diaries to collect research data. A situated ethics perspective argues that ethical principles’ meanings vary according to the research circumstances and should be moulded within differing research perspectives (Simmons and Usher 2012). Claims have also been made within academic literature on video diaries as to the empowering and participatory nature of this method (Cashmore, Green and Scott 2010; Muir 2008). Challenges, however, have been levied against the perspective of participant empowerment by scholars such as Jones et al (2014), revealing the hotly contested nature of video diaries. This chapter will contain a discussion and reflection based on the existing evidence base on the question of empowerment in this method and the ethical implications of this. Following this introduction, the chapter will be structured around the following questions and topics: 1. Why use video diaries? The methodological advantages and disadvantages of using video diaries in social science research will be explained in this section, using examples from published video diary studies, and partially drawing on the authors expertise of diary method in their own empirical research. 2. Preparing and supporting participants for video diary research. The process of preparing participants for video diary research will then be discussed, including the ethics of training people to use video capture technologies. Further ethical issues such as securing informed consent, providing support during the diary keeping phase, and considering the implications of anonymity with this method will also be reflected upon. 3. Analysing video diary data. The essential steps in data analysis are outlined in this section, as are various frameworks researchers have used to analyse video diary data. The analytical challenges that video diaries present, as one form of visual and audio data, will be identified and discussed. 4. Communicating findings from video diary data. Key implications of communicating findings from video diary data will be presented here. Such rich data offers enhanced audience understanding and engagement by illustrating a particular research finding with an audio or video clip. However, 445

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being visual and audio data, video diaries pose ethical challenges regarding securing informed consent at different stages of the research, as well as in varying communication formats (written publications as well as spoken conference presentations / events). 5. Conclusion. In this final section, the authors will summarise the main points made in the chapter and look to the future for using video diary method in a digital age, providing an overall assessment of the application and use of this method as well as potential notes of caution surrounding its use without due ethical consideration.

Why Use Video Diaries? As with any research method, video diaries offer a range of advantages and disadvantages associated with their use. Decisions around whether they are an appropriate methodology for a researcher’s study are dependent upon a range of factors such as: study aims, participant groups, technological access, and skills.

Methodological Advantages and Disadvantages One key advantage of the diary and video diary method is that it typically (but now always) removes the researcher from the point of data capture, which affords participants private space to record their views. This can be especially useful when researching sensitive, emotive, or taboo topics, such as sexual health, condom use, marital problems and living with the chronic illness of another. The fact that the researcher is absent at the point of data collection can thus enable participants to feel freer to truly express what they are feeling at any given moment. Buchwald et al (2009) found that this method worked particularly well for children to produce narrative accounts of their experiences of living with their parents’ cancer.

Accuracy and Mental Processing Additionally, diaries can provide a more accurate record of an event than solely conducting a qualitative interview, as diary reflections are usually contemporaneous – that is, recorded at the time of, or as soon after the event as possible, thus reducing recall bias, which often happens during a qualitative interview. The fact that digital devices automatically record the date and time of an entry can make for an even more reliable method. Video diaries can help participants to think through their views. Buchwald et al (2009) commented that one of their participants said ‘talking to the camera could make him see things more clearly and prepare him to talk to his friends about his thoughts and feelings’ (p. 16). This resonates with findings from one of the authors’ research whereby a student research participant discovered that recording video diary entries about their experiences of studying research methods crystallised their understanding of the methods and how these related to their own doctoral studies. This ultimately proved so beneficial to the research participant that it became a tool they adopted to think through a problem or challenge, by vocalising it in a short video.

Location and Access Another important advantage of video diary method is its reach and flexibility: it can transport researchers to places and situations that they would otherwise be unable to venture or experience, including for example a research voyage in the Antarctic, a child having an asthma attack, and a person’s commute 446

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or wild swimming experience. All of which have been investigated using video diary methods. Video diaries can establish a connection between place and data that can operate as a catalyst to express views which may not have been stimulated in an interview room. Buchwald et al (2009) describe a child in their study outlining how he had experienced a relatively good day, despite living with parental cancer, due to having a newly decorated bedroom. He was able to demonstrate this by panning round to show his room in the diary recording. This feature of video diaries giving rise to richer and deeper data will also be revisited later in this chapter, especially in relation to Noyes’ (2004) work also with children. As researchers have pointed out: ‘the sheer variety of topics that video diaries have been used to investigate illustrates the possibilities that they have opened up’ (Bates and Moles, 2021: 3). Furthermore, it suggests that the video diary method is one the most useful and advanced research methods for the digital society in which we live.

Evolving Experiences Diary method is beneficial for providing a longitudinal account directly cast from participants’ own viewpoints. Contrasting with snapshot and cross-sectional methods, video diaries can capture evolving and shifting participant perspectives, as events and experiences unfold over time. Video diaries also offer a very different, and as argued by many richer forms of data than some other qualitative methods, such as interviews (Buchwald et al 2009; Noyes 2004). The visual component of this data affords the potential to analyse unspoken as well as spoken communication and revisit recordings multiple times. This advantage of videos will be further discussed in the section on analysing diary data.

Participant Empowerment Moreover, empowerment of participants via this method has been argued by some e.g. Buchwald et al (2009) and Noyes (2004) especially due to increased control in various ways although as earlier indicated, this is a debated point which will be discussed in more detail towards the end of this chapter. The contrast between etic (outsider looking in) and emic (insider) perspectives is important here. Video diary data has been argued to provide both forms of insight becoming emic in the rich, involving nature of the participant’s inner and daily life as well as etic providing observation data (Taylor et al 2019). One form of empowerment, increased participant control over their data, is pertinent for video diaries as people can regulate data distribution with more ease than many other research methods. Once a video entry has been recorded the participant has complete autonomy to determine whether they wish to share it with the researcher. This level of complete control for participants is not as easily achieved for example during an interview once a view has already been expressed. It can, of course, be removed from the research, however, being in the position to not share it at all even after vocalising it strengthens participant autonomy. Further advantages of video recording being at the participant’s behest are demonstrated in the staccato opportunities afforded by this technology to pause, stop, and restart the diary entry. As such the video diary fits more easily within the participant’s life; should an interruption occur or something urgent arise the recording can be temporarily ceased and later resumed. This makes video diaries especially suited to particular types of participants. For example, those who are exceptionally busy with conflicting commitments, such as new breastfeeding mothers (Taylor et al 2019) as well as those with reduced cognitive abilities due to a disability or to not being yet fully developed, as with children, and thus having limited capacity to focus. Buchwald et al (2009) averred that the children in their study found video 447

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recording especially beneficial due to their relatively short attention spans and the ability to pause the video when distracted again offers both autonomy and suitability to this participant group. Taylor et al (2019) also successfully used video diaries via camcorders with breastfeeding mothers documenting their experiences. Participant control over initiating and ceasing diary recording was highly beneficial and appropriate for the sometimes-frantic lives of new mothers.

Disadvantages Attrition Every research method has disadvantages as well as advantages, and video diaries are no exception. First and foremost is the issue of respondent attrition and completion problems. One of the benefits of diary method, its longitudinal nature, also poses on the flipside a drawback in that participants’ engagement needs to be maintained over the lifetime of the diary study. Thus, video diaries, even if each recording is relatively brief, can arguably be considered commitment-intensive due to their overall longevity. Researcher Absence One of the earlier mentioned advantages, that the researcher being removed from the moments of data collection facilitating more open participant vocalisation, by contrast gives rise to a disadvantage that the researcher is not present to probe, prompt or clarify what is being said. This can be mitigated by both careful pre-diary phase preparation and regular communication flow between the researcher(s) and participant(s), as well as post-diary phase interviews to review data and check interpretations (Buchwarld et al 2009). That said, it is argued that researchers are inevitably part of the world being studied, whether they are physically there or not, as participants talk about their lives with the researcher in mind (Gibson, 2005). Lack of Privacy Recording one’s thoughts and feelings is a private process, and space may be lacking for participants to keep a video diary. A lack of privacy is often reported as a drawback in video diary studies. For example, Feminist researchers Nash and Moore (2019) used video diaries to study women’s experiences of a scientific leadership programme; they used video diaries due to the remoteness of the study setting - a ship in Antarctica. The 25 female participants used their own digital camera to record one daily diary entry over a 21-day period. A total of 220 videos were collected and consent was gained to publish these on the internet. Although it was not a requirement for the research, participants wanted privacy to record their diaries. However, the researchers underestimated the burden of participation for these women – many of whom found it challenging to find a private space (on board a ship) to record their diary entries. Technical Capabilities Technical aspects may also pose a potential issue as not all types of people will have the required skills to operate video equipment, if used, and / or video journaling software. Technological comfort and savviness within our digital age is differentially present across potential participant groups. A reluctance or initial discomfort about appearing and speaking on camera can also be a factor for some people, ‘camera shyness’, and this may modify or compromise what some participants may be willing to say

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being initially acutely aware of the video camera. Some might assume that younger people are less likely to have problems using a video recording device; however, the issue is often familiarity or willingness rather than age. For example, one video diary study involving chronically-ill children in hospital found that some children had difficulties using an analogue device and concluded that recording video diaries can be ‘terrifying or strange’ for children (Karisalmi and Nieminen, 2017: 175). Other researchers in childhood studies have found significant variations in data produced by boys and girls; for example, in one study involving 12 boys and 11 girls - all of whom were asked to produce a video diary once a day for four days - only one boy but six girls completed the task, and in total, the girls produced 52 files while the boys created 40 files (Iivariet et al, 2014: 512). Clearly, not everyone is skilled or inclined to keep a video diary, which is why researchers often produce guidance for participants and often spend a lot of time with participants to help them prepare for the diary-keeping phase. Keeping a video diary is not for everyone, and/or for some people, filming oneself can feel like an alien process, especially if they are using a device, they are not familiar with. It is certainly a less straightforward way of keeping a diary than writing. For this reason, researchers might pilot the method with their target population first. For example, gerontologists in the USA set up a pilot study to ‘determine whether video diary is a feasible methodology for eliciting perceived barriers, facilitators, and potential solutions for aging in place among older African Americans living alone’ (Owens et al, 2019: 397). They involved 12 people in the pilot - five men, seven women, aged between 66-80 - each of whom was provided with a 10-inch android tablet to record their videos. Ninety-two videos were submitted; however, the quality of some data was poor due to camera framing and audio problems (Owens et al, 2019). Technical difficulties are a major disadvantage of the visual diary method.

Preparing and Supporting Participants for Video Diary Research All participants regardless of age, gender, disability, willingness, or level of technical competency will require some degree of preparation before they start producing their videos. The extent and nature of guidance and training will of course vary with different population groups, and it is not always obvious to a researcher what that will be. For example, in the study involving older African Americans living alone, researchers discovered through the pilot work that this population group needed additional camera training. Other researchers have discovered that regardless of the amount or type of guidance provided, participants will often choose to do their own thing when it comes to keeping a diary. For example, one young participant in a video diary study of asthma, decided to record herself having an asthma attack (Rich et al. 2000).

Participant Briefing It is crucial to consider whether video diaries will be used as a stand-alone method in the research study or combined with other research methods. The bulk of evidence points to an integrated methodological approach as strongest - that is, using video diary methods as part of a suite of ethnographic research. If video diaries are used with other methods, a pre-diary phase interview either with each participant individually and / or with groups of participants provides opportunities not only to brief participants on diary recording processes but also invite their understandings of what a video diary is, how diary data should be handled and their preferences and agreements on whether it produces public or private data and how this may vary within the data itself. 449

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Noyes (2004) carried out video diary research with six 11-year-old children to investigate their experiences (specifically ‘learning dispositions’ i.e. attitudes to school and learning of transitioning from primary to secondary school in England, especially relating to learning maths. Noyes (2004) hosted group discussions with the children as well as solo pre-diary interviews. A specific video diary room intentionally evocative of the TV programme Big Brother was set up with a video camera on a tripod. The group sessions were especially useful in clarifying the importance of ‘privacy’ in terms of not being overheard, as the video diary recording took place at the children’s school. The children also agreed that the video diary data was both public and private in that they viewed it as an opportunity for them to provide an account of their weekly experiences for themselves personally yet it was also to enable others to understand their circumstances and views on learning maths. A specific element to note is that if conducting video diary entries during the school day, these can be viewed by children as a welcome escape from lessons, thus setting a maximum time limit for diary entries is important (Noyes 2004). Some studies permit a more free-flowing approach to participant’s expression of views providing relatively loose guidance on what diary entries should include to act as ‘triggers’ rather than being directive (Taylor et al 2019, p. 17).

Misconceptions and Power Dynamics Despite detailed preparation, misconceptions of the researcher’s role may yet occur in video diary research whereby researchers can be viewed as authority figures, connected with the institution that participants attend, for example a school or service. This is particularly salient in video diary research with children, whereby some children perceived the researcher as a teacher at their school despite careful information provision from the outset (Noyes 2004). Part of ethically appropriate and situated preparation is a detailed consideration of power dynamics, which are present with any research method. However, in research with children, including video diary work, it is especially critical to break down power barriers. Children are used to adults being authority figures in their lives, instructing them on what to do and when to do it. Researchers providing instructions for diary keeping as part of the research process could be perceived in this way, and this could not facilitate the most open and optimum expression of views, if not handled with ethical and positionality sensitivity (Buchwald et al 2009).

Consent and Anonymity Consent and anonymity are more complex in video diary research than other methods. Even though consent may have been sought faithfully in accordance with standard guidelines for example the ‘British Education Research Association, Ethical Guidelines’ for education studies, has informed consent truly been given? When researching populations with lower cognitive abilities than a standard adult, such as children, this becomes an especially crucial element. For example, in Noyes’ (2004) study, parental consent and permission from the school had both been given as well as the children being engaged and informed throughout the study. Yet Noyes (2004) comments on the challenges of really establishing how far consent has been fully understood and therefore truly given, a point which is echoed in Lindsay (2000). Questions of anonymity are also far deeper than merely using pseudonyms when there is visual facial data thus researchers (and even participants) must fully consider securing anonymity if sharing visual data. 450

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In one study participants were given a hand-held video camera and instructions on the safe and ethical use of video recording as advised by the school (i.e. not posting recordings on YouTube or recording people in public places). The study was tailored to the needs of the participants by agreeing that the participants could film their use of the internet sporadically over a one-year period. One participant (Noah) could not physically record the diaries (after several attempts) and asked the researcher to assist him in recording his video diaries at home. Another participant (Mick), who could not physically do the recording himself asked his parents to assist him with recording the diaries. Another Edinburgh-based study secured consent for various stages of using video diary data and even dispensing with anonymity via a screening page on their Qualtrics survey as part of the study’s mixed methods approach (Hall et al 2021). The differing levels of data usage permission were threefold: by other researchers, in oral history archives and the most extensive being on various forms of media including online and television. Due to wide dissemination intentions some studies such as Taylor et al (2019) have elected to explicitly not promise anonymity and secure consent for visual data distribution at the outset. Participant control and empowerment in this case was attempted by allowing participants to edit their video entries before submitting to researchers. Other researchers have found that there can be significant variation in children’s video diaries; in a sample of eleven Danish children aged between 8 and 15 living in foster care, younger children (aged 8–11) more likely to make short videos of themselves playing in their room whereas older children, aged 12 to 15, made lengthier videos of themselves talking directly to the camera (Bengtessen and Lucknow, 2020: 109).

Minimising Attrition Expanding upon what is already noted above in the previous section on varying willingness and commitment to keeping a video diary (Livariet et al 2014), some academics propose agreeing a particular time of day with participants to increase regular diary entry likelihood. In research with children this type of advance spoken agreement regarding timing, has been successful in securing frequent participation (Buchwald et al, 2009). Similarly, Noyes (2004) in their education research study advocated one day during the school week when the children could record their video diary entries. Creating structure and limiting participants in some ways can boost participation whereas a more free-flowing approach of complete spontaneity, potentially leading to procrastination, may not.

Location Location of diary recording is another matter for deliberation at the outset of the study and, as with any research study, must be guided by pragmatism, the aims and nature of the topic of study as well as ethical concerns regarding privacy and anonymity. For many participants recording a video diary in their own homes is going to be most suitable, for others a specific public location with a private facility. Matters of the type of technology used for video recording are of importance here; is this via participants’ own mobile phones, an online journaling device or a handheld video camera or a fixed camera in a particular location? Due to the research taking place with a group of school pupils Noyes (2004) opted for a specific room in the school and fixed camera for their education research study whereas Buchwald et al (2009) invited children to address the video camera in their own room at home, this personal space being far more suitable for a study about children’s experiences of parental cancer. 451

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Participant Performativity and Perceptions of ‘Camera’ Identity Who or what do participants perceive the video camera as, is a pertinent question for video diary research. Child participants have been noted to say ‘You’ to the camera frequently (Noyes 2004). However, to whom are they referring when they say ‘you’, is it the researcher, a wider audience or are they anthropomorphising the camera and viewing it as akin to a person with an identity? Some participants, especially children and young people, express concern about ‘boring’ the audience with their views: ‘Have I bored you yet?’ (p. 122) was enquired by Diane a participant in Bloustein’s (1998) study and similarly Stacey in Noyes’ research frequently made statements such as ‘I’m boring you, I bet’ (2004, p. 201) As noted earlier that video diary entries can help people formulate their thinking, the video camera can to some degree even become a confidante and friend in the eyes of some participants. Stacey in Noyes’ (2004) study describes their acute embarrassment during their first diary. Although this subsided, they always felt somewhat anxious during diary recordings: ‘...every time I do this I need the toilet, well not desperate, desperate….’ (p. 201), however, the diary also became a friend ‘someone that I can talk to…. well something that I can talk to’ (p. 201). Echoes of this personal relationship with the camera was noted by Bloustein regarding Diane ‘Towards the end of the fieldwork, she was to describe the pleasure of using the camera as a diary because “It became like my best friend”’ (Bloustein, 1998, p. 122). Video diary research, especially if an actual video camera is used as opposed to a participant’s personal mobile telephone, can have an impact upon those researched at least initially. This can range from camera shyness and an unwillingness to relax and freely express in front of the camera to engaging in high levels of performativity and becoming an uber version of oneself. Noyes (2004) commented that an especially confident participant with a strong ability in maths (the topic of the research) behaved in a performative way, and said they felt famous: ‘As it is my first day of the diary I find it really good today… [pause for effect]...to get to know….YOU [points, stares and grins proudly at the camera]...because…. first time I’ve been in front of a camera....don’t feel bad...([grins]....feel good….feel famous [satisfied giggle]. (p. 199). Studies have noted that camera-affected participant behaviour, whether shyness or reinforced confidence, typically subsides as the camera and video diary process become more familiar and more naturalistic behaviour ensues (Noyes 2004). Noyes was in a strong position to verify this due to collecting different data types and noting variation; they understood participants’ daily behaviour in classrooms via observation data and thus their typical off-camera conduct. Potential ethical issues of increased embarrassment or confidence these examples illustrate should be considered by researchers, however, if sensitively handled by maintaining frequent contact with participants and having pre-and post-diary interviews or sessions are not overly problematic.

Participant Care When using video diaries for research on sensitive / emotive topics, participants may become upset yet an advantage of video diaries (the researcher being removed from the point of data collection) also represents an ethical challenge as they unable to soothe participants’ regarding any negative effects that vocalising their thoughts and circumstances may evoke. Buchwald et al (2009) offer a potential solution. They overcame this issue by offering support if participants needed this and additionally by interviewing participants a fortnight after diary submission, to discuss any emerging issues. Families were also offered follow-up counselling and if support was required a psychologist was at their ‘disposal’ (Buchwarld et al 2009 p. 18). Researchers can also be alert to the potentially burdensome nature of video 452

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diary recording, reviewing entries for signs of respondent fatigue and advising participation cessation if indicated (Taylor et al 2019).

Analysing Video Diary Data A common question for diary researchers is about the analysis of data - that is, ‘the process of bringing together a series of things in ways that make them mutually meaningful’ (Pink, 2009: 120). Visual diaries often produce a large amount of complex and captivating moving images and audio that can be challenging to amalgamate and interpret. Some of these challenges are often practical in nature, for example, the time it takes to deal with the sheer volume of data, others are more methodological and might concern the validity or trustworthiness of data that participants have self-produced for a researcher. Either way, as with all research methods, the analysis of video diary data is a time-consuming and technical process which requires skill, knowledge, and resources. What is more, there is no standard method or ‘proper way’ of analysing video diary data; the approach will depend on your academic discipline, research question/aims, study setting, participants, methodology and resources available. Therefore, it is important to have a plan, and in doing so aim for ‘analytical explicitness’ (Pink, 2009: 120) - that is, being clear about what you are doing, when, and why. Here are six basic steps, which can assist with the planning of data analysis in a video diary study: Step #1 Decide on the most appropriate systematic coding technique (or combination of techniques) to use in your video diary study; for example, thematic analysis, narrative analysis, content analysis, discourse, or nexus analysis (see Schollon and Schollon, 2004). All these trusted methods of data analysis have been used by diary researchers and are appropriate to use in a video diary study. Which one you choose will again, depend on your academic discipline, research question/aims, study setting, participants, methodology and resources available. The important point here is that you need to choose and stick to a technique through the data analysis process to ensure rigour and focus. Step #2 Consider using a specialist technique or software. Specialist techniques have been developed for the analysis of video diary data, which may be appropriate for your study. For example, if you are investigating a health condition, you could use a specific method like Video Analysis, Intervention/ Prevention Assessment (see box 1 below). Step #3 Select data to analyse. Diaries, and in particular, video diaries often generate a huge amount of data. Even the relatively small-scale pilot study involving 11 older African Americans created over 90 video clips. Not all of the data collected will necessarily be usable or important for your study, so one of the first steps in the analytical process is to select the data to analyse and delete/remove what is not required from the dataset. For example, in one video diary study of wild swimming, which involved swimmers wearing a GoPro webcam on their head during a swim, data was captured of nudity, when the swimmers were getting dressed; the researchers deleted this footage (Bates and Moles, 2021). Alternatively, some data may be of such poor quality that it is not possible to hear or see people properly; in which case, you might decide the data is unanalysable. In effect, then, this step is a quality check; it involves cleaning the dataset and ensuring that all the video diaries files you plan to analyse are of sufficient importance and quality. Step #4 Note your reactions to data – positive and negative. Reflexivity is an important aspect of video diary research. Therefore, noting down how you respond or feel and think about the data in front of you is a necessary step in the analytical process, as it can help to highlight and work through any taken for granted assumptions (either about the topic or participants involved, or both). 453

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Figure 1.

primary audiovisual data’ (Rich & Patashnick, 2002: 245).

Step #5 Scrutinise what is visible and audible. Videos as a form of rich visual data offer increased opportunities for deeper analysis by researchers. A critical benefit is they facilitate noting unspoken as well as spoken information. Moments with no words are not empty – instead they are filled with the significance of water (Bates and Moles, 2021: 9) with the deeper layers and insights into participants’ world view this provides. During the analysis process, researchers must, however, remain especially reflexive and open to the data itself with the increased potential offered by such enhanced data. Step# 6 Consider what was required of the participant to produce materials. This is an important step in the analysis of video diary data, as the effort required by a participant to make their video diary, and the style in which they choose to make it, can reveal something about the phenomenon of interest. For example, in a video diary study of basketball players, the researchers got a glimpse of participants’ identities through their ‘on-camera performances’ (Cherrington and Warren, 2010: 273). Other research-

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ers have noted that the parts of the body, which are shown and not shown in a video diary, can help to elucidate participant’s priorities (e.g. Bates, 2012) While working through these steps, it is essential to consider and be sure of one’s general approach to data analysis. A grounded theory approach can be taken to video data analysis which involves analysing solely from the videos themselves and not from any resulting transcripts (Pirie 1996). Such an approach is extremely time consuming as not all video material is relevant. A potential solution to this, whilst still remaining faithful to the original visual data, is to focus on ‘key moments’ during the diary entries, transcribing these and analysing in NVivo (Noyes 2004). Taylor et al (2019) used an exclusively visual analytical approach, coding unspoken communication such as expressions, emotional states and participant’s choices around what was included in the video framing as well as ‘linguistic detail’ (p. 18) directly into NVivo, resulting in 170 codes and 27 overarching categories. A similar approach is to produce ‘logs’ which contain some transcripted elements and descriptive comment and analysis by the researcher of what they have witnessed. Logs are then coded, the videos reviewed in their entirety and full transcriptions made of particularly striking sections of the video diaries, like the ‘key moments’ approach advocated by Noyes (2004). An iterative process ensues of going back and forth between the logs and video to confirm the saliency of the interpretation and analysis (Pirie 1996, p. 7). A word of caution, if this is done too early in the analytical process it can be reductive thus informed and reflexive decisions around timing of data abstraction are fundamental. As such, some researchers take the decision to not ever transition from primary video data to solely abstracted transcript data, and instead always refer to the original visual data during analysis. For example, Towers (1996) used an approach of unconstrained copious notes whilst watching the videos, with multiple reviews of the videos and the notes increasingly added to during rewatches (Towers 1996 cited in Pirie 1996 p 8.)

Visual Data Richness: Unspoken Communication Whichever approach is taken, repeated viewings of the visual data are essential. Once the researcher is satisfied that the content is familiar, they can then look more deeply to unspoken nuances without needing to focus on the what in narratives but instead spotlight the how. Seeing the participant, their body language, their appearance, form of dress and their tone and being able to review this multiple times enriches analysis. Noyes (2004) comments on Stacey in their study, whom they describe as particularly ‘self-aware’, and how their eyes were downcast during a particular diary entry whilst speaking in a low tone. Stacey outlined feeling inept at maths and repeated particular words ‘I hate it, I hate it, I hate it’ (p. 199) resulting in powerful data, arguably less likely to have been produced during an interview. During the analysis process, Noyes reflects on being able to visualise Stacey saying this as well as see the transcribed text, leading to the production of a far more nuanced and deeper interpretation. The availability of spoken and unspoken communication can, however, give rise to analytical challenges as these may provide conflicting information thus researchers must make difficult decisions on how to handle this if it arises (Jones, Griffin & Sullivan 2000). For example, Buchwald et al (2009) outline a child narrating their account of a good day playing cards at their grandmothers despite their mother having been taken into hospital. Although the child managed to perceive a positive aspect within a challenging situation, the tension over their mother’s illness was palpable in the recording, providing more nuanced information. Thus, the differential data potentially yielded by the spoken and unspoken are not necessarily conflicting, the unspoken can provide an opportunity to deepen analysis of the spoken, by taking another layer into account. 455

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Collaboration with Participants A more collaborative approach to analysis can also be taken, particularly at later stages. Post-video diary reviews across diary entries together with participants, echoing ‘video stimulated recall’ method (see Nind et al 2015), to check interpretations and clarify any inconsistencies or contradictions is a desirable approach (Noyes 2004). Noyes comments that no participants wished to amend any of their video diary accounts following these collaborative reviews. Such an approach can provide reassurance to the researcher of thorough analysis and faithful interpretation but additionally empower participants with the opportunity to modify or further explain their original narratives. Participants further reflections can then lead to more data, requiring an iterative analytical process common to any qualitative research method. Researchers in the video-diary study of asthma also chose to collaborate with participants. In this study, participants were provided copies of their visual narratives, once all the data had been collected. After being allowed to view and edit out any unwanted material, participants were asked for a written release of their image and voice recordings for dissemination in any medium, allowing their visual data to be used for multimedia publication, research presentations, and educational purposes (Rich et al, 2000: 470).

Communicating Findings From Video Diary Data Video diary data is rich yet by being, so it also contains a plethora of recorded information about participants, their face and their voice as well as their views presenting confidentiality issues. Video diary data needs to be protected during data collection as well as communication stages. If the videos are recorded in a participant’s own home, it must be ensured in the participant preparation stage that they have somewhere in their home that they can record the diary in privacy and not be eavesdropped upon. Storing video entries appropriately is also crucial and these should be kept away from others perhaps in a drawer (Buchwald et al 2009). Video research with children throws up ethical challenges at data collection and dissemination stages. It is generally accepted as good practice to involve parents in research with young children, unless there are very good reasons not to. Privacy issues can be discussed during joint meetings with parents and children in the preliminary phase of research explaining and emphasising the significance of children’s privacy regarding their personal views. Yet parents should not feel excluded and should also be taken care of during the research process. Buchwald et al (2009) offered parents an interview to discuss in general terms what it can be like for children living with a severely ill parent. Parents in that study reported finding this very useful in thinking through how to help their children. Due to the very specific data that participants sometimes reveal which relate to their unique circumstances, those who know them or any institution they are part of may be able to identify them despite appropriate measures to secure anonymity being utilised such as using pseudonyms. It is generally impossible to prevent this completely and, as with any research, this needs to be made explicit at the outset to participants. However, at the writing stage, highly careful consideration must be made as to what data can be used reflecting on its potential for inadvertent participant identification. A possible solution is to review any quotations and data with participants prior to publication. Notwithstanding this, there can be additional ethical challenges in truly securing the informed consent to use data from those with diminished cognitive abilities due to disability or not being yet fully developed, such as children. No videotapes or parts of recordings should ever be used to share with others for the purposes of research dissemination without the complete consent of participants, and in cases of children or those with disabilities the consent of their carers should also be sought. 456

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Technological Implications Technical decisions around video camera formats such as a participant’s mobile phone, a fixed video camera in a room on a tripod, a video camera given individually to each participant and whether the video cameras are old-style VHS ones with tapes or more contemporary digital ones all yield potential benefits and issues in relation to analysis and file sharing. Noyes (2004) used a standard video camera instead of a digital one to intentionally create a Big Brother diary room feel to their research. Although this was beneficial in the data collection stage in terms of how the participants viewed the camera, this created problems in the analysis stage as it necessitated reviewing the original video tapes and forwarding to the correct point in the tape to see particular ‘key moments’. This task would have been simple with a digital video camera which results in visual computer files but was cumbersome and time-consuming with an analogue video camera.

Participant Empowerment Video diary method has been argued to be more empowering for participants than other methods, partly due to visual methods overall being constructed as more participatory whereby those taking part in the research have greater voice as well as increased control and choice over what is filmed or photographed etc. (Muir 2008; Kaplan & Howes 2004). This prevailing view can, however, be challenged and contested. Although participants may well arguably independently produce much of the data, this is necessary framed within the lens of the researcher’s and research project’s aims and their ‘conceptual framing’ (Jones et al, 2015, p. 398), powerfully described as ‘ventriloquization’ by Piper and Frankham (2007). Moreover, the aforementioned issue of the absence of the researcher who might otherwise stimulate a two-way flow of conversation, has been argued to be unempowering for participants as they cannot seek clarification in the moment or be soothed if they experience distress (Holliday 2007). Another significant issue indicating disempowerment participants may become encumbered by the responsibility to keep a video diary, it may develop into a chore that they must endure and feel they must ‘find things to say’ (Jones et al, 2007, p. 400). The previously highlighted camera shyness and self-consciousness experienced by some research participants, especially at the outset of the video diary keeping phase, can also be unempowering and affect what participants say (Jones et al 2007). Taking all of the above into account, a reasonable position to take on video diaries is that in fact much of the data is co-constructed as with other forms of qualitative research methods, rather than solely participant produced (Lomax & Casey 1998). Thus, it is not unequivocally participant produced and completely empowering but must be considered carefully from a situated ethics perspective according to the particular topic, location, and participant group.

CONCLUSION In conclusion, the analysis of video diary method offered in this chapter attempts to challenge some assumptions about the innovative nature of this method. The amount and range of video diary studies that have been published in the past twenty years alone, shows how useful and important this method has been, and is becoming, as a data collection tool in the social sciences. Of course, as will all research methods, video diary method has its problems and limitations, not least of which is the burden it places 457

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on participants–to learn how to use the recording equipment, to find a private space place to record, to think of things to say, and so on. Nonetheless, the longevity of this method and effectiveness of video capture technologies, means that video diary is an important method for the digital age. In the future, the authors believe that the trend for using video diary method in social science will continue and potentially escalate. The ‘TikTokification’ of the social web means that the short video format is proliferating (Knox, 2019). Social researchers may well capitalise on this trend and build video diaries into their proposals and studies. However, there is a risk that in the future additional pressures could be placed on participants; to record videos in a certain way using certain software, for example. Similarly, the authors foresee the trend for participatory methods leading to an expectation that participants not only create their video-diaries in a certain way but analyse and share them too. Finally, a growing challenge for all social research methods in a digital age concerns data management and protection. Digital protection and security of digital data is a widespread concern. It is important therefore to develop ethical standards and principles to support the digitalisation of video-diary research.

REFERENCES Alaszewski, A. (2006). Using Diaries for Social Research. Sage Publications. doi:10.4135/9780857020215 Bartlett, R., & Milligan. (2015). What is diary method. Bloomsbury Academic. Bates, C. (2013). Video diaries: Audio-visual research methods and the elusive body. Visual Studies, 28(1), 29–37. doi:10.1080/1472586X.2013.765203 Bloustein, G. (1998). ‘It’s different to a mirror ’cos it talks to you’: teenage girls, video cameras and identity. In S. Howard (Ed.), Wired-up: young people and the electronic media. UCL Press. Buchwald, D., Schantz-Laursen, B., & Delmar, C. (2009). Video Diary Data Collection in Research with Children: An Alternative Method. International Journal of Qualitative Methods, 8(1), 12–20. doi:10.1177/160940690900800102 Cashmore, A., Green, P., & Scott, J. (2010). An ethnographic approach to studying the student experience: the student perspective through free form video diaries. A practice report. The International Journal of the First Year in Higher Education, 1(1), 106–111. Cherrington, J., & Washington, B. (2010, July). Shooting a diary, not just a hoop: Using video diaries to explore the embodied everyday contexts of a university basketball team. Qualitative Research in Sport and Exercise, 2(2), 267–281. doi:10.1080/19398441.2010.488036 Edinburgh, L. D., Garcia, C. M., & Saewyc, E. M. (2013). It’s called “Going out to play”: A video diary study of Hmong girls’ perspectives on running away. Health Care for Women International, 34(2), 150–168. doi:10.1080/07399332.2011.645962 PMID:23311908 Gibson, B. (2005). Co-producing Video Diaries: The Presence of the “Absent” Researcher. International Journal of Qualitative Methods, 4(4), 34–43. doi:10.1177/160940690500400403 Halimaa, S.-L. (2001). Video recording as a method of data collection in nursing research. Vard i Norden, 60(21), 21–26.

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Hall-Lew, L., Cowie, C., McNulty, S. J., Markl, N., Liu, S.-J. S., Lai, C., Llewellyn, C., Alex, B., Fang, N., Elliott, Z., & Klingler, A. (2021). The Lothian Diary Project: Investigating the Impact of the COVID-19 Pandemic on Edinburgh and Lothian Residents. Journal of Open Humanities Data, 7, 4. doi:10.5334/johd.25 Helseth, S., & Slettebø, Å. (2004). Research involving children: Some ethical issues. Nursing Ethics, 11(3), 298–308. doi:10.1191/0969733004ne697oa PMID:15176643 Holliday, R. (2007). Performances, confessions, and identities: using video diaries to research sexualities. In G. C. Stanczak (Ed.), Visual research methods: image, society, and representation. Lond. Iviari, N., Kinnula, M., Kuure, L., & Molin-Juustila, T. (2014). Video diary as a means for data gathering with children – Encountering identities in the making. International Journal of Human Computer Sciences, 72(5), 507–521. doi:10.1016/j.ijhcs.2014.02.003 Jones, R. L., Fonseca, J., De Martin Silva, L., Davies, G., Morgan, K., & Mesquita, I. (2015). The promise and problems of video diaries: Building on current research. Qualitative Research in Sport, Exercise and Health, 7(3), 395–410. doi:10.1080/2159676X.2014.938687 Joseph, D. H., Griffin, M., & Sullivan, E. D. (2000). Videotaped focus groups: Transforming a therapeutic strategy into a research tool. Nursing Forum, 35(1), 15–20. doi:10.1111/j.1744-6198.2000.tb01173.x PMID:10847061 Kaplan, I., & Howes, A. (2004). ‘Seeing through different eyes’: Exploring the value of participative research using images in schools. Cambridge Journal of Education, 34(2), 143–155. doi:10.1080/030 57640410001700534 Knox, E. (2019). ‘TikTokification’ and the top trends for FMCG and retail marketers in 2021. The Drum Network. Lindsay, G. (2000). Researching children’s perspectives: ethical issues. In A. Lewis & G. Lindsay (Eds.), Researching children’s perspectives. Open University Press. Lomax, H., & Casey, N. (1998). Recording social life: Reflexivity and video methodology. Sociological Research Online, 3(2), 121–146. doi:10.5153ro.1372 Minnis, A. M., & Padian, N. S. (2001). Reliability of adolescents’ self-reported sexual behaviour: A comparison of two diary methodologies. The Journal of Adolescent Health, 28(5), 394–403. doi:10.1016/ S1054-139X(00)00218-4 PMID:11336869 Morrow, V. (2001). Using qualitative methods to elicit young people’s perspectives on their environments: Some ideas for community health initiatives. Health Education Research, 16(3), 255–268. doi:10.1093/ her/16.3.255 PMID:11497110 Muir, S. (2008). Participant produced video: giving participants camcorders as a social research method, Real Life Methods. University of Manchester. Available from: https://www.socialsciences.manchester. ac.uk/morgan-centre/research/resources/toolkits/toolkit-04/

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Nash, M., & Moore, R. (2019). Exploring research relationships and other ethical challenges of participatory visual research in remote environments. Journal of Sociology (Melbourne, Vic.), 55(3), 604–623. doi:10.1177/1440783318802982 Noyes, N. (2004). Video diary: A method for exploring learning dispositions. Cambridge Journal of Education, 34(2), 193–209. doi:10.1080/03057640410001700561 Pink, S. (2009). Doing Sensory Ethnography. Sage Publications. doi:10.4135/9781446249383 Piper, H., & Frankham, J. (2007). Seeing voices and hearing pictures: Image as discourse and the framing of image-based research. Discourse (Abingdon), 28(3), 373–387. doi:10.1080/01596300701458954 Pirie, S. E. B. (1996). Classroom video recording: when, why and how does it offer a valuable data source for qualitative research? Paper presented at the 18th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education, Panama City, FL. Punch, S. (2002). Research with children: The same or different from adults? Childhood, 9(3), 321–341. doi:10.1177/0907568202009003005 Rich, M., Lamola, M. S., Gordon, J., & Chalfen, R. (2000). Video intervention/prevention assessment: A patient-centered methodology for understanding adolescents’ illness experience. The Journal of Adolescent Health, 27, 155–165. doi:10.1016/S1054-139X(00)00114-2 PMID:10960213 Rich, M., & Patashnick, J. (2002). Narrative research with audiovisual data: Video Intervention/Prevention Assessment (VIA) and NVivo. International Journal of Social Research Methodology, 5(3), 245–261. doi:10.1080/13645570210166373 Rose, G. (2016). Visual Methodologies: An introduction to researching with visual methods (4th ed.). Sage Publications. Schollon, R., & Schollon, S. (2004). Nexus Analysis: Discourse and the emerging Internet. Routledge. doi:10.4324/9780203694343 Simmons, H., & Usher, R. (Eds.). (2012). Situated Ethics in Educational Research. Routledge. doi:10.4324/9780203354896 Taylor, A. M., van Teijlingenb, E., Ryan, K. M., & Alexander, J. (2019). ‘Scrutinised, judged and sabotaged’: A qualitative video diary study of first-time breastfeeding mothers. Midwifery, 75, 16–23. doi:10.1016/j.midw.2019.04.004 PMID:30981161 Wiles, R., Crow, G., & Pain, H. (2011). Innovation in qualitative research methods: A narrative review. Qualitative Research, 11(5), 587–604. doi:10.1177/1468794111413227

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The Walkthrough Method: State of the Art, Innovative Aspects, and Application Fields Michela Cavagnuolo Sapienza University, Italy Viviana Capozza Sapienza University, Italy Alfredo Matrella Sapienza University, Italy

ABSTRACT Nowadays the social scientists are called to integrate within their studies new tools that modify and innovate the scientist’s typical toolbox. Digital platforms, media, and especially apps pose further challenges to social scientists today, as they are an important place of significant socio-cultural, economic, health, relationships, and entertainment transformations. When studying digital technologies, in fact, it’s important to pay attention to both their socio-cultural representations and technological aspects – since even design and data outputs have social and cultural influences. In this context, new research questions arise; among all the possible tools in the digital method toolbox, the walkthrough method is a noteworthy way to answer them. Starting from these considerations, this chapter aims to analyze, through a review of the literature, the birth and development of the walkthrough method in its various meanings to identify the innovative aspects and fields of application.

INTRODUCTION AND GOALS Digital development has invested predominantly in all fields of society: the field of sociology and in general the field of social sciences are no exception. Starting from these considerations, social scientists are called to integrate everything within their studies with technological innovations and new tools that modify and innovate the scientist’s typical toolbox. Digital platforms, media and especially Apps DOI: 10.4018/978-1-7998-8473-6.ch028

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pose further challenges to social scientists nowadays (Light, 2016), as they are an important place of significant socio-cultural, economic, health, relationships, and entertainment transformations (Light, Burgess& Duguay, 2016). When studying digital technologies, indeed, it is important to pay attention to both their socio-cultural representations and technological aspects – since even design and data outputs have social and cultural influences. In this context, new research questions arise; among all the possible tools in the digital method (Rogers, 2013) toolbox, the walkthrough method, object of our reflection, is a noteworthy way to answer them. Indeed, in addition to the purely technical aspect, using the walkthrough method it is possible to combine scientific and technological studies of cultural nature, allowing a critical analysis of the cultural meanings embedded in the creation and use of digital products, both by developers and by users. One of the first uses of the Walkthrough as a technique - to distinguish it from the acceptance of authors like Grimes (2015) and Singh et al. (2000), regarding consumption and evaluation of cultural goods - came from software engineering (Human-Computer Interaction) and aimed to improve the quality of code and user experience, correcting them if it was difficult for users to follow the intended procedures or pathway (Fagan, 1976; Lewis et al, 1990; Nickerson & Landauer, 1997). Moreover, according to Duguay (2017), the Walkthrough method is not far from social scientists’ research field. Indeed, the author combines Giddens’ conceptualization of authenticity, that is the ability to refer to a coherent biographical narrative, with Callon’s sociology of translation (1989), that is part of the ActionNetwork Theory (ANT) and summarizes in four phases the process of translation: problematisation, interessement, enrolment and mobilisation. Using these theories and the Walkthrough method - that questions Tinder’s technological architecture, promotional materials, and related media - Duguay was able to identify how Tinder sets up a network of human and non-human actors that frames authenticity as established through their Facebook profile and follows regulatory standards relating to age, gender, ethnicity and socio-economic status. This approach paves the way for future investigations into user responses and demonstrates the broader applicability of this theoretical approach to identify human and technological influences on building authenticity with digital media. Over the years, thanks to further developments, the Walkthrough method has been integrated to analyze the users’ point of view and to evaluate the usability of software applications; therefore, it is configured as an analytical analysis tool that can combine analyses carried out across digital platforms (Katz, 2020) with classic qualitative techniques such as: interviews, for example to analyze how, in the context of global migration, the use of digital media can maintain transnational connections or to analyze the governance of different Apps and compare them (Li, 2020); focus groups, to evaluate the implementation of new Apps through the users’ experiential system (Wardhani et al.,2019); content analysis, to analyze the terms of service and privacy policies and establish measures for the collection, storage, transfer, use and disclosure of App data (Heemsbergen & Molnar, 2020); critical analysis of the discourse, to analyze the digital intermediation of connectivity services (Cabalquinto & Wood-Bradley, 2020); participant observation, to carry out comparative analyses on the phenomenon of platformization (Nieborg, Duffy & Poell, 2020). Anyway, the approach that developed the most is the one of usability evaluation: the Human-Computer Interaction (HCI) implies an iterative process in which evaluation is fundamental to inspect the usability of the user interface. Usability evaluation, indeed, investigates how easy and enjoyable it is for users to efficiently use a system (Nielsen & Mack, 1994) and in general what are the system’s specific strengths and weaknesses associated with its design. Usability is not an objective concept because it depends on the evaluator’s personal interpretation of the HCI, that’s why usability evaluation approaches try to identify what are the required features to consider a system “usable”. Many 462

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authors have classified software evaluation methods in different ways (Hartson, Andre & Williges, 2020; Hilbert & Redmiles, 2000). For example, Oppermann e Reiterer (1997) distinguish between subjective evaluation methods, objective evaluation methods, experimental evaluation methods and expert evaluation methods. The latter includes what Jacobsen (2000) defines the most robust usability walkthrough, that is the cognitive walkthrough. This technique evaluates the design of a user interface trying to understand if it is easy to use even without formal training (Rieman, Franzke & Redmiles, 1995). To do so, the expert follows a sequence of actions necessary to accomplish some tasks and identifies possible usability problems (Ivory, Sinha & Hearst, 2001). Cognitive Walkthrough was born in 1990 thanks to Lewis, Polson, Wharton and Rieman, but since then other authors have proposed lots of different versions and extensions of this technique: heuristic walkthrough, the Norman Cognitive Walkthrough method, Streamlined Cognitive Walkthrough, Cognitive Walkthrough for the Web, Groupware Walkthrough, Activity Walkthrough, Interaction Walkthrough, Cognitive Walkthrough with Users, Extended Cognitive Walkthrough, Distributed Cognitive Walkthrough, Enhanced Cognitive Walkthrough (Mahatody, Sagar & Kolski, 2020). However, all the procedures and approaches can be mixed within a pluralistic approach, that is a co-interpretation and a co-designing between the designer and the user. Thanks to this collaboration, the Pluralistic (Cognitive) walkthrough (Bias, 1994) makes possible not only to understand if task goals are being reached, but even to identify problems linked with memory and cognitive overload and to evaluate the overall ease of use (Faiola, 2007). Starting from these considerations, this chapter aims to analyze, through a review of the literature, the birth and development of the walkthrough method in its various meanings, to identify the innovative aspects and fields of application. To achieve this goal, 130 scientific articles (1990-2021) were extracted, and then examined, from the Scopus database using the keywords “walkthrough” and “method”, subsequently filtering further for the “Social Science” field. After extracting the first articles, these were analyzed and implemented with other works present on the main Open Access academic platforms, such as ResearchGate, JSTOR and Academia.edu.

THE WALKTHROUGH METHOD The Walkthrough method is a direct interaction mode with a digital interface that examines both the technological mechanisms and the cultural references included in the interface. The Walkthrough method is therefore positioned in the context of STS or in the context of studies that combine scientific and technological studies with cultural studies that aim to identify the connections between contextual elements and technical interfaces. The Walkthrough method is actually an already consolidated procedure in the field of cultural goods consumption and evaluation (Grimes, 2015; Singh et al., 2000) and it has both pedagogical and commercial value. Originally, it was used for advertising analysis, for studying educational games, for reviewing online games, Apps and sites such as YouTube (Lee & Hoffman, 2015; Singh et al., 2000; Smith & Sanchez, 2015). These procedures are able to explain the mechanisms related to systems design. In the academic context, on the other hand, the first uses can be traced in the engineering field in general (Fagan, 1976) and in human-computer interaction (HCI) field to produce user guides previously analyzed in terms of interactions and therefore more suitable for consumers (Lewis et al., 1990; Nickerson & Landauer, 1997).

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Today, the Walkthrough method is used in a much more specific way and it is based on the analysis of the App as a socio-technical artefact. This method is indeed used to understand how the interface guides users and how it shapes their experiences, integrating techniques as observation, screening of different screens, the description of various functionalities and data flows of the activity. The Walkthrough method is important for several reasons: the first one of interest are the research on Apps or digital platforms, the second important one is that thanks to this method it is possible to proceed with Apps or platforms usability evaluation. Indeed, it must be emphasized that the Walkthrough method is configured as a procedural basis for the other technical variations that have developed over the years, as it will be discussed in the next paragraph. When talking about the Walkthrough method it is therefore evident that the first relationship to be analyzed is the one between the Apps and the new methodological challenges. Several authors emphasize that researchers must be very pragmatic when using this method, because it is always necessary to conform it to the context of the research. App development is associated with the introduction of smartphones in the digital media environment and with the decline of the open web. Given that Apps “solve particular, often singular, user needs - originally, business needs” (Pressman, 2005), they are now a very significant component of digital cultures and the digital economy and that is why they represent an important object of study. However, researchers encounter difficulties when trying to approach the analysis of Apps. Often the first problem that researchers have to face it is linked to the fact that Apps are closed systems (Burgess, 2012) and that their sources of basic codes are not public; this does not allow the researchers to study the structure and the operative code, which are at the basis of the study of web pages and software programs that have instead accessible extensions. Furthermore, researchers now collect digital data by querying APIs (Application Programming Interfaces), which are protocols that allow Apps to interact with other software programmes, but which often return only partial or limited data sets (Burgess & Bruns, 2015). Rieder & Röhle in 2012 stated that socio-technical closure creates new challenges for access to data and for the consolidation of digital research methods (Rogers, 2013): the fact that it is possible to collect large amounts of data or metadata often implies an analysis that neglects the symbolic and cultural elements. It is in this context that the Walkthrough method comes into play: since it is a hybrid technique, this method is an innovative and useful way of integration for the development of studies in the field of STS. In order for App analysis to be innovative, it is therefore necessary to pay particular attention to the socio-cultural representations incorporated in them and to the technological characteristics that can influence cultural and social dynamics. Light, Burgess and Duguay (2018) used the Walkthrough method to study the world of software applications (App) starting from previous studies that they themselves have carried out as: • • • •

study of the interaction of a social profile with Bots (Light, 2016 a); studies on the use of Apps to explore different sexual cultures (Light, 2016 b); evaluation of user’s authenticity in different social networks by linking them together (Duguay et al., 2017); study of creative features on Instagram (Duguay, 2016).

In particular, the scholars registered on the Clue App, an application used for monitoring the menstrual cycle, in order to study how sexual activity changes during the various phases of the cycle. The first step for the researchers is to simply register on the App, to analyze icons, features, technical aspects and symbolic elements such as images and texts. This process is in turn integrated with a context analysis, 464

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an analysis of the operating model and above all of the governance of the specific App. The procedure (Light, Burgess & Duguay, 2018) of what we have defined as “basic” Walkthrough is presented in detail below, which is a method that represents the shift towards cultural and technological analysis in the computational field (Berry, 2011).

Phase I: Analysis of the Environment of Use According to Van Dijck (2013), researchers must take into account, as well as users, contents and technology, the socio-economic and cultural aspects of the platforms; for this reason, the first phase of the analysis includes the vision of the App, of the operating model and of the governance. 1. Vision: this action involves the study of the target users and the final use of the App, usually traceable in the supplier’s organizational materials; it also allows to study the concepts that the App wants to convey with its business (Papacharissi, 2009). Understanding the original vision that the App transmits provides a basis for identifying how users approach it and with what cultural background. For example, the authors state that there are substantial differences between users who sign up for dating Apps that claim to find a soul mate and Apps that are based on the intent of bringing together people who want to meet occasionally. 2. Operating model: analyzing the operating model means studying the business strategy and income funds; this leads to the identification of the underlying political and economic interests. Often the Apps have within them various functionalities that can be purchased to enter a new dimension consisting, for example, of a greater level of interaction and also this dimension is configured in turn as a new level of analysis. Another “form of payment” that the Apps receive is the transfer of users’ sensitive or geolocation data which are often resold for commercial purposes to other platforms (Nieborg, 2015). The materials produced, sources and information are all useful elements for studying the operating model of the App. 3. Governance: governance is the way in which the supplier regulates users’ activities, that is rules, guidelines and the actions that different types of users can carry out. For example, it is very interesting to analyze which types of content the Apps do not allow to publish and how the content reporting mechanism works. In this regard, it is very important at this stage to study the Terms of Service (TOS) which contain how the App defines users behaviour (one of the tools used to study TOS over time is the Internet Way back Machine in Archive). The usage policies and sometimes the FAQs also provide information on ownership, data applications, privacy and copyright licenses on the generated content (Fielser et al., 2016).

Phase II: The Technical Walkthrough 1. Registration and entry: each App can have a different registration method, both as regards the device and the way to access; for example, in the lasts years many Apps are using the direct connection with Facebook, while gaming Apps often have to be installed through a specific URL and then logged in. 2. Everyday use: in order to an in-depth analysis of the various features of the application, researchers must use the method for some time; only in this way they can track down the mediators and fully navigate the interface.

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3. App suspension, closure and leaving: the process of suspending or closing the App often does not mean a total closure (Karppi, 2011), indeed if the users try to log in again on an App that was previously deleted (an example is Facebook), the App will rebuild the entire personal database to restructure the account. The analysis of unexpected practices is also very important, because often users do not use platforms as the developer imagined, as in the case of Tinder, where the initial project envisaged the meeting between soul mates but over the years it has transformed into a showcase of sex (Maureira, 2014). At last, it must be emphasized that, especially when dealing with certain applications not yet socially accepted, the researchers must remain “out of the game” and must not in any way harm users’ privacy (Hammersley & Atkinson, 2007). In this phase, researchers empirically come into contact with the App, accesses it, uses the screens and explores the menus with a critical eye. Moreover, they track key players, purchase icons and buttons as well as actions that allow users data collection such as the ability to record video, audio and various messaging. At this stage, the researchers’ ability to use observation techniques is very important to understand how the App describes, for example, gender, ethnicity and sexuality. The technical and cultural influences are studied “throughout mediator character” that provides indications on how to create relationships on the App. The mediator studies the following characteristics: a. User interface arrangement: the way in which the buttons are positioned with respect to specific functions; b. Functions and features: required fields, connection with other accounts, pop-ups; c. Textual content and tone: menu order, available categories, drop-down menus; d. Symbolic representation: phase where it is important to use a semiotic approach to grasp the links between cultural associations and usage scenarios; this often means studying branding and colours. Before moving on to the next paragraph, that is to the classification of usability testing methods and to the importance of the Cognitive Walkthrough, it is good to underline that there is a parallel definition of the Walkthrough method (basic) that has developed in researches within organizations: the SocioTechnical Walkthrough (STWT) Herrmann (2009). The author defines STWT as a methodological approach that takes into account a multitude of aspects and makes them the subject of analysis, specifically communication, negotiation and decisions taken within the development of socio-technical systems. STWT results can be used in skills development, organizational change, programming and software configuration. Its peculiarity lies in taking into consideration the points of view of all stakeholders, integrating them and accompanying participatory planning throughout its development. Socio-technical systems include the interaction and influence between human actors, organizational units, communication processes, technical units and human-computer interaction. These systems are characterized by a continuous evolution influenced by conflicts and power relations; indeed, the integration of all these functions is the most problematic thing when putting this technique into practice. Herrmann, Loser & Jahnke (2007) proposed this procedure as a documentation and facilitation method and with its further development they implemented the field of Computer Supported Cooperative Work (CSCW). Unlike the classic method, being a participatory procedure, the authors create action research practices through workshops and focus groups that the facilitator develops in this way:

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1. Preparing the first analyses of the work that have to be shown to the stakeholders; 2. Asking the members of the group step by step, what the next phase of the work should be according to them; 3. collecting responses, suggestions and proposals; 4. integrating the various perspectives; 5. analyzing congruent prospects and comparable positions; 6. integrating the new contributions into the work plan and identifying the new documentation to be surveyed and analyzed. The result of this Walkthrough method (STWT) is therefore a set of new schematic models to be used to implement the project which includes details and points of view of all stakeholders.

THE COGNITIVE WALKTHROUGH METHOD Classifications of Usability Evaluation Methods As has been shown above, the Walkthrough method, although having several applications, has become more established as a usability evaluation method. Usability evaluation methods are indeed often used since, as expressed by Harrison, Henneman & Blatt (1994), they bring advantages both for the end user and for organizations and consequently in terms of the market. Among the usability Walkthrough, Jacobsen (2000) believes that the most robust one is the Cognitive Walkthrough; precisely for this reason this technique will be exposed as much as possible in detail in this paragraph. In order to contextualize and collocate the Cognitive Walkthrough within the usability evaluation methods, it is necessary to offer an overview of the various classifications that have been proposed in this regard. Taking up the distinction offered by Scriven (1967), it is possible to identify two types of evaluation: the formative and the summative one. Formative evaluation includes those methods that investigate systems’ usability when they are still in the design phase. The methods that fall within the summative evaluation, on the other hand, are interested in fully functional systems in order to measure the effectiveness of the single system or by comparing multiple systems; in this way it is possible to understand if a system is effective and/or which of two or more systems is better (Bauernfeind, 2008). However, there are scholars who believe that the most used classification is the one that distinguishes usability evaluation methods into three groups: test, inspection and inquiry. The first group includes methods that involve subjects that can be associated with the potential users who are asked to perform typical tasks to measure their performance or other aspects. For this group of methods and techniques, a mandatory requirement to be met is the presence of a functioning system or its prototype, otherwise it is impossible to analyze the interaction between the user and the interface. Inspection methods, on the other hand, rely on expert judgment. The experts can be human-computer interaction experts or software engineers or domain experts who can operate at any stage of system design. Finally, inquiry methods investigate what are the preferences, desires and behaviours of users; in this way they can trace the fundamental requirements needed for the system design (Thimbleby, 2006). As a promising variant of this latter classification, it is possible to refer to the proposal contained in State of the Field theoretical background (2008) in which the addition is attributed to the author Luis Olsina, with reference to web sites usability evaluation, of a fourth group of methods, namely those that simulate the interaction

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between users and sites. As it can be deduced from what has been explained up to now, the possible classifications are many and often with common aspects. For this reason two other classifications considered important will be mentioned, the one of Lindgaard (1994), based on the difference between the moment in which the data are collected and if the method or technique is conducted on field or in a laboratory, and that proposed by Hilbert & Redmiles (2000), in which methods are classified as predictive, observational and participatory (Bauernfeind, 2008). For the purposes of the narrative proposal of this paragraph, the classification formulated by Opperman & Reiterer (1997) is of particular interest, because the methods are divided as follows: subjective evaluation (e.g. questionnaires or interviews); objective evaluation (e.g. observations or video recordings); expert evaluation (e.g. Heuristic evaluation or Cognitive Walkthrough) and Experimental evaluation. Among these last four types of usability evaluation methods, the one that most pertains to the topic we want to advance is clearly that of expert evaluation. As in the example proposed above, the first of these methods that we want to expose is the one of Heuristic evaluation. The Heuristic evaluation makes it possible to identify any type of problem that may occur during the interaction with the interface, from the most trivial to the most serious. Indeed, it follows the ten general principles for the design of an interface identified by Nielsen (1994): 1. Visibility of system status: the System must provide appropriate and fast feedback to constantly inform users about what is happening; 2. Match between system and the real world: the system must use user-oriented words, phrases and concepts rather than the system ones; 3. User control and freedom: users must be able to easily notice how to get out of an error situation; 4. Consistency and standards: use platform conventions so as not to confuse users about the possibility that different words, situations, or actions mean the same thing; 5. Error prevention: identify the conditions most prone to errors to eliminate them or to ask users for confirmation before they perform the action; 6. Recognition rather than recall: to reduce the number of information that users must remember, objects, actions and options must be visible or easily found when needed; 7. Flexibility and efficiency of use: the system must offer the ability to customize the experience to expert users, without forgetting to provide for the interaction needs of novice users; 8. Aesthetic and minimalist design: avoid providing irrelevant information or information of little need, as the relative visibility of the relevant ones diminishes; 9. Help users recognize, diagnose, and recover from errors: The error messages must be as clear as possible and indicate in simple language, thus avoiding the codes, what the problem is and how to solve it; 10. Help and documentation: the information must be easily identifiable, therefore not too extensive, and effective for users, so it is better that they are task oriented and that they list in a concrete way the actions to be taken. However, expert’s judgments underlie a certain subjectivity and are influenced by their experience, background and talent in anticipating what users find easy or difficult when using a system (Oppermann & Reiterer, 1997). Lindgaard (1994) defines it as a detailed analysis of informal subjective usability conducted by experts who simulate the perspective of a typical end user using vague guidelines and not specific rules or procedures, as happens instead in the Cognitive Walkthrough. 468

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Cognitive Walkthrough: Birth and Evolutions The Cognitive Walkthrough was proposed by Lewis, Polson, Wharton & Rieman in 1990 with the aim of verifying whether the studied interface is usable even without any previous learning (Mahatody, Sagar & Kolski, 2010). Indeed, in the same year Polson and Lewis proposed a learning by exploration model called CE + (because of its origins from an earlier work on CCT and EXPL), from which they identified guidelines for the design of systems which use can be learned during exploration. The CE + model combines aspects of the Cognitive Complexity Theory (CCT), the analysis of the outcomes derived from the learning procedures from examples (EXPL) model, the initial decision process that can be found in puzzle-problems literature and learning from current cognitive architectures (Polson & Lewis, 1990), obtaining a model based on: a problem solving component, which allows the choice of actions; a learning component, which analyzes the effects of choices and stores the results as CCT rules; an executive component, which allows to activate the rule suited to the context by coordinating its execution with the problem solving component (Mahatody, Sagar & Kolski, 2010; Polson & Lewis, 1990). For this to be possible, the eight Design Principles for Successful Guessing must be respected, as set out by Polson and Lewis: 1. Make the repertoire of available actions salient: this means that the actions that users can choose from must be presented explicitly, otherwise the operations are more difficult to learn and remember; 2. Use identity cues between actions and users goals as much as possible: it is necessary that all the materials associated with the actions share the terms with which the users represent their goals; 3. Use identity cues between system responses and users goals as much as possible: users must be able to understand if the action performed is actually bringing them closer to achieving the goal of interest; 4. Provide an obvious way to undo actions; make available actions easy to discriminate: in case users perform an incorrect action, they must be able to go back; 5. Make available actions easy to discriminate: in order for users to recognize the most suitable action to achieve the objectives, the operations to be performed must not seem similar. 6. Offer few alternatives: an excessive number of possibilities to choose from makes it difficult to identify the correct alternative; 7. Tolerate at most one hard to understand action in a repertoire: if people do not understand the consequences of the chosen actions, they will find it difficult to identify the correct one; 8. Require as few choices as possible: A long series of choices to make can result in a low probability of successfully completing the entire process. At this point it is possible to explain what the Cognitive Walkthrough is starting from a fundamental distinction with the already explained general principles of interface design (Nielsen, 1994). As it will perhaps become evident comparing these different guidelines, the Cognitive Walkthrough is presented as a more formal method than Heuristic evaluation as it requires a greater specificity of the user interface and of the tasks to evaluate usability (Dyckhoff et al., 2012) – as it will be better shown later presenting the numerous evolutions of this method. In any case, in its first version already, the Cognitive Walkthrough is concerned with both the navigability aspect of the interface and the cognitive processes implemented by the users. The activated evaluation is divided into two phases: the first foresees that the evaluators underline a set of tasks to be performed and identify the actions to be performed for each of them. In

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the second phase, on the other hand, each action is analyzed by selecting and executing the actions in order to evaluate the system response starting from the process of the previously exposed CE + model (Mahatody, Sagar & Kolski, 2010). The same authors later proposed an extension of the CE + model, as they realized that the first version of the Cognitive Walkthrough allowed to identify only 50% of real usability problems (Mahatody, Sagar & Kolski, 2010). Following this extension, the Cognitive Walkthrough refers to the cyclic cognitive mechanism, taking up Norman’s model of action theory, which involves a series of steps that start from the users’ initial goals, leading to the generation of an action plan which is then carried out, and arrive at the feedback evaluation, in order to review the objectives and thus resume the cycle. These steps make it possible to bridge the gap between the users’ objective state, which is psychological, and the system state, which is physical (Norman, 1986). In addition to this theoretical background, the CE + extension also considers Kintsch’s construction-integration model, which describes the processes by which users integrate an input with prior knowledge to build a representation that will allow them to perform the task: since knowledge influences interpretation, it is essential to understand how knowledge is organized, that is, how associations between concepts and propositions occur (Kintsch, 1988). At this point, the two phases that characterize the model are possible, namely that of construction, in which all possible constructions and relationships are built, and that of integration, in which those unsuitable for the context are identified and then eliminated. Starting from these assumptions, the second version of the Cognitive Walkthrough can question the way in which users perceive the interface and the representation of the texts/objects that compose it, taking into consideration users’ background and knowledge (Mahatody, Sagar &Kolski, 2010). The result is a form with specific questions to which the evaluator must answer to analyze the interface, following also in this case - as in the first version of the Cognitive Walkthrough - two phases: preparation and evaluation. In the first phase, the evaluators select a set of prototype tasks of the various types of tasks that the application is able to support. For each task, the initial state of the interface, the sequence of actions required to complete the task and users’ starting objectives are described. In the next phase, however, the interaction between the user and the interface is analyzed in detail to understand what goals users could have before the action, if the requests and the labels of the interface will induce the users to perform the correct action, assuming the correct goals, and finally how the users’ goals will change based on the interface feedback after the action has been performed (Polson et al., 1992). Given the complexity of this version of the Cognitive Walkthrough, an automated implementation has been proposed thanks to the use of Apple’s Hypercard. This made it possible to evaluate each action in a short time without repercussions in the accuracy of the forecasts; but, as we will see, it has not reduced the complexity and repetitiveness of the procedures sufficiently (Rieman, et al., 1991). Furthermore, scholars have expressed some perplexities regarding this second version of the Cognitive Walkthrough, such as the absence of a theory that explains how the effects of actions on the environment can generate iterations of the interaction, as well as the absence of a theory that explains how the context of the interaction can lead to differences in user interaction (Ryu & Monk, 2004). The authors Ryu and Monk therefore propose an extension of the Cognitive Walkthrough, called Interaction Walkthrough which aims to identify problems of interaction between goals, action and effects that can occur if unpredictable actions are planned to achieve a goal, if the same action produces different effects based on the system mode and finally if goals reorganization leads to goals constructed in an ambiguous/irrelevant way or if the information offered by the system are misleading or even missing (Mahatody, Sagar & Kolski, 2010). In particular, Interaction Walkthrough is based on four steps that can be iterated: clean reverse 470

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engineering, generally ignoring non-interaction issues, using a device, its user manual and training material; development of an accurate simulation; recording design questions and queries, arising from Steps 1 and 2; review with domain experts using the original device and/or simulation. One of the main advantages of this type of usability evaluation is the fact that, instead of considering prototypes that may also be non-functional, it starts from a functioning system, thus leaving no room for the possible risks deriving from the imaginative effort required of the evaluators in other types of inspection, such as Cognitive Walkthrough (Thimbleby, 2006). We therefore note once again the presence of some limitations found in the Cognitive Walkthrough presented so far, which is why there are lots of other variants. Before presenting them, however, it is necessary to premise that the creators of the method have already proposed a variant in 1994 and it will be this third version that will inspire most of the declinations of the Cognitive Walkthrough, as it is considered boring but easier to learn and use than the previous version (Mahatody, Sagar & Kolski, 2010). This version of the Cognitive Walkthrough indeed provides that the evaluators consider in sequence each of the actions that users should follow to perform a task, imagining a story that tells the typical user interaction with the interface. They therefore ask themselves for each step what users would like to try to do and what actions are made available by the interface: when the users’ intentions lead them to select the appropriate action, the interface is correctly designed (Wharton et al., 1994). The evaluators must therefore imagine a plausible scenario for each action and answer 4 questions. The first one concerns users’ thought before they begin to perform the action, that is if they will intend to achieve a correct effect; the second one concerns users’ ability to identify the necessary commands, or if they will be able to realize that the correct action is available; the third one questions users’ ability to identify the command, that is to associate the correct action with the effects they try to achieve; finally, the evaluators wonder if users are able to interpret the feedback they receive from the interface, in order to understand that they have performed the correct action to approach the solution of the task (Mahatody, Sagar &Kolski, 2010). In order to answer these questions, before starting the Walkthrough it is essential to pay attention to some fundamental aspects: who the users of the system are; what kind of tasks will be analyzed; what is the correct sequence of actions for each task and how it is described; how the interface is defined (Wharton et al., 1994). From these reflections, 7 variants of the Cognitive Walkthrough were born: Norman’s Cognitive Walkthrough, Streamlined Cognitive Walkthrough, Cognitive Walkthrough for the Web, Activity Walkthrough, Extended Cognitive Walkthrough, Distributed Cognitive Walkthrough, Enhanced Cognitive Walkthrough. All these variants are united by following the same procedural logic of the third version of the Cognitive Walkthrough, if not throughout the evaluation process, at least in one phase of it. This also means that all these variants refer at least partially to the use of the questions previously exposed, but some of them add other analysis tools (Norman’s Cognitive Walkthrough, Streamlined Cognitive Walkthrough, Cognitive Walkthrough for the Web, Activity Walkthrough, Enhanced Cognitive Walkthrough). Finally, most of the variants (Norman’s Cognitive Walkthrough, Cognitive Walkthrough for the Web, Activity Walkthrough, Extended Cognitive Walkthrough) focus on different theories than those that are central to the third version of the Cognitive Walkthrough. Finally, there are variants of the Cognitive Walkthrough that cannot be considered extensions of any of the three versions proposed by the original authors, but rather real declinations of this technique which indeed often strongly hybridize Cognitive Walkthrough with other usability evaluation methods. These declinations are the Heuristic Walkthrough, the Groupware Walkthrough and the Cognitive Walkthrough with Users.

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The Heuristic Walkthrough combines the advantages of the Heuristic evaluation, the Cognitive Walkthrough and the Usability Walkthrough. It therefore provides a list of tasks that users should perform, a list of questions related to the cognitive sphere and a list of usability heuristics (Sears, 1997). Evaluators use the first two lists in the first phase of the Walkthrough to focus on the priority aspects, while in the second phase they use the knowledge obtained in this way to freely explore every aspect of the system and the list of heuristics (Mahatody, Sagar & Kolski, 2010). The Groupware Walkthrough, on the other hand, is a procedure used specifically for evaluating the usability of collaboration software, namely Groupware. This technique allows to take into account information about users and work contexts and therefore to include considerations for the complexities of teamwork. The two components of the Groupware Walkthrough are an activity model for identifying and analyzing real-world collaborative activities and a detailed process for evaluating a system’s support for such activities (Pinille & Gutwin, 2002). Finally, the Cognitive Walkthrough with Users is divided into three phases. In the first one, the system is evaluated using the formal aspects of the Cognitive Walkthrough: the definition of the data necessary to carry out the Walkthrough, walking through the actions and reporting the results of this analysis in a document. Later, people with typical user characteristics are identified and the experts explain to them about the usability test, so that each of them can try to achieve a series of tasks defined according to their user profile and report their impressions about interactivity, design, functionality, etc., first through thinking aloud and then, once the series of tasks is over, giving one’s point of view regarding any problems encountered (Granollers & Lorés, 2006). In the third phase, however, the experts review the doubts expressed by users during the third phase (Mahatody, Sagar & Kolski, 2010). As evident, this declination of the Cognitive Walkthrough suggests the possibility of using a pluralistic approach also in this specific usability evaluation method. The Pluralistic (Cognitive) Walkthrough, indeed, can simulate user interaction process in performing each task as in the Cognitive Walkthrough, but integrating the participation of users and the design team. The latter, guided by the scenario, checks if the goals have been achieved, if there have been problems related to memory or cognitive overload during the execution of the tasks and if in general the interface/system is easy to use (Faiola, 2007).

APPLICATION FIELDS AND EMPIRICAL RESEARCHES In the following paragraph the most significant studies are presented for the purpose of explaining the Walkthrough method and its different meanings. As will be seen shortly, the studies cover a period of about twenty years and take up the most varied forms of integration of the method and different fields of application. The following are the studies collected in the macro-sectors most present in the literature analyzed.

Evaluation Usability Interface One of the most dated studies, but which serves as a basis for subsequent ones, is that of Demetriadis, Karoulis & Pombortsis, which in 1999 already used the Walkthrough to evaluate an educational simulation interface. In this context, the term “evaluation” refers to the process of “gathering data about the usability of a design or product by a specified group of users for a particular activity within a specified environment or work context” (Preece, Rogers, Sharp, Benyon, Holland & Carey, 1994: 602). The evalu-

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ation of the interface is a fundamental step because it concerns the part of the software that allows users to communicate with the machine and because it offers the possibility for designers to redesign it thanks to feedbacks received during the evaluation. The authors used the Cognitive Walkthrough (Rowley & Rhoades, 1992) as an evaluation method based on the evaluation of the initial design by experts through a questionnaire (Aedo, Catenazzi & Diaz, 1996), because it is cheap to apply and one of the most used (Catenazzi, Aedo & Sommaruga, 1997). The evaluation session proposed in this case lasted about 4 hours with 6 different interface activities, after this phase the evaluators/experts filled out a questionnaire that was analyzed and used to redesign and integrate the interface by the users. Another example of user interface evaluation is a study on the development by some travel companies to market an online travel guide (Plach & Wallach, 2002; Kjeldskov et al., 2005) that had to be offered as an additional service to their customers to be more competitive on the market (Wardhani et al., 2019). The authors designed the interface by making a preventive study in which they collected all the needs of tourists through focus groups (Krueger, 2000); then, several users of similar platforms were interviewed and a context survey was carried out. The first results led to the initial design of the App interface, then an evaluation was carried out through the Usability Walkthrough and finally usability tests were used to evaluate its effectiveness (Discovery, Formative and Evaluative method (Ardiansyah & Ghazali, 2016). Specifically, the Usability Walkthrough (Tullis & Albert, 2013) was carried out through the use of the App by some users (both registered and unregistered) that walked through the menu and the navigation functions to search for places to visit through geolocation. The goal was to understand how users are able to move around the App and which are the most used buttons. One of the results, for example, is the insertion of tutorials and direct button The users were also subjected to a usability test consisting of various items/actions (13) where they have to say the degree of difficulty (1-5), where 1 = very simple and 5 = very difficult, in carrying out every particular action. Finally, the authors used these parameters to evaluate the effectiveness of using the App. Gu, Gu and Laffey in 2011 designed a new mobile learning App with the aim of creating prototype lessons that students can follow even when they are on the move (Sharples 2000; Patten et al., 2006), using Heuristic evaluation and focus groups to understand how to build and improve the functioning of the application in order to build a lifelong learning network that can create benefits in everyday learning (Fischer & Konomi 2007; Sharples 2007; Clough et al., 2008). Specifically, after having designed the application and having analyzed its functionality, the heuristic principles of a pedagogical type were drawn up to be followed during the heuristic evaluation compiled by a group of expert evaluators and then implemented with a focus group to detect the experience of users. Thanks to these analyses, the developers have redesigned both the graphic aspect of the App and the type of content; for example, it was found that in a mobile learning App, highly detailed audio content is more effective than video content. The same procedure was also used to evaluate the use of virtual signage by new drivers (Rane et al., 2016). Sutcliffe and Deol Kaur (2000), instead, used the Cognitive Walkthrough to evaluate the usability of the virtual business park Apps. After designing the App, following Norman’s (1986) action model, the authors decided to evaluate the functionality of the system. (Habibi et al., 2019). Specifically, the evaluation procedure was divided into two sections: the first one more general, where the different functions and the navigation cycle were analyzed; the second more specific, where the users who tested the product answered a questionnaire about specific actions that users can perform, the level of comprehensibility of the various functions and finally the level of difficulty/simplicity of execution. All feedback was then used by the developers to redesign and improve the App (Yuniati, 2019). 473

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Cultural Production and Imaginary Study Another very interesting field of application is that of imaginary studies and online cultural production. For example, Chen (2021), using the Walkthrough method and semi-structured interviews, was able to study the cultural imaginary (Taylor, 2004) of the Chinese immigrant community in Australia that arose on the Tantan dating App. Yu (2017) indeed argues that the social imaginary of the internet is “the repertoire of images, metaphors, stories, and legends” (p. 245) and therefore the repertoire of meanings shared by the reference cultural group (Hine, 2000). The analysis initially focused on the study of the platform, on the analysis of the cultural material produced and especially of the stories published by users. In a second session, instead, with interviews with users of Chinese origin, the author investigates what are the elements that most represent Chinese culture and reconstructs three different perceptions described by the interviewees that influence the imagination itself: a local perception, patriotism and a mixture of cultural references. Katz 2020 uses the Walkthrough to understand how digital platforms reconstruct the concept of peace (Waltz, 1988; Galtung & Fischer, 2013; Lynch, 2013; Bräuchler, 2015), placing it in the context of everyday life (Rovelli, 2018). First of all for his study Katz collected the platforms to be analyzed by searching for them on Google using the keywords “interactive pace”. After identifying the 198 platforms worldwide, he first examined the visual aspects of the Apps, how they are used and the registration phases. The author then divided the platforms into categories: gaming, exploration, listening and interactive participation platforms; each type of platform has its own way of reconstructing the concept of peace and adapting it to everyday life. Duguay (2016) used the Walkthrough to study the elements of the platforms (Instagram and Vine) that can influence the visibility of some profiles and selfies of LGBT people. Senft and Baym (2015) affirm that a selfie can be the daily representation of a culture, of being oneself, of a feeling, of a lifestyle. If some social media sets their algorithms in such a way that some selfies, which do not reflect the dominant culture, do not have the same visibility as others, then that social media is targeting cultural production or at least its media representation. The author chose to compare Instagram and Vine precisely because of the high visual content. After a detailed analysis of the platforms and methods for generating content, the content generated and most present on the home page was analyzed with the respective hashtags. It turned out that Vine is a much more open social network and that democratizes published content more, Instagram instead often brings content similar to the dominant culture: white people with a beautiful body and heterosexuals (Abidin 2014; Alexander & Losh, 2010; An, 2013). Another important study is the use of the Walkthrough combined with discourse analysis to study how commercial and government sectors can influence motive mechanisms and promote the importance of connectivity services for transnational mediation (Cabalquinto & Wood-Bradley, 2020). One of the goals of the work is to reveal how the rhetoric imposed on digital platforms can influence migrant subjectivity and media representations, also consequently influencing public opinion on migrants. The platforms were first analyzed as socio-cultural artefacts and then the strategies hidden behind the contents were studied through discourse analysis (Appadurai, 1996; Van Leeuwen, 1966). From the analysis it emerges that the platforms that support connectivity services define the migrant as a hypothetical customer and therefore offer them connection services; the migrant is seen as an economic subject, but the companies aim at the migrant’s emotional sphere to sell more services. Another interesting contribution in the sphere of the creation of cultural products on platforms is the Chinese study on political discourse, “positive energy”, (Chen et al.,2021) on the Douyin platform. The authors used the Walkthrough combined with content analysis (Neuendorf, 2017) to study how the 474

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platform is used as a tool to strengthen the state of Chinese patriotism (Wang and Lobato, 2019; Lin & de Kloet, 1019). First of all, the study analyzed the functionalities of the platform for the creation, sharing and interacting with the contents that identified the governance procedures and subsequently an analysis of the content was carried out on the “positive energy” section with the collection of about 800 videos with related metadata, captions and views. The videos were then categorized and analyzed by tagging the content, to find out which one was most related to Chinese patriotism. Finally, Nieborg, Duffy & Poell (2020) in their study start from the Covid-19 Pandemic to explain how in this period the process of platformization (Plantin & Punathambekar, 2019) has proved vital for everyday life and how platforms have become further space for cultural production. They affirm that, in order to study the relationship between platform and cultural production, it is necessary to aim for a new methodological orientation that includes the creation of three new categories to study: Platform Economics, Platform Evolution and Platform Geographies. They therefore propose to study business models, privacy, licensing and intellectual property (Cunningham & Craig, 2019), in order to analyze the platforms as a real market where communication and culture are produced. In this regard, they also focus on the offices where the developers are located, on the language they use and on the evolutions over time.

Usability and Human-Machine Relations Connell, Blandford & Green (2004) use the Cognitive Walkthrough to analyze the usability problems of London Underground ticket machines. The authors choose to use this method because it focuses on the ability of the device to support the objectives of the users in all interaction activities and, in addition to the study of the technical system of the machines, they carried out an observation study to detect all the details of human-machine interaction. In particular, they carried out observations of about 3 weeks in different stations, they counted the actual use of the machines for the total number of failures at the first attempt of use, and finally the critical points were identified through interviews with users in difficulty. Finally, by drawing up the list of critical problems observed and not, it was possible to understand what the usability problems were (Kaya et al., 2021). Liljegren & Osvalder in 2004 used a similar procedure in the medical field to evaluate the usability of new equipment to select them and insert them into health care procedures. They then carried out user questionnaires, monitoring patients’ vital parameters and usability tests which have been validated by a team of nurses, doctors and engineers. The Cognitive Walkthrough analysis included some tests such as recording the patient on the device, alarm settings, entering limit parameters and viewing personal reports. After monitoring by observation, questionnaires containing usability tests were given to patients where, on a scale of 1-5, they had to say the degree of difficulty they had encountered on various items concerning both the instrument and its use. So ultimately, especially in this area, usability tests are used both to collect simple patient opinions and to evaluate the functionality of the machine (Liu et al., 2005; Scherf at al 2020). Another study in the human-machine relationship (Johnson, 2010) was conducted on some tools used for monitoring the household activities of the elderly (XiaoYan & Emery, 2013). Using the Cognitive Walkthrough and the Heuristic evaluation a web interface was developed and linked to monitoring tools that allowed family members or nurses to check elderly vital vestments. The information collected in this study is therefore contextual, medical and related to user interaction with both the tool and the web interface (Medina et al., 2010). Specifically, the Cognitive Walkthrough has the purpose of evaluating whether the activities can be carried out in a simple way and indicate the problematic areas; for the

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interface analysis procedure a team of expert evaluators was used, consisting of web designers and psychologists who tested the interface and proposed changes by filling in some questionnaires. To deepen the analysis, on the other hand, heuristic evaluation was used (Molich & Nielsen, 1990; Nielsen & Molich, 1990) which provides the basic principles to be evaluated by the experts (ten general principles for the design of an interface). Using these heuristics, the evaluators made observations and compiled the list which was combined, synthesized and used to modify the interface in the debriefing phase to build a user-friendly web. Similarly, Edwards et al., (2008) used the Heuristic Walkthrough method to evaluate and improve the usability of commercial electronic health records that can help healthcare professionals simplify processes and improve the care quality (Koppel et al., 2005). The authors define Heuristic Walkthrough as a predictive method that can be very useful for product development, reducing service costs and saving money (Bias & Mayhew, 1994; Wolf & Karat, 1997). The evaluation took place in different phases, the different components of the medical record were broken down and each section was assigned to specialized evaluators who drafted the checklist of questions to be used in the evaluation phase. For example, those who work in the ambulance have dealt with the importance of easily entering information and changing the previous ones (if any) and the nurses have dealt with the importance of the presence of the complete list of all drugs that must already be present on the platform to avoid errors. Finally, the technology-expert evaluators took care of testing the functioning of the platform and the compression of information that should not overload the system or slow it down, because time is one of the most important factors in healthcare, especially in first aid. The efficiency of the Heuristic Walkthrough method lies precisely in the use of experts in the sector and in the creation of ad hoc checklists that allow an efficient evaluation (Sears 1995,1997).

Comparing Platforms, App and Governance Jia and Ruan (2020) used the Walkthrough to study the structure of 4 different Apps (TopBuzz, Douyin, TikTok and WeChat), comparing user privacy governance. The first step was to present an overview of properties, functions, business models and strategies. The interface and privacy were analyzed in the registration and deletion phase of the account which allows the use combined with an analysis of the content of the terms of service and of the specific privacy policies for the collection, storage, transfer of App data, use and procedures for transferring data to third parties. The authors also analyzed the Apps both in their original and international versions and, at the end of the comparison, they found that the international Apps have a more transparent governance and attentive to user privacy (Pappas, 2020) and meet the requirements of the GDPR 2016. Ritter (2021), instead, in his study starts from the assumption that, in order to study well the functioning of a platform, the Walkthrough method must be integrated with digital ethnography because only in this way it is possible to understand the socio-cultural assumptions incorporated in the interfaces and the way in which they generate symbolic meanings. Combining such techniques gives digital methods a constitutive role in the study of media and socio-technical assemblages (Madsen et al., 2018: 203). The author states that Shadowing should also be integrated towards software developers, attending company meetings, collaborating with the organization, understanding the culture of the company and testing the platforms before they are even online: “this fusion expands the scope of ethnographic knowledge across the digital-physical continuum. Observational research unravelling the multiple layers of the digitalphysical continuum furthermore helps reclaim human agency and avoids the traps of technological determinism” (Ritter, 2021: 15). 476

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CONCLUSION In conclusion, it can be said that the Apps and the different digital platforms reflect our cultural values​​ and involve not only users but also developers, advertisers and different interactions that shape the daily practices of all of us. The Walkthrough method is an approach that was born precisely to study these interactions. It allows researchers to position the App in a specific context, analyze its vision, operating model and governance, even its daily use and the possibility of cancellation. Thanks to this method it is possible to recognize the cultural values ​​that users transmit on the Apps and, if combined with other methods, it may have no limits of use in the web world: “Combining the Walkthrough method with other forms of data collection heeds discussions of how digital methods can incorporate other digital and traditional methods to expand and compare datasets (Snee et al., 2015). Moreover, since Apps are not stabilized artefacts, it may be necessary to conduct the Walkthrough multiple times throughout an App’s development and updates” (Light, Burgess & Duguay 2016: 896). Specifically, the most fruitful combinations, mentioned above, are those between Apps’ analysis and questionnaires or focus groups, used to evaluate Apps’ usability and improve it; analysis of the technical environment combined with structured or semi-structured interviews to study cultural productions and the collective imagination; combination of the Walkthrough method with the content analysis in order to detect cultural transmission modes. It is also important to underline that, regardless of whether or not to use the different combinations, it is possible to apply these techniques not only to the most varied studies but also to the most varied academic areas, primarily sociology, communication, healthcare, marketing and political science. Finally, from the various studies presented, it also emerges that different techniques are used among evaluation and usability tests to compare the results and above all to be able to extrapolate as much information because, as mentioned, each method and technique has its own specificity; therefore, in addition to the comparison, it is also important to go very deep into the analysis. The Cognitive Walkthrough responds perfectly to these needs, since on the one hand it offers many possibilities of application –as shown by the multiplicity of its variants. On the other hand, it can be seen how this method respects a methodological rigor that orients the evaluator and prevents the analysis from being overly subjectivist. Therefore, using the Walkthrough method in all its forms means using an innovative method, a method that manages to include all the facets of the online world and human-machine interaction. It can be defined as an “all-round” method which, used with the right procedures and with the right mash-ups, can create a new union between the classic researcher toolbox and the new digital analysis techniques that are being presented in this volume.

ACKNOWLEDGMENT This research received no specific grant from any funding agency in the public, commercial, or not-forprofit sectors.

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Social Media Analysis The object of analysis that receives more attention in social research in the digital era is certainly social media, and the techniques to extract, analyze, and systematize the contents proliferate in the operational field giving sight to very particular disciplinary, methodological, and ontological mixes. In this section, the authors offer an overview.

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The Spatial Dimension in Social Media Analysis: Theoretical and Methodological Characteristics Ciro Clemente De Falco University of Naples Federico II, Italy Noemi Crescentini University of Naples Federico II, Italy Marco Ferracci University of Naples Federico II, Italy

ABSTRACT In the data revolution era, the availability of “voluntary” and “derived from social media” geographic information allowed the spatial dimension to gain attention in digital and web studies. The purpose of this work is to recognize the impact of this research stream on some methodological and theoretical issues. The first regards “critical algorithm studies” in order to understand what algorithms are used. The second concerns how these works conceive the space. The last two issues concern the disciplinary areas in which these researches take place and which are the ecological units taken into account. The authors answer these questions by analyzing, through a content analysis, the researches extracted with the PRISMA methodology that have used Twitter as a data source. The application of this procedure allows the authors to classify the analysis material, moving simultaneously on the four defined dimensions.

BIG DATA AND NEW WAYS TO STUDY SOCIETY In the digital society (Lupton, 2015), data are constantly produced as direct and indirect effects of bureaucratic, legislative and planning activities, but above all, as a spontaneous accumulation of information derived from the use of social networks used to enhance the exchange of social relations and knowledge DOI: 10.4018/978-1-7998-8473-6.ch029

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 The Spatial Dimension in Social Media Analysis

(Amaturo & Aragona, 2016). This huge amount of data, coming from multiple sources, gave rise to the phenomenon of the data deluge (Halford, 2013), which is commonly known as “Big data”, a term that has been spread increasingly since 2010. As pointed out by Davenport and Bean (2012), big data can be useful in supporting multiple processes (decision- making, prevention and/or safety) using information processed in real time, detecting changes, trends and new directions through continuous monitoring. In research, big data has made it possible to observe human behavior more easily. Indeed, a large amount of previously inaccessible information, is available today to analysis. In particular, big data make it possible to carry out “non-intrusive” investigations in “natural” contexts, avoiding distortion linked to the researcher/interviewer/observer’ s presence (Mahrt & Scharkow, 2013). Thanks to big data, it is easier to conduct research on both hidden and inaccessible populations. In this regard, Kitchin (2014) talks about the data revolution era, a period in which new data and new sources allow researchers to find new ways to study society and its dynamics. In addition to large amounts of data, completely new kinds of data are now available, which can be categorized in different ways: the way they were generated (direct; automated; volunteers; Kitchin, 2014), the area of their origin (public sector, private sector, digital platforms (Elias, 2012), their structuring level (structured; semi-structured and unstructured) and their type of origin (digitized and originally digital) (Rogers,2015). Part of the data is identified, organized and archived thanks to metadata, which can be defined as “data related to data.” Thanks to metadata it is easier to extract useful information from large datasets. Extraction is done through data mining and web scraping tools, which become precious allies for researchers. These are completely new tools that should be part of the technical background of the new generation of social scientists (Lupton, 2015). However, it should be emphasized that accessibility is not guaranteed for all data. For example, the data produced by digital infrastructures, which are very useful for research, are difficult to access due to the growing trend towards “proprietary closure” (Manovic, 2012). Mostly, companies have access to large data sets, while researchers can obtain part of these data thanks to API (Application Programming Interface). APIs are sets of commands used to collect data from large companies’ databases Furthermore, the available data may only be partially useful for answering research questions, and sometimes may even guide the research question (Boyd & Crawford, 2013). On the bright side, new lines of research and new approaches can arise thanks to new data and big data in the social research context, in fact, according to Halford (2013), the connection of these new types of data with other kind of data, allows for better ways of producing sociological knowledge. Among these types of data, the author includes geolocated user data. Some digital platforms, in fact, allow the collection of data relating to the geographical position of users and this has led to the development of an approach which aim is to jointly analyze two worlds that were previously considered irreconcilable: the online and the offline world. Chappell (2017) argues that due to this type of data, innovative methods can be developed to study social phenomena such as social stratification. According to the authors these innovative approaches could help sociology to maintain its distinctive abilities and to avoid the “incoming crisis” (Burrows & Savage, 2007) resulting from the multiplication of “social” data users. The purpose of this work is to critically analyze the emerging field of studies that focuses on the analysis of geolocalized data from social platforms in relation to methodological and theoretical issues. The first is related to “critical algorithm studies” (Seaver, 2016) and basically concerns methodological awareness in the use of algorithms. The second, related to the tradition of ecological studies, concerns the way in which these works conceive space. Is it a geographical space or a “sociological” one? In the end, these two issues concern, on one hand, the disciplinary areas in which these studies take place, and, on the other hand, the ecological units that are taken into account. In order to do this, the work will be organised as following: the second section will introduce and discuss 489

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the new field of research that uses geo-localized data produced by geo-social media. In the third section we will highlight epistemological and methodological issues related to this new line of research. The empirical approach to this work will be found in the fourth section, in which the methodology adopted to answer the research questions will be explained. In particular, the analysis of the articles, extracted with the PRISMA methodology (Moher et. Al, 2018) for systematic reviews, will be carried out through an analysis of the content of the third type (Rositi, 1988). The application of this procedure will make it possible to classify the material of analysis, moving simultaneously on the four defined dimensions, so that the fifth and last section will describe the analysis and interpretation of the results.

TWITTER AND GEOTAGGING DATA SEARCH Several fields of study and research related to social sciences have been interested in the relationship between the individual dimension of interactions and the environment in which they occur. In recent years these studies have been supported by using data available in the digital scenario from the web and, specifically, from social media. They are considered not only as an object of study, but also as a useful source for understanding social phenomena (Rogers, 2016). At the beginning of the 2000s, Social GIS applications for the analysis of actions and habits in the urban environment were the first applications developed to focus on both territorial dimension and digital activities (Zupi, 2017). One example is Foursquare, a social platform through which users could locate themselves in a specific place and they could spread their activities attaching reviews about their experiences. In this way, two types of data integrated with traditional and pre-existing information were created regarding social analysis and urban space: Volunteered Geographic Information (Goodchild, 2007) as well as Geographic Information deriving from Social Media (Campagna et al., 2016). Considering the diffusion use of social media in the cities, the analysis of these data can certainly help emerging research and studies about urbanization. Today so- called geo-spatial data, which can be extracted from social media like Facebook, Twitter or Instagram, can provide a description of dynamic models of urban environments and life with higher spatial and temporal resolutions compared to what was previously possible with conventional data sources (for example census data and field surveys) (Batty, 2013). This allows more complex analysis of data composition, also formed by various languages and contents such as videos, animations, images and other creative elements that produce systematic meanings. Several different geo-tagged contents related to social networks, also called geo- social media, contain both descriptive comments and information about position and therefore, «can support the understanding of the needs of users, public opinion or where it is necessary to develop any resolutions. However, differently from polls and citizen interviews, social media data are forms of direct communication, therefore, these data are unstructured, vary in quality and are often of unidentified relevance for local government needs» (Zhang & Feick, 2016). From careful and sophisticated analysis or geo-localized data it is possible to converge the management of various types of emergencies, environmental monitoring or spatial planning and the understanding of perceptions or users’ needs (such as evaluations of activities, things or characters, levels of well-being or malaise, etc.). In this way it will be possible to hypothesize comparisons between the spatial distribution of perceptions related to a single phenomenon and the geography of the phenomenon itself. Since 2010, Twitter has been identified both qualitatively and quantitatively among the various social platforms as one of the most widely used sources for urban and mobility studies. Geo-localized tweets are useful tools for urban studies allowing space-time tracking / tracing of users (e.g., the times and places that

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concentrate the greatest number of users) (Condeço-Melhorado et al. 2020). Currently most smartphones are GPS-enabled, so information regarding geographical location is often included as an additional tag in tweets. «In combination with temporal annotation, this type of information can be incorporated into tweets to describe where and when they are transmitted. So, Twitter data has become a kind of big timespace data» (Chen et al. 2018). Moreover, the research on Twitter pertinent to public opinion studies has focused on both quantitative and qualitative parameters of democratic ownership of discussions with the participation of users. Research on Twitter has been understood, from the quantitative point of view, as density of discussion (people talk to each other and express opinions), horizontality (everybody talks to everyone) and reaching distant spaces. The research is considered qualitatively by researchers who have analyzed the emotional involvement of users (Papacharissi, 2015) and the polarization of their opinions (Bodrunova et al. 2018). So, Twitter allows anyone who has an appropriate device to create short messages of 140 characters or less, and to publish and share it with other users. This instrument has, in this way, the potential impact extension on many local government organizations including urban planning, police and crime analysis, as well as being a platform of choice for scientific studies interested in the analysis of social mobilizations, broader urban policies and planning applications. It is certainly possible to run some significant analysis on tweets unaccompanied by positioning information and this can happen, for example, through the extraction of keywords or hashtags and using probabilistic models (Zhao et al., 2011) also to understand the possible formation of “networks” through the retweets and obtain information about the location that allows scholars to cover many other lines of investigation (Meyer T et al. 2018). Twitter in fact becomes a useful tool, through applications that benefit from location information, for example, for the detection or monitoring of events (Korkmaz et al.2015), or for the spread of influence (Generous et al.2014). Tweets such as those dealing with fear or insecurity related to a certain phenomenon or a certain historical period, might be restricted to specific neighborhoods where the highest crime data is recorded into, potentially illustrating a connection between actual recorded deviant behaviors and the perception of security. Or again, on the eve of an election, favorable tweets for candidates and/or parties could provide a picture of how the electorate might vote, although this is actually methodologically challenging (Sloan & Morgan, 2015). For this reason, geotagging data is chosen more frequently for the quickest possible dissemination of information across geographic distances and languages and Twitter discussions have different network structure depending on whether a discussion is issue- or event-based. It is an important and a particular scholarly focus in different studies of the social sciences and allows various types of analysis.

ISSUES REGARDING THE NEW APPROACH As stated earlier many researchers are using geo-tweets in their studies on social phenomena. So far the opportunities that researchers can derive from new and big data have been widely debated, but it is also important to highlight the critical issues that concern them. These issues, consequently, also concern the approaches that use them and, in particular, the approach we are talking about. Among the principal issues we can find “epistemological orientation” and “methodological awareness”.

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Epistemological Orientation In reference to the epistemological question, due to the large amount of available data, new forms of empiricism have been born. According to this epistemological orientation “data can speak for themselves” and for this reason the “end of theory” came about (Anderson, 2008). Researchers in the era of data revolution engaged in a debate about the opposition between theory-driven and data-driven research. The data-driven approach does not consider what authors like Lakatos have said about the space between phenomenon and researcher in which some tools (methods and techniques) and ideas (theories and empirical hypotheses) are included (Aragona, 2017). So it is impossible for data to speak from themselves! Hence, from the “ecological” point of view, analyzing geo-tweets without a sociological conception of space is as useful as using a data-driven approach. Since the early stages, space and its characteristics have shown a primary role in the analysis of social phenomena (Porter, 2011). For this reason, it is impossible to define the space in only one way. In this paper we mainly mean, with the term sociological space, the social characteristics of an ecological unit that can influence individual action and social phenomena. It should be emphasized that the “social characteristics” of the ecological unit do not refer to a predefined set of indicators because these vary according to the phenomenon studied (Garner & Raudenbush, 1991). It is possible to trace the beginning of the reflection on sociological space to Durkheim that in Suicide ([1897] 1951) show how features of the social environment can affect individual behavior (McPherson, 2019). The logic behind this conception is that individuals belong to many social contexts that can influence their behavior (Blau, 1960) and some of these social contexts corresponds with the ecologic unit in which individuals acts. Many concepts have been used to describe the effect of social context on individuals: structural effects (Blau, 1960); compositional effects (Davis et al, 1961); contextual effects (Lazarsfeld, 1965) and neighbo(u)rhood effects (Cox, 1972). There are some substantive differences between this concept and one of these differences regarding the type of characteristics of a context to take into consideration. In Blau’s conception only the characteristics with an individual correspondence can be taken into consideration (Micheli, 1977) while for other authors environmental, geographical and institutional ones must also be considered (Galster, 2012). The characteristics of the sociological space initially mentioned is in line with the second conception. By the term “ecological approach” we refer to research that aims to understand the effect on the space’s characteristics on a social phenomenon and individual behavior. The development of this approach has helped to identify the types of ecological analysis (Riley, 1964) the direction of the effects of sociological space on individuals (Miller, 1978) and the casual mechanisms that give substance to these effects (Galster, 2012) the statistical methods to identify and isolate them (Pintaldi, 2000) and the precautions to be taken when moving within this approach (Robinson, 1950). The characteristics of space influence individual actions and to question this means starting from the individualistic assumption, well exemplified in Popper’s philosophy, that society does not exist but that there are only individuals (Gobo & Mauceri, 2014). Thus, starting from the ecological perspective, we can look at the research that uses geo-localized data (where the territory plays a leading role) and ask ourselves what conception of space underlies these studies. The basic idea is that when the sociological characteristics of the space are not taken into consideration, we are faced with a data-driven perspective, otherwise a theory-driven perspective.

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Methodological Awareness As far as “methodological awareness” is concerned, the “hybrids of big data” concept underlines how useful big and new data are, but at the same time, not necessarily representative of the population, or valid and reliable (Lazer, 2014). Indeed, internet and platform users still tend to be composed mainly of more educated, young and fairly wealthy people. This is also true for Twitter (Mellon Prosser, 2017) and it is important consequently to say that geo-tags are not representative of the general population (Malik et. al, 2015; Jiang et. al, 2019; Zhang et. al, 2018). For this reason, the results of the analysis of geo-tweets must be considered cautiously. On the Twitter developer pages, in fact, it is reported that only 1% of Twitter users assigns spatial coordinates to their tweets (so-called geotagging). Besides geotagging there are two other ways to extract spatial information: geocoding and geoparsing. Geocoding is the transformation of a well-formed textual representation of an address into a valid spatial representation, while geoparsing works on unstructured free texts. For each of these three ways of extracting geographic information there are some critical issues related to the way in which algorithms work (Middleton,2018; Kumar, 2019; Ozdikis, 2019; Avvenuti, 2020). That is why the identification of geographic location is one of the most challenging tasks because the location information fields, such as user location and the name of place of tweets are not always reliable. Thus, algorithms that extract and/or elaborate big data cannot be considered as “objective entities”. The perspective that describes the algorithms as entities without social, cultural and economic ties is wrong because the algorithms incorporate the needs and the point of view of their creator This fact is related to critical algorithm studies (Seaver, 2017). «Algorithms are presented as fast, rather than slow; as automated, rather than hands-on; as machinic, rather than human. Each of these presents a series of problems when algorithms move into new domains» (Dourish, 2016:6). Algorithms possess a high degree of opacity with regard to their processes and agency that obscure their social construction process (Burrell, 2016). For instance, Burrell (ibid.) recognized three major factors of opacity: these are due to intentional secrecy according to particular state or corporate aims; technical knowledge and literacy because the “language” of algorithms requires particular skills that the majority of population does not have; finally, they require a degree of understanding of both machine learning and scale by which some algorithms work. As claimed above, algorithms possess different ontologies (Mol, 2002) making them more than they appear, often these are hidden by their aseptic appearance and theoretical independence from the context. Moreover, the “life” of algorithms is always driven by values and aims that must not be left out (Takhteyev, 2012; Devendorf & Goodman 2014). However, algorithms need a further degree of complexity to connect their technical and cultural ontology and to open the black box – their illusory evidence regarding self-referential feature from sociotechnical construction- (Aragona &Felaco 2018) . The raw material used by algorithms are data, or, as in this case, Big Data which add a further degree of complexity and opacity to the process, due to their structure and source. Many scholars have tried to deconstruct the processes of production and enactment of these kinds of data and their relationship with algorithms by pointing out the needing to deconstruct all the passages leading to the final product (Courmont 2021). However, in the eras of data deluge (The Economist 2010) and platform society (van Dijck et al., 2018) understanding socio-technical processes is becoming more complex due to the ability of these entities to obscure their traces and lead the human action. As well-defined by Aragona et al (2020) focusing on both «algorithm in making» and «algorithms in use» is the right way to bring out and shed light on the culture beyond algorithms. According to the authors focusing on all the process of algorithms, life can help to increase the awareness about the fact that algorithms are not self-autonomous per se. To make these new forms of methodological approach, 493

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for instance qualitative methods such as for ethnography, all the processes must be examined closely. In order to increase awareness in using Big Data and algorithms, particularly coming from social networks, some critical points must be noted: Firstly, regarding the algorithms of extraction, the quantity of data collected, thanks to scraping tools, and the source of data actually available might not correspond. This could be due to technical, legal, temporal, or political problems, hence, data exist but they cannot be extracted; Secondly, the representativeness of the population regards both algorithms of extraction and analysis. The former is related to the percentage of the population that really use and produce data. The latter regards the problems of generalizability of result to the whole population. Thirdly, the use of algorithms of analysis implies an awareness of the concerns above, and scholars that approach Big Data analysis have to recognize the different shortcomings of their tools of analysis to better understand ways to adapt data to tools and, in turn, tools to data. Indeed, the construction of a dataset requires different degrees of cleaning and harmonization of data before using it, in particular if the data come from different sources.

DATA AND METHODS In order to answer to our two research questions a literature review was carried out . The review was conducted by three researchers from the University of Naples “Federico II” following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) methodology (Moher et al., 2009). The PRISMA approaches the literature review process through four phases: 1) identification, 2) screening, 3) eligibility, and 4) inclusion. The articles analyzed are those which, after a funnel-shaped selection process, reach the fourth stage. In the first phase the database was built. The database used was Scopus because it provides an excellent coverage for social science research (Burnham, 2006). The key words selected were: “geo” AND “twitter”; “spatial” AND “twitter”. Since it was quite difficult to adapt the guiding concepts for several social platforms, it was decided to focus the analysis on one single social platform. Twitter was chosen because it is a data source often used by researchers (Tufekci, 2014). Twitter, in addition, with the aim of allowing easy data collection, releases more precise metadata than other platforms. This metadata enables more operations (Hipp et. al, 2019). Given the scarcity of results in the Italian language on Scopus and on the other international search engines like Emerarld or Web of Science or national search engines like Torrossa or RivisteWeb, it was decided to analyze only scientific products written in English. As far as this the research area is concerned, only studies in the “social sciences” were selected, and for scientific publications, only full papers were considered. The extraction took place at the beginning of October 2020 and given the recent nature of this approach, no time limit was set for the research. In the second phase, the title and abstract were analyzed, and the articles with the less relevant contributions were removed. In the third phase, after reading them, the articles with contents not in line with the search queries were removed. During this process 261 scientific products were found during the identification phase and after reading the abstract 83 papers were removed because of their low pertinence to research aims (eligibility phase). Finally, according to the application of eligibility criteria, a final set of 95 works (inclusion phase) was analyzed (cfr. fig.1). To answer research questions, content analysis was selected as an adequate technique (Amaturo & Punziano,2013; Krippendorff, 2018). In particular, content analysis was used as an investigation (Rositi, 1988; Losito, 1996). In this type of content analysis, the “object” of investigation is not only the linguistic one but also the extra-linguistic one, considered in its entirety and the unit of analysis is represented by

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Figure 1. The assessment and selection of contributions: PRISMA flow diagram

the entire text (Losito, 1996). The articles were analyzed through an analysis sheet that can be seen as the equivalent of the questionnaire for the individuals. The analysis sheet was divided in four sections: 1) the first contains information about the general characteristics of the article (year of publication, number of citations, authors, affiliation of the authors etc.); 2) the second section contains the abstract and keywords; 3) the third section contains three useful variables for understanding the epistemological orientation of the article and necessary for the comprehension of what kind of relationship exists between the space, the phenomenon investigated through the tweets and potentially any other phenomena. 4) the fourth section contains variables relating to the methodological dimension, in particular the type of

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algorithm used for the extraction and analysis, and the possible critical discussion of the limits related to data validity and data representativeness.

DETECTING APPROACH DIRECTION Geo-twitter and social sciences: characteristics of studies Before analyzing the data to answer our two research questions, a brief overview of the approach that uses geo-localized Tweets as a data source has be given. As seen before, there were 95 scientific articles analyzed. The observation regarding the distribution of number of articles per year makes it clear that the number of articles has increased almost constantly since 2013/2014 (cfr. fig.2). It is conceivable that this growth is due to the fact the access to both IT tools and useful knowledge for extracting and analyzing geo-localized data has become increasingly easier. Figure 2. Number of articles per year

Another interesting fact concerns the information extracted from tweets. Working on Twitter data does not necessarily mean analyzing the content of tweets. Indeed, 44% of the articles analyzed only takes into account the geographic information without considering the content in the text of tweets. The analysis of the user’s position is useful and used to track the users’ movements in the space, to estimate the number of users in a specific ecological unit and to obtain information on the users’ socio-economic status. In some studies,for example, the users’ socio-economic status is inferred from their residence. Thanks to a bibliometric analysis conducted by Vosviewer and graphically returned through the Gephi

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Figure 3. Main themes

open-source software it was possible to observe the themes that emerged from the full papers considered. This network of themes was built through the keywords present in each article considered and were subsequently grouped into 7 clusters that were named by reasoning for congruence thematic The first cluster, called “Health and Food”, includes articles regarding public health, food or nutrition issues. The second cluster, named “Urbanization”, refers to the process of formation and growth of cities and their specific characteristics. This Cluster includes issues regarding studies of urban areas, demography, socioeconomic conditions and livability. Then we find the cluster named “Disaster”. This contains articles that analyze geo-location data during natural disasters such as floods, hurricanes or fires. It focuses on data about emergency management and the consequent resource allocation. The next cluster, “Needs and Services”, includes articles dealing with services, commonly understood as intangible resources, with which specific needs are satisfied. Indeed, emerging themes of these articles are related to urban planning, public spaces, accessibility and innovations in smart cities. Another cluster was defined as “Crime”, contains articles having as main object crime and refers to specific human activities such as the development of sporting events. At the same time, it also contains risk assessment analyses of a population or a specific territory. The “Traceability of activities” cluster includes issues related to the monitoring of mobility or trajectories, monitoring of specific areas such as Community structures, or to the detection of specific communities. Finally, the last cluster “Narrative and Behaviour” focuses on narrations and the behaviours of individuals. Issues such as human activities, timeframes or urban dynamics and also specific areas such as neighborhoods and metropolitan areas can be found in this cluster.

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Table 1. Predictive variables used by Nguyen et. al (2016), Widener et. al (2014) and Gibbos et. al (2017) Nguyen et. al (2016)

Widener et. al (2014)

Gibbos et. al (2017)

Demographics

Demographics

Demographics

Percent 65 years+ Percent 10-24 years Percent male Percent Afro-American Percent Hispanic Household size Economic Disadvantage

Lila tract Low access tract Low income tract Proportion male Proportion black Population (2010) AIC Sentiment

Sentiment (-1 to 1)

Multiethnic Age 18-29 Stability Percent moved in five years Percent homeowner Gentrification Gentrifying Not gentrifiable Recent development Intersection density

Finally, the last point to underline is the type of ecological unit considered: states, regions, cities as well as districts and census sections. The previously mentioned administrative ecological units are used in 62% of the analyzed articles. On the other hand, the remaining 32%, of the ecological units of reference is of a non-administrative type, such as the “cells” of variable diameter or blocks of administrative units.

The Epistemological Orientation In most ecological analyses, the spatial variability of the analyzed phenomenon is explained/interpreted through the socio-economic characteristics of the ecological unit under consideration. For this reason, in order to analyze the conception of space (e.g., geographic or sociological) and the underlying epistemological orientation, a variable was developed to understand whether and how the phenomenon analyzed through geo-tweets was related to the socio-economic characteristics of the chosen ecological unit. After the pre-test phase, four categories of this variable were identified: “same plan”; “explanatory”; “reconstruction” and “descriptive”. The first result is that the first three categories indicate a sociological conception of space. In particular the analysis shows that 43% of the articles indicate an “explanatory” conception of space. These kind of studies analyze how the socio-economic characteristics detected on the ecological unit taken into consideration can influence the phenomenon detected by tweets. Among these types of works we can find the study of Nguyen and colleagues (2016) who explored how the territory’s socio-economic characteristics can influence the happiness level detected by the tweets (Cfr. tab.1). Widener and colleagues (2014) study how this characteristic affects the state of health (as detected by tweets) and, finally, the work of Gibbons et al. (2017) describes the relationship between “gentrification” of the territory and the density of social networks (cfr. tab.1). In the articles, during the pre-test phase, we found several with a slightly different conception of sociological space. These were article which, in the random model, the phenomenon analyzed by the tweets is not considered as a dependent variable. In Cao et. al work (2020), for example, the content of tweets and the socio-economic characteristics of the ecological unit taken into consideration influence the phenomenon analyzed (e.g., problems arising from the use of drugs).

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Table 2. Predictive variable used by Ristea et. al (2020) Crime history variables: Historical crime data before prediction day for the five bins Demographic variables: Popoulation at crime risk; residential population; population white, population black ora African Amerian, population Asian, population 62 years and over, foreign born (%), 25 years and over, 25 years and over less than high school, 25 years and over some college, foreign-born, household with individuals under 18 years, population 18 years and over total, households by type: non-family, households by type: husband-wife family, Bachelor’s or higher studies (%), 25 years and over bachelor’s degree or higher, Hispanic or Latino (of any race), average household size of occupied housing units by tenure: owner occupied, average household size of occupied housing units by tenure: renter-occupied Socio-economic variables: Vacant housing units, homeowner vacancy rate (%), unemployed, households below poverty (%), below the poverty level (%), rental vacancy rate (%), occupied housing units, hardship index, income per capita, price per person for Airbnb, Airbnb locations Environmental variables Restaurants, bars, bus stops, building, bike, racks, transportation routes: density Dynamic variables: Geolocated twitter data for the five ins: density and distance Violent tweets for the five bins: density and distance

Nella research led by Ristea and colleagues (2020), on the other hand, in the prediction model of criminal acts identify the number of tweets and the number of tweets with violent content as independent variable in addition to the socio-economic variables (cfr. tab.2). In these cases, the “socio-economic characteristics of the space” and the phenomena detected through the “tweets” are conceived, in the causal model, on the “same level” as independent variables useful to explain the object under study. This kind of work was classified in the “same plane” concept and accounted for 11% of the articles. The theoretical perspective of this type of article is very interesting per due reasons. First of all, space is characterized by both “socio-economic” and “communicative” aspects. This is a perspective close to both the Durkheimian conception of space and that of authors such as Berelson and Lazarsfled. In the first conception, attention is paid to those characteristics of the ecological unit that can be traced back to the social structure, while in the second, the communicative context plays a fundamental role in explaining the phenomenon under investigation. In the frame of contextual analysis (Allardt, 1968), Lazarsfeld introduced the communicative context in his study on the American presidential elections (1948). The second reason is that, compared to what we saw in the first category the online-offline relationship is partially reversed, in these articles. Indeed the characteristics of the online world can influence what happens in the offline one. Furthermore, it should be emphasized that there is no clear distinction between online and offline worlds, since both are taken into account as independent variables in the explanatory model. Regarding the category of ‘reconstruction’, 22% of the analyzed articles belong to this category. The choice to classify the cases in this category was made because in these studies the phenomenon examined is explained without the use of data or statistical analysis techniques but thanks to the discursive reconstruction of the socio-economic characteristics of the space. One example of this type of conception is the work by Varicelli (2018) who interprets the way in which social classes move within the city starting from the characteristics of the neighborhoods and streets. Compared to the classical approach of quantitative ecological studies, this type of work lacks an upstream operational definition of the socio-economic characteristics of the ecological unit. Lastly, in

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referring to the “descriptive” mode, about 23% of the articles contain this type of conception. In other words, the socio-economic characteristics of space are not considered. However, it should be emphasized that in most cases this is justified by the type of phenomena analyzed (e.g., migratory flows, reactions to disasters) or by the type of research question. In the work of Yin and colleagues (2017), for example, the objective is to build alternative “borders” based on the movements within space of the analyzed subjects, instead of the administrative ones. This kind of purpose can keep the socioeconomic aspect of the territory in the background.

Methodological Awareness The following variables were chosen to shed light on the dynamics regarding the social construction of algorithms. To problematize the presumed objectivity of an algorithm some variables have chosen in this work. The first concerns the data extraction that is composed by three modalities: geotagging, geoparsing and both. We have chosen to close this variable with these categories overlooking the geocode modality due to its different uses and theoretical structure that we observed during the reading of the articles. Many studies combine this technique with the geoparsing or geotagging category, while others use it as an independent mode of extraction. In the light of these differences, we chose to close our variable with the two categories as shown above to avoid reduction in terms of meaning of this modality that shows an interesting fluency, but this requires a separate discussion. As far as the frequency distribution of extraction variable is concerned, the results show a significant prevalence in the use of geotagging (85%) compared to geoparsing (4%) and the combined use these (11%). To better explain the most frequent modality -geotagging- and to examine how the ways of extraction are used -with or without problematization-, two other variables were added. The first regards the kinds of geotagging that is used to locate data in the articles, whether they collect data thanks to key words included in the geo- located tweets; whether they reconstruct the location of tweets thanks to particular triangulation techniques called bounding box, centroids, areas, or triangles; if needed,data collected from secondary analysis. Despite the fact that geotagging generally shows fewer problems in terms of localization of tweets than geoparsing tools, ongoing research is being caried out to develop the accuracy of this way of data collection (Tsou et. Al, 2017). In addition to this, as stated above, the use of geotagging highlights some other specific problems in this process, for instance only 1% of users voluntarily assign location to their tweets involving problems regarding representativeness of population, or rather, frequently, the real location of tweets doesn’t correspond to the location extracted. Thus, the specification variable “kinds of geotagging” allows us to focus on the different ways of the use of geotagging: 58% use bounding box, 29% key words, nearly 1% both and 12% of articles include data from secondary analysis. To answer our main question, whether any kind of problematization was carried out in the use of geospatial big data, other variables were added. The first was conceived as s a dichotomous variable with yes or no categories of answers. We assigned yes to articles discussing the shortcomings, or that dealt with features of these algorithms, on the new ways to use the specific extraction systems, while the no category was assigned to articles that simply describe the process of data extraction and collection without taken into account the aspects of the algorithms themselves. The results show a slight prevalence of yes (55%) over no (45%). The same structure was used to focus on data elaboration and analysis as well. The first variable concerns the type of data analysis, and the classes of response are: specification of algorithm used in the data analysis, or data elaboration when the authors don’t use any algorithms but statistical analysis. As for the extraction block, a problematization variable was added to detect whether the use of these algorithms

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was problematized or not by users. This was also conceived as a dichotomic yes or no variable. 51% of analysis was carried out thanks to algorithms while the remaining 49% shows statistical elaboration of data. An uncritical use of the algorithms employed for the analysis of data was observed in 61% of the cases compared to 39% regarding the critical uses. This block of variables allows us to point out whether not- linear trajectories were used by scholars in their analysis and, therefore, the cultural construction of data. More work was done on the extraction part by authors in terms of problematization, while less attention was paid to the algorithms used for the analysis as well as described by the problematization variables. This difference could be explained thanks to the need of users to examine the ways of extraction as new techniques showing broad margins of improvement. The use of big data has started to be available in recent times, while less attention was paid on the problematization of algorithms of analysis, which could be explained because these tools are felt to be more reliable than the extraction systems due to the broad literature about them. However, despite little difference in terms of percentage between the two types of problematization, according to the time of publication variable, an unvaried trend during all the time frame could be seen. In general, the results show little attention regarding the use of algorithms in developing research employing geospatial data. In terms of the critical approach towards algorithms introduced by Gillespie and Seaver (Gillespie & Seaver, 2016) the internal features of these entities are perceived to be more autonomous than what we expected from the cultural influences, therefore, they are simply enabled as tools with a high level of self-sufficiency, thanks to their perceived objectivity, without particular needs of contextualization in a particular cultural frame.

CONCLUSION In this paper we have presented an analysis that aims to shed light on both the conception of space that emerges as sociological or geographical from the articles and, at the same time, on whether the methodological process that scholars use during their analysis show some kind of critical thinking or simply carry out uncritical procedures. To achieve these objectives, we chose to employ a content analysis, in particular an investigation type of content analysis following Rositi’s categorization (Rositi 1988), using an analysis sheet on a sample of articles extracted from Scopus database. The review was conducted thanks to a PRISMA schema that shows all the steps of selection we faced during the analysis of the articles. The temporal distribution of the articles analyzed shows a growing interest in this approach. There were many topics analyzed and various types of ecological units were taken into account, administrative and non-administrative, from the largest to the smallest: countries, administrations, cities, neighborhoods or specific geographically delimited places of interest. It should be noted that a substantial percentage of the articles excluded during the early stages of the review had a methodological perspective. This indicates that the debate on the technical instrumentation to use geolocated data in better way is still ongoing and therefore many tools and techniques will be available to researchers in the near future. A clear prevalence of geotagging is the most widely followed way to conduct geo-spatial tweets analysis. Despite the well-known problems related to the small amount of data available with volunteered location, and the representativeness of these data in terms of population and reliability of their position. In addition, evidence concerns the higher problematization of extraction compared to elaboration systems. This could be explained because of geotagging problems. In fact, despite small levels of problematization to both the variables, the extraction algorithms are problematized as much as the elaboration ones. As far as our research question regarding methodological awareness is concerned, our results show that almost a

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half the articles do not pay attention to these issues both for extractions and elaboration algorithms. This means scholars use algorithms partially without taken into account their opacity (Burrell, 2016). Algorithms possess different degrees of complexity in their building and their use. However, most of authors of our sample were not interested in closely examining how the extraction or analysis algorithms are built or the values that those tools convey (Gillespie, Seaver, 2016). Many cases show choices regarding the fact that algorithms of extraction were not problematized, because references about the motivation of the processes of choice in the articles cannot be found. This trend regarding algorithms of analysis is on the rise. However, the meaning of these claims should not be interpreted as authors not knowing the different socio-technical features that constitute the black box (Aragona, Felaco, 2018) of algorithms. However, in this article the cultural dimension is at least as important as the results of research itself . Without these points of reference they express, in a negative way, their uncritical approach to the question. In addition to the theoretical issues many studies do not refer to the traditional problems regarding the use of both big data and algorithms: representativeness of information, the shortage in data availability, the problems of their tools of work and analysis. This second point, like the former, states the procedural frame of reference which is closely related to theoretical awareness because data “speaks for itself” and they do not need a further degree of problematization. Here, too, these problems involve the article contents. If specific reference cannot be found in the articles, this would express the poor interest in studying how the results were built. This claim is more frequent in algorithms of extraction than in analysis ones. An interesting proposal for future research could be to study the relationship between the words included in the articles and the use of authors regarding the algorithms and their cultural features. As far as space is concerned, and initial result is that the theory-driven perspective prevails, which is why in most articles a sociological conception was detected while only a residual number of articles seem to be characterized by a data-driven perspective. A second interesting result regards the differences detected within the three different sociological conceptions of space that emerged in the analysis. In the “explanation” category we find articles with a conception of sociological space close to the concept of social structure while in the articles classified as “same plan” the communicative elements in line with the American tradition are also taken into consideration. In these articles the online-offline dichotomy fades away because both realities are considered as independent variables in the analysis models. Another difference within the three conceptions is related to the way in which the characteristics of space are operationalized. In the articles classified as “same plane” and “explanatory” there is an accurate empirical translation of the socio-economic characteristics of the space, while in the articles classified as “reconstruction” the characteristics of the space are often used as a secondary element. The last result is interesting because it suggests a path for further study. In this study the sociological conception of space was made equivalent to a theory-driven approach. However, the last category demonstrates how the use of the characteristics of space does not necessarily follow the path indicated by the theory or methodology developed within the ecological approach. Thus, if in this first review all the articles that in the explanation of the investigated phenomenon contained at least one reference to the socio-economic characteristics of space was classified as theory-driven, in future research, only those articles that clearly define the socio-economic characteristics of the ecological unit under consideration and clarify how these could be related to the phenomenon studied could be classified as theory driven. All ecological approaches have their own value (Menzel,1950) but only some of these allow us to reach conclusions concerning the individual plan (Robinson, 1950), and this is an aspect to be taken into consideration in future studies. In other words, in this review the studies that took into consideration the sociological

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characteristics of space were considered as theory-driven, while in future studies only those that are in line with the ecological approach might be considered as such.

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Chapter 30

Using Twitter to Form Professional Learning Communities:

An Analysis of Georgia K-12 School Personnel Discussing Educational Technology on Twitter Mete Akcaoglu Georgia Southern University, USA Charles B. Hodges https://orcid.org/0000-0003-3918-9261 Georgia Southern University, USA Lucas John Jensen Georgia Southern University, USA

ABSTRACT Social media has become an important tool for informal teacher professional development. Although there is a growing body of research investigating issues across the US, there is a lack of research on teacher professional development taking place on Twitter in Georgia, USA. In this research, the authors applied digital methods to analyze 5,425 entries from educators participating in a state-level, weekly, synchronous chat about educational technology (#TECHTalkGA) on the social media platform Twitter. Findings include that participants utilized the chat for organization, planning, and classroom technologies, with a predilection toward specific hardware and software topics. Limitations and implications for future research are discussed.

DOI: 10.4018/978-1-7998-8473-6.ch030

This chapter published as an Open Access Chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/) which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited.

 Using Twitter to Form Professional Learning Communities

INTRODUCTION Teachers are required to maintain their professional knowledge, and the process of maintaining current professional knowledge often is described as professional development. Professional development can be categorized as formal or informal (Hodges, 2015). Formal professional development often is experienced in traditional formats such as face-to-face workshops, conference sessions, or webinars led by an expert or experts. Informal professional development may take many forms, but increasingly online social networking tools are utilized. Professional development was the most common educational purpose for social networking identified in the reviewed literature (Galvin & Greenhow, 2020, p. 21). In addition to formal forms of teacher professional development, teachers have accepted informal professional development experiences such as EdCamp meetings or online professional learning networks (e.g. Carpenter, 2014; Trust et al., 2014). One platform for informal online professional development has been through communicating (e.g., sharing resources) through social media (Rosenberg et al., 2016; Greenhalgh et al., 2020). These informal professional development experiences are typically not led by a single expert, but are led by teachers, for teachers. The focus of this paper is a specific use of the free-to-access online service Twitter (http://www.twitter.com) as a professional learning network by education professionals. .

BACKGROUND Online Education and Online Professional Learning In the last two decades, the mainstream growth of the Internet has led to transformative change in education, particularly higher education, as the Internet has provided new opportunities for online and distance learning (Allen & Seaman, 2010; Shea & Bidjerano, 2010). Commensurate with this change has been a rise in the sphere of online activity known as social media – networks of users tied together via Web 2.0-based applications that offer individuals an opportunity to generate and share content of their own (Kaplan & Haenlein, 2010). Noting that the term social media is hard to define in a world where almost all technologies feature a social component, Kaplan and Haelein (2010) defined social media as “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow creation and exchange of user generated content” (p. 61). Examples of social media are large social networks like Twitter, Facebook, Tumblr, or sites like Instagram and YouTube, which focus on one type of media. Online education has been steadily growing, and as of 2008, nearly 4.6 million students were enrolled in some form of online education (Gabriel, 2010). Recent economic downturns have driven millions of people to look online for new learning opportunities and careers. Between 2007 and 2010, online education enrollment increased 25% (Allen & Seaman, 2010; Shea & Bidjerano, 2010). Universities – whether public, private, or for profit – are increasingly pushing online education as part of their curricula (Gabriel, 2010). In some cases, online education is considered necessary for these institutions’ long-term survival (Gabriel, 2010; Kaya, 2010). As universities and other institutions of higher education move to implement more online education, they also struggle with the quality of the education (Kaya, 2010). Lack of engagement and motivation is seen as one of the central problems in the current landscape of online education. Online education – sometimes known as e-learning – offers significant advantages in 511

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flexibility, individuality of instruction, and fewer geographic and temporal limitations; it has also been shown to have significant drawbacks, such as student isolation, the need for tutors, and lack of participation (Wu, Tennyson, & Hsia, 2010; Wu, Tennyson, Hsia, & Liao, 2008; Yang & Liu, 2007). Research has shown that online learning can be a disengaging experience (Barbour & Plough, 2009; Palloff & Pratt, 2007). The flexibility and convenience of taking a class online is an enticing prospect to many students, but staying engaged in an online course, whether synchronous or asynchronous, requires a high level of motivation (Barbour & Plough, 2009).

Online Communication, Social Media, and Personal Learning Environments The pedagogical concept of Personal Learning Environments (PLEs) challenges the dominance of learning management system (LMS) usage in online courses, bringing a student-centered and bottom-up perspective to online education. The concept of PLEs is relatively recent and, as such, no central, agreed-upon definition of a PLE exists, though some key common elements appear in the literature. McElvaney and Berge (2010) defined a PLE as “the sum of websites and technologies that an individual makes use of to learn” (para. 18). This definition is a broad, inclusive one, but it is an acknowledgment that online learning can, and does, take place in spheres beyond the typical LMS-housed world of online education. To be sure, LMSs are included in this definition, but so are social media like Facebook or Twitter, blogs, video sharing sites, and informational clearinghouses like Wikipedia, to name a few. In much the same manner as students learn outside of the face-to-face classroom context, so, too, do online students. Online students might learn from articles posted by friends on Facebook, have heated debates replying to posts using a political Twitter hashtag, or share home renovation ideas on Pinterest. These are all learning activities, however removed they are from the formality of an online classroom. In a sense, all people engaged in online activity have their own personal online environment made up of the websites they frequent, including social media sites that employ hashtags. The thinking behind PLEs has been that instruction should be situated in an environment more congruent with the learners’ typical technology usage for their personal lives (Attwell, 2007). In a PLE, each online learner may create her and his own personal learning environment and can choose to engage with the course materials and assignments using the tools more closely aligned with each learner’s nonacademic life (Sclater, 2008; Van Harmelen, 2008), including social media like Facebook, Instagram, Twitter, and Pinterest. In this type of learning environment, students have more choice about how they engage with the material and can use tools with which they are more comfortable. Students rarely encounter environments like LMSs in their personal online lives, yet many of them spend time traversing the Internet, tweeting, social networking, posting photos to Instagram, putting videos on YouTube and TikTok, and engaging in online discussions. These active online personal lives stand in contrast to the lesser-motivated online educational lives of students. The PLE concept could be a benefit to teachers and students alike, as it allows, almost mandatorily, for differentiated teaching as it moves between online platforms. Students receive more variety in their instruction and teachers have more control over which technologies, tools, and platforms they use to create and deploy the content. As Mott (2010) describes, an ideal PLE features student choice, meaning that they can select tools that most match their needs and interests. However, this approach might be more fractious and decentralized than an LMS, with content and communication possibly happening in multiple places. This might prove confusing and complicated for inexperienced faculty and students, and there could also be privacy and FERPA issues for the use of some of the tools (Mott, 2010). It could 512

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also be time-consuming and costly to maintain the disparate tools needed to teach a course, and the data is not always available or connects with institutional systems like online grade books and class roles (Mott, 2010). Technical support is also split up between tools and platforms, meaning little control over outages and technical issues and splintered support apparatus between providers (Mott, 2010). PLEs support modular, student-centered, constructivist approaches that offer teachers and students the choice to engage with content and course technology in a manner that more meets their needs and interests, but this combination of creative possibility for teachers means multiple tools and non-standardized technology usage from student to student. This could be highly rewarding and offer lots of pedagogical possibilities, but it might require extra time and effort. To understand the concept of the PLE and the contrast between personal and academic online activity, it is important to also understand the development of the Web 2.0 paradigm. Web 2.0 has been a philosophical shift as much as a technological one, representing a change in how people create and share content on the Internet, moving from developer-generated to user-generated and shared content (Cormode & Krishnamurthy, 2008; Greenhow, Robelia, & Hughes, 2009; Ravenscroft, 2009). In the early Internet days of the 1990s, retroactively known as the era of “Web 1.0,” content was delivered in a top-down manner by a limited number of content providers (Cormode & Krishnamurthy, 2008; Greenhow et al., 2009). Although Web 1.0 was touted as being “interactive,” beyond its e-commerce role, it functioned as little more than a repository of knowledge, akin to an encyclopedia or dictionary, or a series of news articles generated by the major media (Cormode & Krishnamurthy, 2008; Greenhow et al., 2009). This version of the Internet mirrored traditional educational practices, expert-driven and top-down, with users functioning as passive receptors of information (Dede, 2008; Greenhow et al., 2009). Most usergenerated content was relegated to communication-based communities, like message boards, chat rooms, and bulletin boards, and posting content online required knowledge of programming hypertext markup language, otherwise known as HTML (Greenhow et al., 2009). In the late 1990s, and even more so in the early 2000s, the top-down paradigm of content generation started to shift toward the user, as new tools – dubbed Web 2.0 – helped users generate and post content to the Web themselves (Greenhow et al., 2009). Websites and tools like blogs, YouTube, wikis, Twitter, and social networking sites all accurately represent the movement in user-generated content that characterizes Web 2.0.

What is Twitter? Twitter is an online social media platform that allows users to communicate publicly. Twitter can be accessed with a variety of digital devices through web browsers or dedicated apps. The unique structure of Twitter allows for synchronous or asynchronous conversations on various topics from news, culture to education (Greenhalgh, et al., 2020). Communication with Twitter centers around sending out tweets to one’s followers and receiving tweets from the individuals (or entities) they follow in their timeline. A tweet is a message consisting of text (up to 280 characters) and media files such as images, animations, or videos can be attached with each message. It is common to identify tweets with special keywords called hashtags. For example, a hashtag identifying a tweet as relevant to teachers would be “#teachers.” Galvin and Greenhow (2020) noted that the use of hashtags and synchronous chats have been repeatedly found to be important to teachers, and that Twitter has been found to be the most popular social networking service for K-12 teachers. Use of hashtags has been found to be similar to “affinity spaces” (Greenhalgh & Koehler, 2017), which helps organize the conversation and context of the discussions.

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How is Twitter Used by Educators? Twitter is utilized for various purposes in education. Teachers find it to be an efficient, accessible, and interactive tool for staying on top of novel ideas, education advances, trends, and educational technology (Carpenter & Krutka, 2015). New teachers have used it to form mentoring networks (Risser, 2013). It has been integrated into the learning process to enhance the linguistic competence of secondary school students (i.e. Cano, 2012), and it has been embraced by educators as a way to form professional learning networks (Carpenter & Krutka, 2014; Coleman et al., 2018; Davis, 2015). In an analysis of several thousand tweets from teachers, Fischer et al. (2019) observed that “Twitter reflects aspects of high-quality professional development” (para. 1). Educators use Twitter to share information and resources with colleagues all around the world, thus possibly alleviating feelings of isolation experienced by some teachers (Carpenter & Krutka, 2014; Trust et al., 2016; Tucker, 2019). It also has been used as just-in-time teacher professional development in response to (tragic) current events (Greenhalgh & Koehler, 2017), and as a way to connect teachers during pandemic-related school closings (Hogan, 2020). Researchers have noted that state-level Twitter chats involving educators are thriving, but also that there is more to be known about these chats (Greenhalgh, 2020; Rosenberg et al., 2016). The #TechTalkGA hashtag is an informal hashtag created by educators who work and live in Georgia, USA to share resources and thoughts about the use of educational technologies. It was formed by educators who are interested in using technology in their classrooms, professionals who work in school libraries, and other district professionals who are involved and interested in increasing the effective usage of technology in schools. Due to its informal and voluntary nature, information regarding its founders, founding date, or other elements that defines its structure does not exist by design (Rosenberg et al., 2016). In fact, the non-existence of this information is what defines these spaces: they are shared and not owned.

Theoretical Framework To structure our investigation, we used the Enriching Professional Learning Networks (EPLN) framework introduced by Krutka, Carpenter, and Trust (2017). The EPLN framework describes a teacher professional learning network as consisting of people, spaces, and tools. Within EPLN, questions are asked such as: Who are the people in the PLN?, Which spaces are conducive to professional growth?, and What tools are acquired by participating in the PLN? Applying EPLN as our lens for this investigation provides a structure for our two research questions.

The Present Study To address the need to know more about state-level Twitter chats, the focus of the present research is examining K12 educators’ use of the Twitter hashtag, #TECHtalkGA, the Georgia-centered Twitter professional learning network with a specific focus on using technology for teaching and learning. #TECHtalkGA functions as both a weekly online chat and an asynchronous portal for educators in Georgia focusing on topics related to technology in education. Our purpose in this study was to examine this network for Georgia educators to fill the gap in the literature about state-level Twitter chats and investigate the nature of the conversation in this hashtag. Two research questions guided our examination of the hashtag data: 1. What is the nature of the activity of the Twitter discussions using the #TECHtalkGA hashtag? 514

 Using Twitter to Form Professional Learning Communities

a. How is #TechTalkGA used across time? b. What types of Twitter interaction types were present across time? 2. What is the nature of the content discussed using the Twitter hashtag #TECHtalkGA?

Methods Digital methods were utilized for the present research. Rosenberg et al. (2016) note that “digital methods are new research techniques that have been built around the collection and analysis of data coming from Twitter and similar sources” (p. 27). While we have applied newer tools and techniques to our data, the use of public data mining has been “an emerging research method for the past two decades” (Kimmons & Veletsianos, 2018, p. 492) and other researchers have established the appropriateness and utility of automated digital methods for quantitative content analysis (e.g. Greenhalgh, 2020). Digital methods may include setting up automations to collect and store publicly available data in Google Spreadsheets. For example, in this study, by using a script we were able to collect a large amount of public data (Tweets) in a Google Spreadsheet and analyze them using computational tools. This exploratory data analysis is “a different approach to analysis that can generate valuable information and provide ideas for further investigation” (Pertl & Hevey, 2010, p.456).

Data Sources and Collection From January 2019 to April 2020, by using a publicly available script, Twitter Archiving Google Sheet (TAGS; Hawksey, 2014), we collected and stored a total of 5425 tweets by automatically searching for the hashtag #TechTALKGA at every hour and save them to a Google Sheet. Greenhow, Galvin, and Staudt Willet (2019) note that studying these tweets as “digital traces” (p. 181) allows researchers “opportunities to conduct authentic and useful research of social media behavior, in the contexts where it actually occurs, to better inform practice and policy” (p. 182). Once the tweets were stored in the spreadsheet, it was exported as a data frame to be analyzed using quantitative and computational qualitative methods. We did not find unwanted tweets to be a problem in the data we collected, but data sets should be examined for possible spam entries (Carpenter, Willet, Koehler, & Greenhalgh, 2019). Using the tidytags R package (Staudt Willet & Rosenberg, 2021) we were able to remove deleted or private tweets.

Data Analysis and Procedures To prepare the data for analysis, we followed the following steps. First, using tidytags R package (Staudt Willet & Rosenberg, 2021), we cleaned the data off of the protected and deleted tweets. Although tidytags can pull numerous variables for each tweet (e.g., location, urls, etc.), due to the nature of our research questions we were only interested in time, interaction, and tweet text data. Using the built-in functions inside tidytags, we created an edgelist to identify the usage types (e.g., retweet, quote tweet, etc.). This allowed us to perform analyses on the tweet data regarding the usage frequency and types that were investigated the first research question. In addition to providing basic descriptive statistics, we also analyzed the content of the Tweets using the Quanteda package in R (Benoit, et al., 2018). Quanteda allows researchers to analyze the content of the Tweets using a corpus-centered approach to understand overall trends. Corpus here refers to the 515

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text, in other words the content of each tweet. By creating a corpus from the tweets, we created a data that is composed of each word of each tweet. Using this corpus data, we were able to clean up the words that would not contribute to the findings (e.g., http, #TechTalkGA, personal pronouns, etc.) or were a regular part of the tweets sent to this hashtag (e.g., “A1” for answering tweets to questions asked during weekly chats). In order to understand the content discussed in #TECHtalkGA, our initial analysis was to take a look at the most frequently used words in the hashtag. To prepare the data for the corpus analyses, first we converted the tweet text data into Quanteda corpus. Using the built-in functions in the package we were able to conduct “dictionary analysis, exploring texts using keywords-in-context, computing document and feature similarities, and discovering multi-word expressions through collocation scoring” (para. 1). After removal of the stopwords, and creation of the corpus text data, we analyzed both the nature of activity (using regular statistics), and also explored the frequency of certain keywords, as well as how the content of the tweets were related to each other (i.e., textplot networks). To prepare the figures, we used ggplot2 package available in tidyverse (Wickham et al., 2019).

Findings The findings are organized by research questions.

Research Question 1: What is the nature of the activity of the Twitter discussions using the #TECHtalkGA hashtag? First, we examined the activity on #TechTalkGA over time as seen in Figure 1. The data showed that from January 2019 to April 2020 the highest levels of activity (i.e., number of Tweets) for #TECHtalkGA occurred mostly on synchronous chat nights (Monday evenings), when the regularly scheduled chat occurred. During this period, users frequently tagged (i.e., @ mentions, n = 7346), retweeted (n = 1152), sent direct responses to each other (n = 943), and quoted other tweets (n = 672), as seen in Figure 2. Periods of relatively low activity are during winter and summer breaks. Next, we analyzed the dataset to determine the most active participants in terms of the number of tweets they sent and received. We found that participation was skewed: only four users had been power users of the hashtag, accounting for almost half the number of tweets (n = 2401). The most sought out member was the organizer of the weekly Twitter chats associated with the hashtag. The top sender list was highly similar to the top receiver list.

Research Question 2: What is the content discussed using the Twitter hashtag #TECHtalkGA? Our analysis revealed that there were a total of 5399 unique words identified, after grouping similar words by the first four letters. The most frequently used word was “tech,” (n = 940) being followed by “teac,” (n = 911) and “lear” (n = 688). Using these letter combinations allowed us to capture various forms of words. For example, using “teac” captures words like “teach”, “teaching”, and “teacher”. As can be seen in Figure 3, the most frequently used words reflect the main components of educational technology: learning, teachers, students, time, school. The frequency of words technology, edtech (n = 518), and google (n = 422), indicate that technology was a central focus of the PLN (34%). It also is 516

 Using Twitter to Form Professional Learning Communities

Figure 1. Hashtag Activity January 2019 - April 2020*

*Note. Green points are days with more than the number of average Tweets.

Figure 2. Interaction Types Across the Timeline

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Figure 3. Word Frequency January 2019 - April 2020

evident that the hashtag served some social functions, as indicated by the frequency of the words “share” and “friend” in the most frequently used words. As it can be seen in Table 1, friend and share were usually used in the context of inviting others to the weekly chats or introducing themselves during these chats. The words school, edtech, student, teach, learn, and tech were contextualized within educational technology where the participants talked about the use of technology for teaching and learning purposes. Google was used to talk about Google-specific tools and apps, and indicates the popularity of Google tools in schools. In addition to populating the most frequently used topics, we ran a follow-up analysis to see how much the users talked about some of the more technology integration related topics in comparison to topics that relate to the overall design and integration of technology-rich learning. The International Society for Technology in Education (ISTE) publishes various, well-known and accepted standards related teaching and learning with technology. So, we chose some keywords from the ISTE Standards for Educators as well as other more technology-centered words that were frequently used in our corpus: “empow”, “construct”, “innovat”, “design”, “computational”, “think”, “creative”, “collabor”,”technology”, “google”, “tool”, “digital”. We observed that most words in our ISTE vocabulary did not frequently occur in tweets tagged with the hashtag of focus, especially some of the more specific words such as “computational” or “constructionism” (Figure 4). In contrast, the technocentric words relating to specific software or hardware more frequently occurred in the corpus. Finally, to get a sense of the conversation involving the hashtag, above and beyond stand-alone words, we analyzed the context in which the words co-occurred. This analysis gave us a sense of which words were used in the context of the others, enabling us to make some conjectures about the content of the conversation. Using the feature co-occurrence matrix (FCM) created through Quanteda, we created a text network plot. First, using the topfeatures function, we selected the most frequently used 30 words. Next, we created a network plot depicting the frequency of occurrence and co-occurrence (the minimum

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Table 1. Sample Tweets for the most frequent keywords in TechTalkGA tweets Keyword

Tweet

friend

Get ready for tonight’s #TECHtalkGA at 9pm (EST), sharing our best WFH tips! Remember to invite a friend!

google

#TECHtalkGA A4: Most recently, I’ve been “teaching” more & more ppl about Google Meet, Microsoft Teams, Flipgrid, and Screencastify!

share

Q1: Welcome to #TECHtalkGA! Share your name/dist/role and where you were in 2009!

school

#TECHtalkGA A3: I am so incredibly proud to work in @FCSchoolsGA! We have some of the best Ts ever, who truly care about Ss; who are working harder than ever to design “qual learning exps” for Ss from afar with ZERO prep! They are amazing!! Never been prouder!

edtech

#TECHtalkGA Q2: What’s the number one thing you think Ts need to understand about using #edtech in the classroom?

student

#techtalkga A4: I’ve done the flipped classroom several years, and I’m often asked to speak about it. Just like other tech, a flipped classroom won’t fix bad teaching. But, it can be a technique that can turn class time a more studentcentered environment.

time

You guys! #GaETC19 is THIS WEEK! I’m so pumped! If you are attending, be sure to join in the #TECHtalkGA chat tonight at 9pm to talk about how to get the most out of our time at the conference!

learn

#TECHtalkGA A4: Some the recommendations from the authors were things like “keep your lessons engaging” That’s easy to say, but difficult to do if you haven’t seen it WITH TECH. Be purposeful about how Ss use it. Teach them how to use it to learn. Model it for learning.

teach(er)

#TECHtalkGA returns to our normal format tonight focusing on how we support students, teachers, and each other! Come share; join the convo at 9pm!

tech(nology)

@iste Regional chat: #TECHtalkGA on Mondays at 9pm! For #ETCoaches, MSs, and anyone passionate about technology in education! All are welcome, even if you’re not in GA!

threshold was set to .85). In our network plot, the size of the words and the links between them represent their proportional frequency. In other words, if a word occurs more frequently or two words appear in the same context more frequently their text size and link width would be proportionally bigger. As seen in Figure 5, we were able to group the discourse into three categories: planning, organization, and classroom technology. Organization included conversations such as organizing the synchronous chat hour the next week, while planning seems to focus on planning to attend an upcoming conference (GAETC19), and classroom technology included using technology to teach and learn.

DISCUSSION With respect to the EPLN framework, our investigation of the data related to research question 1 revealed answers to the questions, Who are the people in the PLN?, and Which spaces are conducive to professional growth? First, it seems like the answer to the who question is that there were relatively few people active in the discussion and the most involved member was the organizer of the discussion. As for spaces conducive to professional growth, it must be noted that the space consists of the online discussion utilizing the #TECHtalkGA hashtag, but it may be considered in two parts: the synchronous chat held on Monday evenings, and the use of the hashtag asynchronously at other times of the week. For maximum engagement with the other participants in the PLN, participants should plan to join the chat during its synchronous portion on Monday nights.

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Figure 4. Frequency of Combined Keywords in #TECHtalkGA

Figure 5. Co-occurrence Matrix

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Our observation of seeing a few participants dominating the #TECHtalkGA discussions is consistent with the “low active participation, yet high professional value seems to define the typical Twitter PD experience” observed by (Galvin & Greenhow, 2020, p. 18). We observed in particular that the use of the hashtag was mostly during the typically scheduled Monday night synchronous chats and that there were comparatively lower levels of activity with the hashtag outside of the synchronous chats and on breaks in the school year (i.e. summer). Based on this finding, we can argue that teachers use the hashtag during the school year, and tend to engage more during times that allow for more interaction. As identified by Greenhalgh et al. (2020), there is a difference between the participation during chat and nonchat times in terms of tweets’ content (e.g., more work-related discussions during non-chat contexts). The chat and the synchronous communication seem to serve social purposes more, but attract a small number of participants. In the weekly non-chat contexts we observed that there was a smaller group of individuals who dominated the communication in terms of contributing original content, and active participation in the discussion consisted mostly of retweets or mentioning another user than replies or quoting tweets. This conforms with Greenhalgh et al.’s (2020) findings in that non-chat use of the Twitter hashtags for informal PLN’s tend to lend themselves to more passive participation, as well as acts that aim to share and disseminate content. The acts of retweeting and mentioning require less cognitive involvement with the conversation than replying and tweeting, which may explain the frequency of the types of interactions that we observed. The results may also indicate that the majority of users may view the hashtag as a source of information to be read or quickly shared more than a community for active discussion. With respect to the EPLN framework, our investigation of the data related to research question 2 revealed answers to the question, What tools are acquired by participating in the PLN? Our analysis of the content of the conversation showed that the hashtag was centered on technology hardware and software and associated planning and organization for hardware and software, and much less so on concepts related to leveraging the power of technology for student learning. That is, the conversation was most often focused on specific technology products (e.g. Chromebooks or Google tools) instead of established models of technology integration. Still, the observed conversations are clearly within the categories of teacher professional learning observed by Greenhow, Galvin and Staudt Willet (2019) of resource exchange, community building, and individualized needs. This suggest that #TechTalkGA as a PLN platform serves its purpose by allowing its participants to share and disseminate knowledge. The content of the conversation reflects the issues that immediately the teachers have to tackle more than issues that require more deliberation and longer-term planning. Ideally topics regarding issues in educational technology, which would be a focus on concepts of student empowerment, design, creativity, etc., would be covered, more, but our data is valuable in that it reflects what the participating teachers worry about in their day to day teaching. Veletsianos (2017) notes that “the hashtag becomes a tool to serve the needs of its users” (p. 290), and in this case #TechTalkGA’s users’ needs seem to focus on more technology-use related and practical issues. Although it is impossible for the authors to gain deep understanding of PLNs mutual engagement, joint enterprise, and shared repertoire (Wenger, 1998) using the digital methods gave us a powerful new way to understand large volumes of conversation happening very quickly and efficiently. Such approaches show promise and will give researchers more analysis power as the software is developed further, especially given the fact that these are open-source and free tools.

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LIMITATIONS This research is not without a few limitations. The major limitations are related to the research methods used. First, it is impossible with the quantitative methods used to know the intentions and desires of the PLN members regarding what they perceive as the purpose of the PLN. The author’s interpretations of the PLN discussion were based on their own notions of what the participants should be discussing. Interviewing or survey some participants may negate some of those notions, leading to different interpretations. Also, it is common in Twitter chats to comment and reply with non-text content like memes, videos, etc. These elements were present in the #TECHtalkGA tweets but could not be analyzed with our chosen digital methods, which analyzed text only. The lack of ability to analyze the non-text responses has been noted in previous literature (i.e. Greenhow, Galvin, & Staudt Willet, 2019). While these are viewed as limitations, the chosen methods allowed us to perform analysis on a large dataset that would be practically impossible to analyze in a timely or reliable manner using more traditional approaches to analysis.

CONCLUSIONS AND FUTURE RESEARCH This paper provides an example of new methods to analyze state-level teacher professional development conversations using digital traces as data and digital methods, gaining some insights into what teachers are discussing and the terminology they are using. All cited resources in this article and software used are open-source and have user-guides as well as well-documented examples. We hope that these methods could easily be applied to other hashtags for other communities of educators. Based on the observations made in the present paper one can determine that at the state-level, teachers will participate in a synchronous Twitter chat organized by a particular hashtag and that they will share information on professionally relevant ideas such as planning, organization, and classroom technology. As Greenhalgh (2020) notes, regional educational Twitter hashtag spaces “are defined by different practices, different social dynamics, and presumably different goals” (p. 18). Depending on the participating teachers’ needs and motivations, they may find participation in this or similar discussions to be what Krutka, Carpenter, and Trust (2017) describe with their framework for enriching professional learning networks. Some questions for future research studies worth pursuing would include: What are researchers and teacher educators to do with the observations from the present study? Is it appropriate for them to join this, or similar PLNs, and attempt to drive the conversation to issues they may view as more important? Should teacher professional learning or graduate degrees for teachers use the findings to revise their preparation programs either to focus more on what in-service teachers are discussing, or to determine what concepts are not being discussed and to enhance teacher preparation curriculum in those areas? Would it be beneficial to encourage pre-service teachers to participate in the PLN as a form of informal mentoring and inclusion in genuine conversations as described in Sheridan and Young (2016)? And finally, it would be worthwhile to investigate the teachers’ use of Twitter as a PLN during the school closings of the COVID pandemic of 2019, if any, and if they did in what ways they found use for it (socially and academically).

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REFERENCES Benoit, Watanabe, Wang, Nulty, Obeng, Müller, & Matsuo. (2018). quanteda: An R package for the quantitative analysis of textual data. Journal of Open Source Software, 3(30), 774. doi:10.21105/joss.00774 Cano, E. V. (2012). Mobile learning with Twitter to improve linguistic competence at secondary schools. The New Educational Review, 29(3), 134–147. http://www.educationalrev.us.edu.pl/dok/volumes/ tner_3_2012.pdf#page=134 Carpenter, J. P. (2016). Unconference professional development: Edcamp participant perceptions and motivations for attendance. Professional Development in Education, 42(1), 78–99. doi:10.1080/19415 257.2015.1036303 Carpenter, J. P., & Krutka, D. G. (2014). How and why educators use Twitter: A survey of the field. Journal of Research on Technology in Education, 46(4), 414–434. doi:10.1080/15391523.2014.925701 Carpenter, J. P., & Krutka, D. G. (2015). Engagement through microblogging: Educator professional development via Twitter. Professional Development in Education, 41(4), 707–728. doi:10.1080/19415 257.2014.939294 Carpenter, J. P., Willet, K. B. S., Koehler, M. J., & Greenhalgh, S. P. (2019). Spam and educators’ Twitter use: Methodological challenges and considerations. TechTrends, 1–10. doi:10.100711528-019-00466-3 Coleman, J. M., Rice, M. L., & Wright, V. H. (2018). Educator communities of practice on Twitter. Journal of Interactive Online Learning, 16(1), 80–96. https://www.ncolr.org/issues/jiol/v16/n1/5/ Davis, K. (2015). Teachers’ perceptions of Twitter for professional development. Disability and Rehabilitation, 37(17), 1551–1558. doi:10.3109/09638288.2015.1052576 PMID:26030199 Fischer, C., Fishman, B., & Schoenebeck, S. Y. (2019). New contexts for professional learning: Analyzing high school science teachers’ engagement on Twitter. AERA Open, 5(4). doi:10.1177/2332858419894252 Galvin, S., & Greenhow, C. (2020). Educational networking: A novel discipline for improved K-12 learning based on social networks. In A. Peña-Ayala (Ed.), Educational Networking (pp. 3–41). Springer. doi:10.1007/978-3-030-29973-6_1 Greenhalgh, S. P. (2020). Differences between teacher-focused twitter hashtags and implications for professional development. Italian Journal of Educational Technology. doi:10.17471/2499-4324/1161 Greenhalgh, S. P., & Koehler, M. J. (2017). 28 days later: Twitter hashtags as “just in time” teacher professional development. TechTrends, 61(3), 273–281. doi:10.100711528-016-0142-4 Greenhow, C., Galvin, S. M., & Staudt Willet, K. B. (2019). What should be the role of social media in education? Policy Insights from the Behavioral and Brain Sciences, 6(2), 178–185. doi:10.1177/2372732219865290 Hodges, C. B. (2015). Professional development tools and technologies. In J. M. Spector (Ed.), The SAGE encyclopedia of educational technology (pp. 590–593). SAGE Publications, Inc. doi:10.4135/9781483346397. n246

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Hogan, J. (2020). How Twitter can serve as a COVID-19 school resource. Retrieved April 9, 2020 from: https://blog.nassp.org/2020/03/24/how-twitter-can-serve-as-a-covid-19-school-resource/ Kimmons, R., & Veletsianos, G. (2018). Public internet data mining methods in instructional design, educational technology, and online learning research. TechTrends, 62(5), 492–500. doi:10.100711528018-0307-4 Krutka, D. G., Carpenter, J. P., & Trust, T. (2017). Enriching professional learning networks: A framework for identification reflection and intention. TechTrends, 61(3), 246–252. doi:10.100711528-016-0141-5 Malik, A., Heyman-Schrum, C., & Johri, A. (2019). Use of Twitter across educational settings: A review of the literature. International Journal of Educational Technology in Higher Education, 16(1), 36. doi:10.118641239-019-0166-x Mott, J. (2010). Envisioning the Post-LMS Era: The Open Learning Network. EDUCAUSE Quarterly, 33(1). Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015. University of Texas at Austin. Pertl, M. M., & Hevey, D. (2010). Exploratory data analysis. In N. J. Salkind (Ed.), Encyclopedia of research design (pp. 455–458). SAGE. doi:10.4135/9781412961288.n143 Risser, H. S. (2013). Virtual induction: A novice teacher’s use of Twitter to form an informal mentoring network. Teaching and Teacher Education, 35, 25–33. doi:10.1016/j.tate.2013.05.001 Rosenberg, J. M., Greenhalgh, S. P., Koehler, M. J., Hamilton, E. R., & Akcaoglu, M. (2016). An investigation of state educational Twitter hashtags (SETHs) as affinity spaces. E-Learning and Digital Media, 13(1-2), 24–44. doi:10.1177/2042753016672351 Sheridan, L., & Young, M. (2016). Genuine conversation: The enabler in good mentoring of pre-service teachers. Teachers and Teaching, 22(3), 293–314. doi:10.1080/13540602.2016.1218327 Staudt Willet, K. B., & Rosenberg, J. M. (2021). tidytags: Simple collection and powerful analysis of Twitter data (Version 0.1.2) [R package]. https://github.com/bretsw/tidytags Trust, T., Krutka, D. G., & Carpenter, J. P. (2016). Together we are better: Professional learning networks for teachers. Computers & Education, 102, 15–34. doi:10.1016/j.compedu.2016.06.007 Tucker, L. (2019). Educational professionals’ decision making for professional growth using a case of twitter adoption. TechTrends, 63(2), 133–148. doi:10.100711528-018-0346-x Veletsianos, G. (2017). Three cases of hashtags used as learning and professional development environments. TechTrends, 61(6), 284–292. doi:10.100711528-016-0143-3 Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge University Press. doi:10.1017/CBO9780511803932

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Wickham, Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., Seidel, D., Spinu, V., ... Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686

ADDITIONAL READING Benoit, K., Watanabe, K., Wang, H., Nulty, P., Obeng, A., Müller, S., & Matsuo, A. (2018). quanteda: An R package for the quantitative analysis of textual data. Journal of Open Source Software, 3(30), 774. doi:10.21105/joss.00774 Cho, V., Dennen, V., Fishman, B., & Greenhow, C. (2019). Education and social media: Research directions to guide a growing field. Teachers College Record, 121(14), 1–22. Greenhalgh, S. P., & Koehler, M. J. (2017). 28 days later: Twitter hashtags as “just in time” teacher professional development. TechTrends, 61(3), 273–281. doi:10.100711528-016-0142-4 Jocker, M. L. (2014). Text Analysis with R for Students of Literature. Springer., doi:10.1007/978-3-31903164-4 Kimmons, R., Carpenter, J. P., Veletsianos, G., & Krutka, D. G. (2018). Mining social media divides: An analysis of K-12 US School uses of Twitter. Learning, Media and Technology, 43(3), 307–325. doi :10.1080/17439884.2018.1504791 Lantz-Andersson, A., Lundin, M., & Selwyn, N. (2018). Twenty years of online teacher communities: A systematic review of formally-organized and informally-developed professional learning groups. Teaching and Teacher Education, 75, 302–315. doi:10.1016/j.tate.2018.07.008 Rosell-Aguilar, F. (2018). Twitter: A professional development and community of practice tool for teachers. Journal of Interactive Media in Education, 2018(1), 1. doi:10.5334/jime.452 Staudt Willet, K. B. (2019). Revisiting how and why educators use Twitter: Tweet types and purposes in# Edchat. Journal of Research on Technology in Education, 51(3), 273–289. doi:10.1080/15391523 .2019.1611507

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Chapter 31

Mapping the Mappers:

Exploring the Communities of VGI Users Through OpenStreetMap Data Francesca De Chiara Bruno Kessler Foundation, Italy Maurizio Napolitano https://orcid.org/0000-0002-7064-8334 Bruno Kessler Foundation, Italy

ABSTRACT Volunteered geographic information (VGI) platforms generate crowdsourced layers where a vast amount of shared and shareable geo-information is available. Monitoring the informative reliability of these sources is an important task, and the main VGI project, OpenStreetMap is a good testing ground to investigate how the collective intelligence made of users’ networks creates public knowledge. OpenStreetMap (OSM) can be defined as a language of representation of real geographical entities shared as web maps. Mappers often work in solitude, but they stick to and strictly respect the rules given by their community. The aim is to create a geographical database used by anyone for any purpose. The chapter explores the following questions: How many contributors are there? Where are they and what do they collect? What are the interactions between them? The chapter illustrates what can be read from the OSM data, the available tools, and what could help researchers to understand this community.

INTRODUCTION The value of territorial knowledge is largely recognised when it’s shared. Entire communities and groups of mappers have made it more tangible and visible through digital maps by using and populating OpenStreetMap. Researchers from different disciplines are capturing the implicit possibilities of observing transformations by starting from the analysis of the Wikipedia of Maps, as Fox (2012) described it because of its editable characteristic. This chapter introduces OpenStreetMap as a data source, mapping tool and a means of visual representation, illustrates what OpenStreetMap is, when, how and why it was DOI: 10.4018/978-1-7998-8473-6.ch031

Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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created. Following this brief introduction, the second section focuses on the socio-economic value it produces and the economic interest shown by companies and corporations. An overview of the literature review shows the relevance of OpenStreetMap for the research and, in particular, the social research. The section also motivates the contribution of OpenStreetMap to research and considers the problem of data quality within the realm of voluntary geographic information (VGI). In the review, the authors add considerations on citizen science (mentioning extreme citizen science), social innovation (e.g., all those local initiatives based on OpenStreetMap, which were then replicated elsewhere) and humanitarian aid. Following this review, the chapter’s core investigates how users collaborate on the project, how they collect data, what’s in the data to learn about people, vandalism, the role of some organisational bodies in the OSM Foundation. The authors explore different methods to explain contributors’ motivations and question the concept of community. The chapter illustrates the methods and tools to extract data on the users and information about an area through the Overpass-API and OHSOME APIs (reading the historical data). The case study focuses on an area in the Alpine Arc, not previously covered and lacking mapped streets. By observing the contributors’ activity, the authors explain how small enterprises can use OSM to solve local challenges in rural areas and the value of local knowledge visually represented through digital maps.

BACKGROUND Mapping the Earth is a process of exploring and representing the territory, a visualisation of local knowledge. Traditionally, this field has been considered reserved for highly skilled individuals and groups. Over the years, surveyors, cartographers, and geographers have been engaged in mapping the world by transcribing it on paper and then digitising it. The history of maps and map-making is full of remarkable episodes of expeditions, for example, Lewis and Clark’s one to map North America’s West and Lambton and Everest’s Great Arc expedition to measure India. National mapping agencies are established in each country and preserve the accuracy and the updates of the national maps. It was common to assume that mappers needed a university-level degree to measure the Earth and transcribe the information on paper or into the computer and that expensive equipment and infrastructure was fundamental to support their work. However, some changes have occurred over the past few decades. Following the removal of the selective availability of the GPS signal by US President Bill Clinton in 2000 (Haklay et al., 2008) and the publication of the interchange standard (GPS eXchange format or GPX) in 2002, the low-cost GPS receivers with better positional accuracy (6 to 10 meters in normal conditions, in contrast to roughly 100 meters) became available on the market and GPS receiver developers rapidly adopted the GPX standard. More people than ever before collected and uploaded information about different locations to their computers. The wide availability of high-quality location information has enabled mass-market mapping based on affordable GPS receivers, home computers, and the Internet. Although a range of projects based on user-generated mapping has emerged, OpenStreetMap (OSM) is probably the most extensive and effective project (Haklay et al., 2008). “The availability of low-cost, high-quality and high-accuracy Global Positioning System (GPS) means that consumers or citizens can now collect geographic information using smart devices such as smartphones or dedicated GPS units; these geographic data can then be uploaded and contributed to OpenStreetMap (OSM)” (Mooney and Minghini, 2017). Haklay et al. (2008) describe OpenStreetMap as “a knowledge collective that provides user-generated street maps”. These 527

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collaborative mapping projects involve thousands of volunteers in the creation, organisation, and use of geoinformation, consequently described as “voluntary geographic information” or VGI (Goodchild, 2007), leading to what has been labelled as a “neogeography” (Goodchild, 2009). The OSM project welcomes anyone to register and take part as a contributor. Mappers can be beginners or experts (usually geographers or software developers). The OSM data model is very straightforward to understand. The primitive data types or objects are nodes, ways (polygons and polylines) and relations (logical collections of ways and nodes). A way is made up of at least two nodes (for polylines) or three nodes (for closed polygons). A node represents a geographic point feature, and its coordinates are usually expressed as latitude and longitude. Within OSM, every object must have at least one attribute or tag (a key/value pair) assigned to describe its characteristics. There are many guides and tutorial documents on how to start mapping with OSM. The OSM Map Features pages on the OSM wiki represent the reference document describing the officially adopted OSM tags. These tags have been agreed upon over the years, and there are wiki pages written to describe the likely usage and use case scenarios of each tag. OSM follows a folksonomy approach to tagging, and, in theory, any tag can be associated with any object (Ballatore and Mooney, 2015). OpenStreetMap leaves contributors free to create tags. As several authors have shown (Ballatore and Mooney, 2015; Ballatore and Zipf, 2015), this could lead to disagreements amongst contributors or confusion about using specific tags in certain geographic scenarios. Services such as taginfo allow exploration and visualisation of the most frequently used tags and their keys for the entire OSM database. The taginfo service is particularly useful for understanding the style or structure of tags used on specific object types, conceptualising the vast range of values some keys are assigned in tags and the spatial distribution of tags. Taginfo is constantly updated in near realtime and stores the tags from every object in the global OSM database. There is no theoretical limit on the number of tags that can be assigned to any object. Nodes that have a tag with a key name are usually called Points of Interest (POI) and typically represent the position of some object or structure of general interest (Ballatore and Mooney, 2015). The authors agree upon the description of OpenStreetMap as a “community of communities, that curate and edit map data on a single platform, compelled by a range of individual and shared motivations, but with the overarching objective of creating a freely accessible, open, and editable map of the world” (Anderson et al., 2019). Kitchin and Dodge (2007) underline the ever-evolving nature of the maps, defining them as a “product of embodied, social, and technical processes that are never fully formed”. These communities edit the map with different motivations, hoping that the common platform results in a uniform valuable product for all. The ongoing efforts of this “community of communities” make OSM a snapshot of the moment, an ever-evolving map reflecting the values and priorities of their creators. Very recently, a particular type of community has emerged and grown: the corporate editors. A crucial aspect in the current debate on OSM development is related to paid editors that are professional mappers (Anderson et al., 2019). Companies investing in OSM is a pretty new trend that is generating a conflict among the community members. The study by Anderson et al. (2019) was the first to explore the role and contributions of corporate entities editing OSM at scale. They analysed the editing activity of teams transparently employed by ten corporations. Some editing mistakes have made the OSM community suspicious of corporate editing. Guidelines around transparency and community engagement are now in place. These corporations should attend the rules by making the usernames of their editors available. Studying corporate involvement in OSM informs and contextualises quantitative analyses of the OSM database to measure the companies’ global footprint.

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Figure 1. Statistics on active members as of 07/07/2021

Source: https://osmstats.neis-one.org/?item=members

Regarding the contributors, it’s possible to get a series of information and statistics on OSM contributors through the website OSMStats - https://osmstats.neis-one.org - developed by Pascal Neis. As of 07/07/2021, we currently have over 7,000 users contributing daily to the map. The map above shows the points where the contributions took place around the world as of 07/07/202. It shows where data is collected most, at the national and city level, which type of data is collected the most, and which hashtags are used the most in the comments. The literature on the behaviour of OSM contributors, the phenomenon of the corporate editors that does eradicate the original nature of the VGI exposing volunteers vs paid editors, not rarely relates to the issues of data quality and is incorporated by the citizen science’s domain and more often the extreme citizen science (Haklay, 2013), as well as the questions of social innovation and civic technology applications based on OSM. A significant rise is observable in the urban contexts, where local initiatives respond to local demand and needs. The social and political dimensions of the OSM project are becoming a research object interesting for the development of a critical geographical research agenda. According to Glasze and Perkins (2015), “a fundamental transition in mapping is taking place, and that OSM may well be of central importance in this process. Since the 1990s, a debate has developed amongst scholars of critical cartography, which focuses on the practices, conventions, and techniques of map-making and use and thus goes beyond former concerns with the map’s visual design”. Drawing upon works by Pickles (2004) and Dodge et al. (2009), the research perspective by Glasze and Perkins questions how mapping practices shape social worlds. They mention Bruno Latour, who took modern cartography to show how specific practices and techniques were used to produce scientific knowledge and thus authority in European centres of power. These practices, conventions, and techniques created preconditions for international trade, territorial expansion, and global colonisation (Latour, 1986). Thus,

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Figure 2. Ranking by country created by Pascal Neis Source: https://osmstats.neis-one.org/?item=trending

new geographies and maps served as immutable mobiles, circulating and reifying a particular way of knowing the world. This interpretation amplifies the debate on GIS and society, opening up to practices and technologies “beyond” and “behind” these representations. Concerning the research directions, three crucial aspects should be highlighted: i) the influence of economic and military interests in GIS development, ii) the disparities over the access to production and the use of geographic information arising from the complexity and cost of GIS, and iii) the quantifiable and metric information with the consequent danger of marginalisation of “qualitative” interpretation. Following the “democratisation” of GPS systems

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Figure 3. Contributions to OSM – Ranking by country created by Pascal Neis Source: https://osmstats.neis-one.org/?item=countries (as of 7 July 2021)

and their diffusion in smart devices, web mapping has become part of mobile and ubiquitous practices. The growing number of “open” projects based on crowdsourcing, with OpenStreetMap being the most prominent example, has also stimulated research approaches developed in critical cartography and “GIS and society” and profit from relations to the broader field of critical social and cultural geography and interdisciplinary internet studies (Graham 2009; Caquard 2014). Elwood et al. (2010, 2011, 2012) have studied different aspects of collaborative and community-based mapping, reorienting attention to the power of technical and political infrastructures in privileging certain kinds of information, moments, or affordances, and drawing attention to the exclusions that are normalised in the neutral specifications of mapping projects. Technical research elides the social and political context of web mapping projects and allows them to advance as “new” without thinking about why or how they are advancing. Glasze (2014) reflects on the main relevant questions concerning this research stream, trying to orient future questions. The main ones relate to the “opening” of geoinformation seen as a result of a process of commodification and commercialisation, the role of communities of collaborative internet activists, the consequences of this shift for nature, quality, processing and presentation of geodata, the way social and spatial inequalities are reproduced in this process, and the new possibilities for surveillance and

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marketing, power, governance, resistance and privacy. These issues can usefully be examined by focusing on OpenStreetMap, now considered the subject of academic research (Arsanjani et al., 2015). Further, OSM offers a unique global dataset and a body of knowledge created and maintained by an extensive collaborative network of volunteers. Research shows that OSM geodata in some parts of the world is complete and locationally and semantically more accurate than the corresponding proprietary datasets while being of high spatial heterogeneity. Scepticism surrounding the quality of the geodata in OSM has resulted in a significant effort to evaluate the quality of the OSM geodata. This has led to several software tools and methodologies for analysing the quality. Other approaches try to improve OSM data through algorithms dedicated to specific object types, such as addresses for geocoding. Investigation of the development and evolution of OSM across the globe over time has also emerged as a research topic for many academic studies (Mooney et al., 2012; Neis and Zipf, 2012; Jokar Arsanjani et al., 2013; Mooney and Corcoran 2013). Several studies dealing mainly with the contributors-centric perspectives of OSM have been conducted, such as analysis of interaction and co-editing patterns amongst OSM contributors by Mooney and Corcoran (2013), the emergence and evolution of OSM by Jokar Arsanjani et al. (2014), and the comparison of the OSM contributions across selected cities in regard with the impact of their national gross domestic product (GDPs) (Neis, Zielstra, and Zipf, 2013), among others. However, no specific study has statistically (and not based on surveying) assessed the possible relationships between the location of contributions and residents’ characteristics because the mapped objects are assumedly implicative of residents’ interactions with the environment. The main objective of the study by Jokar Arsanyani and Bakillah (2015) is to use the contributions to OSM to explore the spatial patterns of the contributions and the driving factors that could characterise the underlying relationships between the contributions and the socioeconomic characteristics of local inhabitants. This study applies an exploratory analysis of a subset of OSM contributors and their contributions to highly contributed areas. It is presumed that the location of contributions indicates the socioeconomic patterns of their contributors. Mashhadi et al. (2015) ask whether society and its characteristics, such as socioeconomic factors, impact what part of the physical world is being digitally mapped. This question is necessary to understand where crowdsourced map information can be relied upon (and crucially where not), with direct implications on the design of applications that rely on having complete and unbiased map knowledge. To answer these questions, they have studied over six years of crowdsourced contributions to OSM. The authors have measured the positional and thematic accuracy and completeness of this information and tried to quantify the role of society on the state of this digital production. Finally, they quantify the effect of social engagement as a method of intervention for improving users’ participation. However, one aspect that perhaps received less attention from the GIS research community is the impact of society and factors such as those of socioeconomics on the contributed information. This aspect has been studied in the domain of social sciences and has been shown to have a high impact on the content generation in Web 2.0. In the domain of GIS, as the contributions are intrinsically spatial, the potential digital production gap may contribute to some areas not being mapped. The risk is that if the socioeconomic factors are responsible for this digital production gap, the deprived areas would also remain information deprived. The aim is to investigate this aspect further by studying the extent to which society’s characteristics, such as socioeconomic factors, determine the success of spatial crowdsourcing and VGI and finally to present the possible interventions that could be done to improve communities’ participation in OSM, by measuring the impact of the social engagement of the editors on their participation. 532

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Concerning the quality of volunteered geographic information, the purpose of the “social” approach by Goodchild and Li (2012) is to rely on a hierarchy of trusted individuals who act as moderators or gatekeepers. Many studies have shown that voluntary contributions by individuals follow a frequency distribution with a long tail, with a few individuals making large numbers of contributions and a large number of individuals making only one or a few. Mooney and Corcoran (2012) analysed OpenStreetMap data for the British Isles and found that for heavily edited features (at least 15 edits per feature), 84% of edits were made by 12% contributors. Tracking contributions by individuals allows for calculating metrics of commitment and reliability that may provide a basis for trust. For example, an individual who makes prolific contributions that attract few edits could be assigned a high score and given a role as moderator or gatekeeper. This type of hierarchical structure already exists in many self-regulated collaborative efforts. In Wikipedia, a group of administrators called sysops have privileges that are not provided to ordinary editors, such as deleting articles, protecting pages from editing, and blocking users. Similarly, in OpenStreetMap, there are two tiers in the hierarchy of contributors: ordinary users, and the Data Working Group (DWG), currently composed of eight members, who deal with vandalism, copyright violation, disputes, geographic locking, etc. Every registered user can add and edit geographic features; however, DWG decides whether there is any dispute. In effect, such hierarchies of trust emulate the structure of traditional authoritative mapping agencies, where experience and qualifications act as surrogates for reliability, and promotion leads to greater authority and higher salaries. The social approach to VGI quality assurance replaces the formal structure of an agency with a much less formal, voluntary system. Flanagin and Metzger (2008) provide a detailed discussion of trust and credibility in the context of VGI. They identify a pressing need to determine the motivations of contributors and argue that VGI is more likely to be trusted than other forms of user-generated content because geographic facts are perceived to be objective and replicable. Some of the more effective applications of the social approach fall into the hybrid area described previously by combining elements of both volunteered and authoritative practice. In their work, Herfort et al. (2021) highlight three essential recommendations. The first one is about improving methods to monitor mapping activity and identify where mapping is needed. At the current state, for most organisations, “monitoring” merely consists of counting the overall number of buildings and highways and usually neglects the spatial and temporal components of mapping activity. Therefore, the humanitarian OSM community should adopt monitoring methods that can more clearly address the question of “Where is mapping needed?” instead of “How much mapping happened?”. They provide spatial analysis methods as a basis for starting addressing the former question. Second: rethink project design to avoid non-sustainable outcomes. The organisations engaged in countries with one-time mapping efforts should be encouraged to assess how projects could be designed to contribute to more sustainable outcomes. We argue that “putting countries on the map” should be seen as only the first step towards considering such countries included, as real inclusion requires an active local OSM community with the capacity to generate, maintain and improve geographic data that reflects local perspectives in the long term. We see a critical lack of local engagement by design, especially in the fast-developing domain of applying artificial intelligence and machine learning methods to humanitarian mapping purposes. This might reproduce the same one-time-mapping patterns observed in the past, as revealed by our analysis. Third: remove structural barriers to empower local communities and develop capacity. Future mapping efforts should promote local data generation methods that achieve high-quality data and simultaneously empower local communities and support them to acquire new perspectives on their territories and development potentials. 533

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Focusing on empowerment goes beyond technically involving local communities in the map-making process. To achieve this, humanitarian organisations and local OSM communities need to identify the structural barriers that exclude some social groups from participating in (humanitarian) OSM mapping and work with them to develop a capacity to overcome those barriers. Balancing the unequal spatial and temporal contribution patterns in OSM goes beyond a more comprehensive geographic database population. It is about building diverse and vibrant communities that can participate in the map-making process and are empowered by the mapping results. Such an inclusive OSM community can fuel the much-needed data revolution and prove its value to inform better political actions towards achieving the SGDs (Herfort et al., 2021).

MAPPING THE MAPPERS In this section, the authors explore how the mappers collaborate to the OSM project by starting from the data collection process, analysing the vandalism, the role of the OSM Foundation, the Data Working Group and the Legal Working Group, how to extract mapper information from the data, how to get to know the users through some tools, how to extract information on areas through Overpass-APIs and OHSOME with different advantages.

The Contributors’ Motivations Neis and Zipf (2012) argue that the “success of the VGI approach to data collaboration and sharing is undeniable”. However, the authors highlight the problems concerning the members’ motivation to participate in projects such as OSM. A series of motivational factors are emerging from the research results; however, they do not differ from the drivers pushing other online communities and platforms such as Wikipedia (freely available information to everyone; learning new technologies, self-expression, relaxation and recreation or just pure fun). The OSM project has hundreds of thousands of registered members, but a tiny percentage is active. Neis and Zipf investigated different datasets of the OSM project at the time, December 2011. Several sources on the Internet have reported on many contributors to the OSM project, which exceeded in December 2011. However, the results have shown that only 38% of the total number of 500,000 registered members, around 192,000, carried out at least one change during their membership. They propose a classification into three categories according to their number of contributions to the project: Senior Mappers, who created more than 1,000 Nodes; Junior Mappers, who made 10 to 1,000 Nodes, and; Nonrecurring Mappers, who only created less than 10 Nodes. The findings of these analyses with a focus on user activities show that the Senior Mapper group represents the smallest group in the database and the most significant part of registered members never contributed at all. The majority of the members are located in Europe (72%), while the remaining members (28%) are divided as follows: North America (12%), Asia (8%), South America (3%), Australia (2%), Africa (2%), and Oceania (1%). Analysis showed that more than half of the Senior Mappers members collected information for the OSM project in at least two different countries. They pointed out the importance of not relying only on the data collection process but also on maintaining the data to keep it as accurate and up to date as possible. Further analysis and observation are needed to track the development of the number of members per country, research that goes “beyond the general activity of the members

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and focuses on changes within the activity areas of the members or whether members edit or improve the objects of other members could be conducted as well” (Neis and Zipf, 2012). When first registering on the OpenStreetMap platform, users are invited to report where they are, and from there, they can see registered users around them and add them as friends. From here, however, no type of interaction arises as happens instead in the social network platforms. Mooney and Corcoran (2012) argue that “there is no explicit social network structure to the OSM spatial database”. OSM contributors do not “follow” or “link” to each other explicitly. They did construct a social network representation of edit patterns using an analysis of the history of all edits and contributions to a specific area. The results showed that a low number of contributors collaboratively edit features, much of the work of editing is carried out over a long time by a small number of contributors, and there’s a lack of significant collaboration amongst them. The platform allows sending users messages and having a blog-style space (used by a few users, especially very active ones, mainly in English). Users can decide to contribute to OpenStreetMap through various tools: the editor integrated into the website (iD) or applications that interface with the API from the more complex ones (e.g. JOSM - a software with advanced features) to apps. It’s possible to make timely reports using smartphones (e.g. StreetComplete, through which people are asked to integrate or correct data in OpenStreetMap). There are free to contribute with any tool. A summary of tools is provided here. • • • •

JOSM used by advanced users; iD Editor - integrated into the website www.openstreetmap.org; MAPS.me - app through which users can enter points of interest (many users don’t know that by contributing to maps.me they are also contributing to OpenStreetMap); StreetComplete - gamification app to improve OpenStreetMap data

Statistics information is available on the OSM wiki pages. Additional statistics on changesets (the set of all the changes that are sent after changing the data) are also available. Each of these editors offers a set of pre-settings that guide users in entering and uploading data. Interactions between users occur only if some decide to do so voluntarily and sending an email. However, some bots check for vandalism or errors and automatically send notifications to users. Therefore, in OpenStreetMap, sociality arises from the need for comparison in cases where anomalies are found in the data. There are also many different channels through which users interact, and they are all very fragmented: official wiki pages, thematic or national mailing lists, Telegram groups of all kinds, Twitter, IRC channels, Facebook groups. In each of these spaces, decisions are made. The community gathers in an international event that reunites all the members annually, the international State of The Map conference, which also has local equivalent versions at the national or continental levels (Asia, Africa, etc.). The OpenStreetMap data still contains information about users (included in the metadata). The data consists of the username (including the unique identification code) and the date of the modification. From the data, it is then possible to identify information such as geographical coordinates, the type of data collected, whether it is new data or a modification or cancellation, the kind of software used to edit and any additional notes (which end up in the changeset) that may be useful. Based on this information, it is possible to obtain information about the users (profiles, places mapped, comparison between users). Pascal Neis’ work and his tools make this information available to all. Neis’ work has been recently 535

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criticized because of the privacy risks, the transparency on the processing of personal data of contributors and the tracking of contributors, and the sharing of OSM data with third parties (Riemann, 2021). Ma et al. (2015) published an interesting article on the heterogeneity of the OpenStreetMap data and community. They have studied the scaling pattern in the collaboration network of the OSM users. The social relationship utilized in their research is “co-contribution relationship since friend relationship like other social platforms is undocumented in OSM history” (Ma et al., 2015). This is established when more than one user contributes to the same element. Mooney and Corcoran (2012) consider just the edit interaction and the sequence of edits. The co-contribution network is different from the co-edit network built by Mooney and Corcoran. The heterogeneity can be somewhat illustrated and measured from the element, users, and their collaborations. Concerning the way users and researchers can access OpenStreetMap data, the authors provide the following list: •

• • •

Planet (https://planet.openstreetmap.org) provides various datasets valuable to reconstruct the OpenStreetMap database, and also to retrieve information on changesets, discussions, notes, access logs to the server that provides the maps etc. in XML formats and pbf and with different time cuts (complete, last week, last month). The Planet History file is a precious source to know the evolution of OpenStreetMap and contains the entire history of data evolution. XAPI (https://wiki.openstreetmap.org/wiki/API_v0.6) are all the APIs to interface to the OpenStreetMap server for reading and writing data, user management and more. They are mainly used to interact with the database. Overpass-API offers a series of APIs to make complex queries to OpenStreetMap data, overcoming the limitations of XAPI. The most famous front-end is overpass-turbo.eu OHSOME (http://ohsome.org) presents APIs through which to query the historical data of OpenStreetMap. There’s a beautiful example to analyze the urban green areas - https://github. com/GIScience/ohsome-py/blob/master/notebooks/Public_Green_Spaces_in_OSM.ipynb

Most services periodically download data by using the Planet OSM service and then offer conversions in various formats. Concerning how the users of an area map and interact in OpenStreetMap, and the characteristics that can be identified, this paragraph shows all the needed stages by starting with the available interfaces to get to the data analysis. These stages are: the inspection of an area; the identification of the characteristics of the user; the identification of active users and finally how the coverage of an area is progressing. 1- Inspect an area. From the official website https://www.openstreetmap.org we can view the data as well as the map. This operation is obtained by selecting “Data” from the “Layer” menu (located in the right button panel). A data layer in blue will appear on the map. Clicking on each object, an information sheet about the object will appear with metadata containing the username. It is crucial here to check the date of the last update. By clicking on the username, you access the OpenStreetMap page, where you can then send a message (if registered) and find out the information that the user has entered about himself. 2- Know the characteristics of a user. All user pages that have not been deleted on OpenStreetMap can be reached at https://www.openstreetmap.org/user/NAME, where “NAME” is the username.

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Figure 4. The heat map of the OpenStreetMap contributions of the user “napo” from the “Your OSM Heat Map” service, created by Pascal Neis

Source: https://yosmhm.neis-one.org/?napo

Further details can be detected from the applications created by Pascal Neis, in particular, Where did you contribute to OSM? and How did you contribute to OpenStreetMap? where you can see the areas of the world where the user has entered data, while, in the second, details on the areas where he works most, the days of activity. To access the data of the second tool How did you contribute to OpenStreetMap? , it is necessary to be a registered user as, in the past, there have been controversies regarding violation of privacy. The report that this application generates is very interesting because, in addition to showing the area where people work, it also shows the type of data it has collected the most. On the type of data, further considerations can be made. 3 - Know the active users in an area. Among the tools developed by Pascal Neis there is also “Overview of OpenStreetMap Contributors aka Who’s around me?” - https://resultmaps.neis-one.org/oooc. This application starts from London but moves to other areas. It then shows the various users who - in relation to what they insert in OpenStreetMap - appear in that area (it is, therefore, an indicator of the area where one operates).

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Figure 5. The profile section for the user “napo” of the analysis generated from the tool “How did you contribute to OpenStreetMap?” by Pascal Neis

Source: https://hdyc.neis-one.org/?napo

Pascal Neis’ classification is based on the amount of “changesets” (= set of data inserted into a session). However, it becomes a useful tool to know who is contributing to the map. 4 – Ohsome (pronounced “awesome”) is a project from the Alexander Zipf group investigating the progress of data covering an area. It’s based on historical OpenStreetMap data. Ohsome has generated the creation of applications such as OSM History Explorer (https://ohsome.org/apps/osm-historyexplorer/) highlighting content development; Ohsome quality analyst (https://oqt.ohsome.org/) that runs a data quality analysis based on historical evolution; Ohsome dashboard (https://ohsome.org/ apps/dashboard/) with which you can view the trend of the collection of specific tags over time in an area; Ohsome2label (https://github.com/GIScience/ohsome2label), which creates models to train machine learning algorithms to recognize objects from an aerial photo (Raifer et al., 2019; Wu et al., 2020); Humanitarian OSM Stats (https://humstats.heigit.org/), which displays the statistics of the work of OpenStreetMap humanitarian aid projects. Ohsome APIs easily allow getting data from an area to study the users who have collaborated and their evolution. These tools can help researchers to get insights on mappers’ behaviour, the collaboration develops, the correlation between the nature of the territory and the relevance of local knowledge. Concerning

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Figure 6. The contributions to OpenStreetMap during the time for the user “napo” of the analysis generated from the tool “How did you contribute to OpenStreetMap?” by Pascal Neis Source: https://hdyc.neis-one.org/?napo

the motivations, the case study in the next paragraph shows how the local demand of delivery services and local communities’ needs in general could be studied and analysed through OpenStreetMap as an instrument of visualizing local knowledge.

OPENSTREETMAP AND LOCAL KNOWLEDGE The presented case study reports an analysis of the Giudicarie area in the Italian region of Trentino and its changes following the considerable diffusion of e-commerce systems (Amazon, etc.) to combine the changed needs of the local population with aspects of social and environmental inclusion. The analysis started within the Logistics of Community (LGC) project with the objective of solving some challenges, adapted from a broader perspective to the entire Alpine arc. Logistics services scale up in large numbers and are spreading considerably; the demographic distribution of the Trentino valleys and the characteristics of spatial morphology typical of local mountain communities severely limit the scalability of these services; there is a substantial inefficiency with waste of time and resources (more couriers pass several times a day in places with low population density); couriers often do not complete the delivery, especially in high mountain villages or in isolated areas that are not well mapped geographically; the

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Figure 7. The main & latest (6 months) activity area for the user “napo” of the analysis generated from the tool “How did you contribute to OpenStreetMap?” by Pascal Neis Source: https://hdyc.neis-one.org/?napo

increase and circulation of goods transport vehicles lead to the rise in traffic on mountain roads with an impact in terms of increased emissions into the atmosphere and accelerating the wear and tear of road infrastructures (consequent increase in maintenance costs). Looking at the specificity of the project, which is also strongly linked to the orography of the territory, it has been considered more appropriate to develop solutions based on open-source products. The main advantage is to create an independent and replicable system. Some solutions, amongst the most successful ones, have been identified. To solve the logistics problems, OpenRouteService to calculate the cost matrix and Vroom to vehicle routing problem have been selected, following an accurate analysis of open-source products to be used in the project. Given the technological choice oriented to use platforms such as OpenRouteService and Vroom that already have importers based on OpenStreetMap, the creation of the road graph, its updating and its maintenance go in the direction of adopting the same data model. Therefore, we will start with the

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Figure 8. Mapping activities and editors used by the user “napo” from the analysis generated from the tool “How did you contribute to OpenStreetMap?” by Pascal Neis Source: https://hdyc.neis-one.org/?napo

mapping of the pilot area using the solutions mentioned above to get its proprietary management of the data mapped on the road graph. It will then be chosen by the LGC project partners whether to make this dataset public. The maintenance of the road graph must consider all the possible combinations of means by which it is possible to move: cars, heavy vehicles, bicycles, electric bicycles on foot. LGC project started to use OpenStreetMap in March 2021 and then focused its energies on mapping the streets. The results can be easily observed in the graph below. In this context, the first contributor can be defined as a paid editor on OSM. The community of mappers developed around the main contributor has been initially engaged in this finalized mapping activity (useful for the logistics project). Even though, after the first round of mapping, these additional mappers, Figure 9. Changesets per country by the user “napo” from the analysis generated from the tool “How did you contribute to OpenStreetMap?” by Pascal Neis Source: https://hdyc.neis-one.org/?napo

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Figure 10. “The OpenStreetMap Contributors Map aka Who’s around me?” - the tool created by Pascal Neis, which shows the centroid of the activity of the OpenStreetMap contributors in a specific area (in this case Trento, Italy) in relation to the contributions developed over time Source: http://resultmaps.neis-one.org/oooc

trained by the hired one, have continued to contribute to mapping the area. An increase in the number of mappers in the area had been registered. In the graph, you can see the rise of the number of the mapped streets in the area across a time span of 6 months. The data have been downloaded and processed by using Ohsome. The curve clearly shows a rise in the activity around March 2021, the start of the project with the paid editor and keeps growing since new mappers joined the project and were recruited by the first one. The mappers are basically high school students engaged through training workshops in the schools of the Giudicarie area. Companies, even SMEs, can use OSM in their business models very profitably. The strategy of paid editors and the training activities are fundamental to develop such a model, which can cover unmapped areas, like rural areas and mountain valleys, solve challenges related to logistics, and create a collaboration network. The company, thanks to the tools here shown, can calculate and measure the social engagement and the effects on the affected communities. Among the objectives of the LGC project, there is also the relationship with and the value of local knowledge. The choice of OpenStreetMap, in this case, has been determined by the fact that OSM is seen as a representation language of local knowledge; for example, the details that navigation systems do not contain in OSM can be identified and registered. The level

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of detail in inaccessible areas, however, requires the need to have more details that derive substantially from local wisdom and knowledge. Figure 11. The number of streets mapped by month from January to June 2021 in the Giudicarie Valleys - Trentino – Italy Data extracted using OHSOME.

FUTURE RESEARCH DIRECTIONS This section outlines future research directions, which may help to inform future research on OSM. Sehra et al. (2017) suggest that further investigation is needed to assess the behaviour of the contributors: methods and motivating factors are required to attract contributions to OSM, e.g. using gamification of applications and rewards. The analysis of user contributions lacks a less general framework, but the motivational factors and patterns of user contributions and the attributes should be considered for creating a user reputation system. Another research direction should focus on the rise of corporate editors, their impact on the quality and the community itself. The research on OSM should be oriented to extend a framework for quality assessment based on new quality indicators, defined in the absence of authoritative datasets. Social research could benefit from this perspective of OSM as a platform, but also a more detailed language of representation of the local knowledge. 543

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As shown in the case study on the Community Logistics LGC project, it’s important to investigate the application of OSM to navigation, together with the role of the locals and the value of their territorial knowledge. The suitability of road networks for navigation can be assessed based on the detection of the topological and semantic inconsistencies. Other areas are related to disaster management. The OSM tasking manager is a mapping tool designed and built by the Humanitarian OSM Team (HOT) (https://tasks.hotosm.org/) for handling disaster situations. 3D models are being developed to improve rapid response. Further studies are required: the analysis of road networks using social network analysis. Another potential research and application area is indoor navigation to handle the GPS/heading accuracy issues during indoor mapping, to develop a mobile-based framework for 3D visualization and navigation for indoor maps. Data mining, machine learning and big data can be applied to OSM research. Crowdsourcing has attracted the attention of the research community for quick and low-cost data collection and tagging. Machine learning is appropriate for labelled, uncertain, vague, diverse, continuous and rapid data. Various data and text mining approaches for assessment of and knowledge extraction from OSM data have been used. Spatial data mining is still in its infancy in OSM research. Various data mining approaches such as classification and prediction, association rule mining, clustering, regionalization and point pattern analysis, and geo-visualization could improve the results in various application areas using OSM. New geographical methods for assessment will need to be developed in the computational paradigm since some researchers state that VGI shows many of the characteristics of big data.

CONCLUSION OpenStreetMap is a project that is creating significant socio-economic repercussions. It is a crowdsourcing project from which advantages and disadvantages derive. Getting to know the people who contribute to the project allows having data quality indicators, to identify how to engage people, build communities and much more. This work presented the state-of-the-art of what exists and how, by combining the various opportunities, it is possible to get to know the protagonists of this project. However, several questions remain open: the evolution of the project, the entry of multinationals, which is not welcomed by many of the contributors to the project, the application of the GDPR, which can preclude some of the analysis currently possible to know the community. Each of these points can bring new challenges to research and make significant contributions to the debate on the construction of the commons, the issues of personal data management, data sharing and collaboration scenarios, and data-driven economic models. This resource has attracted a large part of tech corporations (Microsoft, Amazon, Facebook, Apple not only reuse OpenStreetMap data but, at the same time, collaborate in its maintenance). It turns out to be an important tool in humanitarian aid and has a great social value. High in that, on it, projects have been born to provide support to those suffering from various disabilities, to help communities of cyclists in the creation of tools for cultural heritage, to create artistic artefacts and more. OpenStreetMap exists thanks to the contribution of thousands of people who daily contribute to the project; having the tools to get to know people and get in touch with them becomes crucial.

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REFERENCES Anderson, J., Sarkar, D., & Palen, L. (2019). Corporate editors in the evolving landscape of OpenStreetMap. ISPRS International Journal of Geo-Information, 8(5), 232. doi:10.3390/ijgi8050232 Ballatore, A., & Mooney, P. (2015). Conceptualising the geographic world: The dimensions of negotiation in crowdsourced cartography. International Journal of Geographical Information Science, 29(12), 2310–2327. doi:10.1080/13658816.2015.1076825 Ballatore, A., & Zipf, A. (2015, October). A conceptual quality framework for Volunteered Geographic Information. In International Conference on Spatial Information Theory (pp. 89-107). Springer. 10.1007/978-3-319-23374-1_5 Caquard, S. (2014). Cartography II: Collective cartographies in the social media era. Progress in Human Geography, 38(1), 141–150. doi:10.1177/0309132513514005 Dodge, M., Perkins, C., & Kitchin, R. (2011). Mapping modes, methods and moments: a manifesto for map studies. In Rethinking maps (pp. 238–261). Routledge. doi:10.4324/9780203876848 Elwood, S. (2010). Geographic information science: Emerging research on the societal implications of the geospatial web. Progress in Human Geography, 34(3), 349–357. doi:10.1177/0309132509340711 Elwood, S. (2011). Geographic Information Science: Visualization, visual methods, and the geoweb. Progress in Human Geography, 35(3), 401–408. doi:10.1177/0309132510374250 Elwood, S., & Leszczynski, A. (2013). New spatial media, new knowledge politics. Transactions of the Institute of British Geographers, 38(4), 544–559. doi:10.1111/j.1475-5661.2012.00543.x Flanagin, A. J., & Metzger, M. J. (2008). The credibility of volunteered geographic information. GeoJournal, 72(3-4), 137–148. doi:10.100710708-008-9188-y Fox, K. (2012). OpenStreetMap: ‘It’s the Wikipedia of maps’. A map of the world that anyone can edit. Retrieved from The Guardian: https://www.theguardian.com/theobserver/2012/feb/18/openstreetmapworldmap-radicals Glasze, G., & Perkins, C. (2015). Social and political dimensions of the OpenStreetMap project: Towards a critical geographical research agenda. In OpenStreetMap in GIScience (pp. 143–166). Springer. doi:10.1007/978-3-319-14280-7_8 Goodchild, M. F. (2009). First law of geography. In International encyclopedia of human geography (pp. 179–182). Elsevier Inc. doi:10.1016/B978-008044910-4.00438-7 Goodchild, M. F., & Li, L. (2012). Assuring the quality of volunteered geographic information. Spatial Statistics, 1, 110–120. doi:10.1016/j.spasta.2012.03.002 Graham, M. (2010). Neogeography and the palimpsests of place: Web 2.0 and the construction of a virtual earth. Tijdschrift voor Economische en Sociale Geografie, 101(4), 422–436. doi:10.1111/j.14679663.2009.00563.x

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Haklay, M. (2013). Citizen science and volunteered geographic information: Overview and typology of participation. Crowdsourcing geographic knowledge, 105-122. Haklay, M., & Weber, P. (2008). Openstreetmap: User-generated street maps. IEEE Pervasive Computing, 7(4), 12–18. doi:10.1109/MPRV.2008.80 Herfort, B., Lautenbach, S., de Albuquerque, J. P., Anderson, J., & Zipf, A. (2021). The evolution of humanitarian mapping within the OpenStreetMap community. Scientific Reports, 11(1), 1–15. doi:10.103841598-021-82404-z PMID:33542423 Jokar Arsanjani, J., & Bakillah, M. (2015). Understanding the potential relationship between the socioeconomic variables and contributions to OpenStreetMap. International Journal of Digital Earth, 8(11), 861–876. doi:10.1080/17538947.2014.951081 Jokar Arsanjani, J., Helbich, M., Bakillah, M., & Loos, L. (2015). The emergence and evolution of OpenStreetMap: A cellular automata approach. International Journal of Digital Earth, 8(1), 76–90. do i:10.1080/17538947.2013.847125 Kitchin, R., & Dodge, M. (2007). Rethinking maps. Progress in Human Geography, 31(3), 331–344. doi:10.1177/0309132507077082 Ma, D., Sandberg, M., & Jiang, B. (2015). Characterizing the heterogeneity of the OpenStreetMap data and community. ISPRS International Journal of Geo-Information, 4(2), 535–550. doi:10.3390/ijgi4020535 Mashhadi, A., Quattrone, G., & Capra, L. (2015). The impact of society on volunteered geographic information: The case of OpenStreetMap. In OpenStreetMap in GIScience (pp. 125–141). Springer. doi:10.1007/978-3-319-14280-7_7 Mooney, P., & Corcoran, P. (2012, April). How social is OpenStreetMap. In Proceedings of the 15th association of geographic information laboratories for europe international conference on geographic information science, Avignon, France (pp. 24-27). Academic Press. Mooney, P., & Minghini, M. (2017). A review of OpenStreetMap data. Academic Press. Neis, P., Goetz, M., & Zipf, A. (2012). Towards automatic vandalism detection in OpenStreetMap. ISPRS International Journal of Geo-Information, 1(3), 315–332. doi:10.3390/ijgi1030315 Neis, P., Zielstra, D., & Zipf, A. (2013). Comparison of volunteered geographic information data contributions and community development for selected world regions. Future Internet, 5(2), 282-300. Neis, P., & Zipf, A. (2012). Analyzing the contributor activity of a volunteered geographic information project—The case of OpenStreetMap. ISPRS International Journal of Geo-Information, 1(2), 146–165. doi:10.3390/ijgi1020146 Pickles, J. (2012). A History of Spaces: cartographic reason, mapping and the geo-coded World. Routledge. doi:10.4324/9780203351437 Raifer, M., Troilo, R., Kowatsch, F., Auer, M., Loos, L., Marx, S., ... Zipf, A. (2019). OSHDB: A framework for spatio-temporal analysis of OpenStreetMap history data. Open Geospatial Data. Software and Standards, 4(1), 1–12.

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Riemann, R. (2021). OSM data: Privacy Risks and GDPR compliance. Conference talk available at https://2021.stateofthemap.org/sessions/RJZRZ8/ Wu, Z., Li, H., & Zipf, A. (2020, July). From Historical OpenStreetMap data to customized training samples for geospatial machine learning. Proceedings of the Academic Track at the State of the Map 2020 Online Conference.

ADDITIONAL READING Das, M., Hecht, B., & Gergle, D. (2019, May). The gendered geography of contributions to openstreetmap: complexities in self-focus bias. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-14). 10.1145/3290605.3300793 Lin, Y. W. (2011). A qualitative enquiry into OpenStreetMap making. New Review of Hypermedia and Multimedia, 17(1), 53–71. doi:10.1080/13614568.2011.552647 Palen, L., Soden, R., Anderson, T. J., & Barrenechea, M. (2015, April). Success & scale in a dataproducing organization: The socio-technical evolution of OpenStreetMap in response to humanitarian events. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 4113-4122). 10.1145/2702123.2702294 Sehra, S. S., Singh, J., & Rai, H. S. (2017). Using latent semantic analysis to identify research trends in OpenStreetMap. ISPRS International Journal of Geo-Information, 6(7), 195. doi:10.3390/ijgi6070195 Shiau, S. J., Huang, C. Y., Yang, C. L., & Juang, J. N. (2018). A derivation of factors influencing the innovation diffusion of the OpenStreetMap in STEM education. Sustainability, 10(10), 3447. doi:10.3390u10103447

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Chapter 32

Social Semiotics for Social Media Visuals: A Framework for Analysis and Interpretation Hossam Elhamy Zayed University, UAE

ABSTRACT The social semiotics approach examines the meaning-making process in order to demonstrate how meaning is constructed in social actions and contexts. The rising interest of researchers in social media and its widespread use in society have both highlighted new challenges for data analysis. Social semiotics can provide a deep understanding of the visual grammar of the social media meaning-making process by assuming that this process is considered a social practice. The main objective of this chapter is to guide researchers and enable them to use the social semiotic approach as a research tool for the analysis of visuals in the social media environment. The chapter introduces the key elements, principles, assumptions, and rules of using the social semiotics approach in the analysis, understanding, and interpretations of social media visuals and how to explore the role played by visual elements in the meaning-making process in a social media within a specific social context.

INTRODUCTION The social semiotics approach examines the meaning-making process in order to demonstrate how meaning is constructed in social actions and contexts (Van Leeuwen, 2009: p. 6). The focus of this chapter is to explain the possibilities of utilizing social semiotics in analyzing and interpreting the meaningmaking process through visual elements on social media within a specific social context. The content of the chapter is organized around social semiotics as an approach and research strategy, beginning with an explanation of the meaning of semiotics, sign, and semiotic resources. Some insights are given as

DOI: 10.4018/978-1-7998-8473-6.ch032

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 Social Semiotics for Social Media Visuals

well on social semiotics principles, assumptions, and basic questions with an emphasis on visual social semiotics and multimodal visual semiotics. The chapter also discusses aspects and issues related to the social media visuals from the semiotic perspective, showing how social media are considered, semiotically, an environment for communicating multimodal and multilayered content. During the scientific contributions made in the field of social semiotics, some analytical frameworks were developed. This chapter presents these analytical frameworks with an emphasis on the aspects that can be applied to the visual elements of social media. The analytical frameworks addressed in the chapter include the tripartite model that divides meaning generated by visual elements into three categories: representational, interactional, and compositional meaning. Another tripartite division of a sign into icon, index, and symbol is also discussed. Finally, this chapter sheds light on an important aspect of the analysis of visual elements in social semiotics, which is the analysis of connotation and denotation meanings. Within this point, the model of the Peircean triad is discussed.

Semiotics, Sign, and Semiotic Resources The end of the nineteenth century to the beginning of the twentieth witnessed the birth of two major ideas that characterized the beginning of contemporary semiotics. The semiotic approach was developed through the efforts of Swiss linguist Ferdinand de Saussure (1857–1913) and the American philosopher and mathematician, Charles Sanders Peirce (1839-1914) (Abdalla Mikhaeil & Baskerville, 2019, p. 6; Moerdisuroso, 2014, p. 81). The term ‘semiotics’ is derived from an ancient Latin term “semio,” which is absorption of the Greek “semeion,” that refers to a sign. This term is related to an ancient use in medicines; a diagnosis act named the semeiosis process. The term ‘semiotics’ means the signs and the rules, which govern their use that the semiotician should address (Moerdisuroso, 2014, p. 81). ‘Sign’ is the key concept of semiotics. One of the best definitions of semiotics is that of Ferdinand de Saussure, who defined semiotics as the science that studies the life of signs within society; and he called it semiology (Van Leeuwen, 2009, p. 3). Semiotics pays great attention to the context, since the context for meaning according to semiotics is a system consisting of signs (Hatt & Klonk, 2006, p. 200). Pierce suggested an intermediating formulation for the sign; this simply means that the sign mediates between what it represents, its object, and the interpretant, which is the effect the sign makes upon the person interpreting it (Jappy, 2013, p. 4). From a social semiotics perspective, signs are always newly made in a specific environment and according to the interests of the sign makers. According to this concept of the sign, signs are made by a sign-maker who brings meaning into a kind of apt-conjunction with a form, a selection, or a choice shaped by the sign maker’s interest; interests that are formed, subsequently, by our environment and circumstances (Mirsarraf et al., 2017, p. 3). According to Saussure, language is a system of signs, and there are not natural signs. This means that signs do not refer to objects or events in the world, but they are conventions. The language is a code with specific rules. The elements of the code are signs. Any sign is composed of a signifier and a signified (Hatt & Klonk, 2006, p. 202). Traditionally, semiotic resources have been referred to as “signs.” Van Leeuwen (2009) defined the semiotic resource as any product or action that humans can use to communicate, whether it is created or produced physiologically (such as voice apparatus, facial expressions or gestures, etc.) or technologically (such as using pen, ink, or paper, machine, fabrics, or computers, etc.). 549

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Van Leeuwen points out that semiotic resources are not restricted to speech, writing, and picture making. Everything we make or do can be made and done in different ways and, therefore, allows the articulation of different cultural and social meanings (Van Leeuwen, 2009, pp. 3-4). Just as any other semiotic resource, visual resources in social semiotics perform simultaneously several meta-functions in order to convey meaning (Stoian, 2015, p. 3). In social semiotics, resources are investigated as signifiers, observable acts, and objects that occur in a domain of social communication and have both theoretical semiotic and actual semiotic potential. Theoretical semiotic potential refers to all past and potential uses of signs. Actual semiotic potential refers to that kind of past uses that are considered relevant by the users of the resources, and by such potential uses that may be uncovered by the users based on their interests and needs. This can be by either using those resources according to rules or by leaving them free in some way in generating the utilization of resources (Van Leeuwen, 2009, p. 4). Semiotic resources are shaped by how people use them to make meaning. This means that the way humans employ semiotic resources to produce meaning shapes them. This is the purpose of semiotic resources: to construct meaning based on people’s intentions. According to social semiotics, every sign serves three functions simultaneously: they express something about the world (ideational metafunction), position people in relation to each other (interpersonal metafunction), and form connections with other signs to produce coherent text (textual metafunction) (Bezemer & Jewitt, 2009).

Social Semiotics Approach As noted by Wong (2019), since the millennium’s turn, visual imagery has been addressed as one of the major modes of communication along with written and spoken communication. This has led to scientific attempts to explore the structure of visual signs and the meaning-making process that takes place through these visual elements. Ever since it was founded in the mid-1980s and fully developed in the early 2000s, social semiotics has been a social theory for understanding meaning and communication in which semiotic resources are used in different ways by sign makers for serving specific social requirements needed in a given social context (Wong, 2019, p. 2). While structuralists seek to describe the overall organization of sign systems as “languages” and engage in a search for “deep structures’” underlying the “surface features’” of phenomena, social semiotics has moved beyond the structuralist concerns with the internal relationships between parts of a self-contained system. Semiotic theory is sometimes allied as well with a Marxist perspective, which stresses the role of ideology (Chandler, 2007). The fundamental theoretical bases in social semiotics research developed from the systematic functional linguistics (SFL) work of linguist Michael Halliday and his colleagues. SFL emphasized the importance of focusing on the relationship between the linguistic and the social context. The structure of language has evolved and will continue to evolve as a result of the meaning-making processes or functions they serve within a particular social system or the culture in which they are used (Unsworth, 2008). According to Halliday’s theory of metafunctions, language performs three metafunctions simultaneously, namely ideational metafunctions, interpersonal metafunctions and textual metafunctions. Kress and Van Leeuwen (2020) extended the same metafunctions to visual social semiotic resources, renaming them representational, interactive, and compositional metafunctions. They proposed that the visual, like all semiotic modes, must serve a variety of communicative (and representational) requirements in order to function as a communication system (Stoian, 2015, p. 24). 550

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Social semiotics is an approach that attempts to examine the meaning-making process in order to demonstrate how meaning is constructed in social actions and contexts (Van Leeuwen, 2009, p. 6). Social semiotics is a method or research strategy that enables researchers to investigate the systematic relationships between social reality and signs, texts, and discourses. This kind of research is considered a part of critical theory in that it accepts that language constructs meaning in its own categories and meanings, therefore, are ambivalent and unstable. On another hand, social semiotics attempts to formulate a theory enabling researchers to examine the dynamic interrelations between signs and social reality or social context (Meinhof, 1993). According to the social semiotic approach, the meaning given by a “sign” is not made by the mind, but by social actions in the community. Meanings do not arise in the individual; meaning is a “super individual” and “intersubjective” action, and consciousness. The functionality of any semiotic system is based on the social understanding of meaning. Halliday argued that the semiotic system could be considered as the fourth order of complexity besides the physical, biological, and social systems (Andersen, 2015, p. 2). Some researchers are interested in explaining the differences between traditional formal semiotics and the social semiotics approaches. Mirsarraf et al. (2017) pointed out that formal semiotics assumes that the relevant meanings are only found in the text itself, to be extracted and decoded by the analyst using an impersonal and neutral coding system that is universal for users of the code. In contrast, social semiotics cannot presume that texts create exactly the meanings and effects that their authors hope for; it is precisely the struggles and their uncertain outcomes (Mirsarraf et al., 2017, p. 4). Whereas traditional semiotics is primarily concerned with a systematic examining of systems of ‘signs’ themselves, social semiotics is concerned with formal semiotics, and goes on to ask how people use those “signs” to (re)construct or (re)produce the life of their communities (Harrison, 2003, p. 48). Just as the emphasis in linguistics changed from the ”sentence” to the “text” and its “context,” and from “grammar” to “discourse,” the emphasis in semiotics shifted from the ‘sign’ to how people use semiotic “resources” to generate visual communicative materials or events, and interpreted them in the context of a specific social situation (Van Leeuwen, 2009). While general semiotics views the text as a fixed entity, social semiotics focuses on the dialectical conflict between text and its semiotic systems, which always present a specific semiotic action, namely discourse. The discourse, according to Van Leeuwen, is defined as a social construction of knowledge of some aspects of reality. The discourse is developed in a particular social context and in a consistent way with the interests of the people; either in large contexts such as multinational contexts, or small contexts, such as a family, as well as in the context of formal institutions such as media organizations, schools, and informal context, such as a conversation between friends (Moerdisuroso, 2014, p. 85; Van Leeuwen, 2009).

Social Semiotics Principles and Assumptions Van Leeuwen (2009) argued that the basic assumptions of the social semiotics approach are to investigate semiotic systems and resources in culture, as well as how they are used and socially regulated in multimodal text composition in various social practices and behaviors. There are some principles that make social semiotics a new and distinctive approach for applying semiotic analysis. Wong (2019) pointed out that there are four interconnected assumptions upon which the social semiotics approach is based. The first assumption is that the meaning-making process is always 551

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multimodal, involving a kind of multiplicity of modes, such as images, postures, gestures, actions, music, color, writing, or speech. All those modes have the probability to contribute equally in meaning making The second assumption central to the social semiotics approach is that semiotic resources are used by people in a particular “social context.” This social context shapes the semiotic resources or signs used for meaning making and how these resources are selected, formed, and employed in the production of meaning. Therefore sign-making is conceived as a social process. The third assumption includes the notion of “the motivating sign.” This assumption assumes that when making signs, people bring together the available form that is likely to express the meaning they need to represent at a certain moment in a particular social context. Accordingly, the relationship between form (i.e. signifier) and meaning (i.e. signified) are not arbitrary, but motivated. Finally, the fourth assumption for the social semiotics approach is that it is the sign maker’s interest that guides those makers’ selection of semiotic resources. The word “interest” here is defined as the articulation and realization of an individual’s relation to an event or object within the context of interaction with other factors and components of a situation (Wong, 2019, p. 3). Van Leeuwen (2009) proposed a viewpoint for dealing with semiotic resources, suggesting that the semiotician usually does three things: 1. Gathers, documents, and systematically catalogs semiotic resources, including their history. 2. Explores how these resources are used in specific social, cultural, historical, and institutional contexts, and how people discuss them in these contexts, such as planning them, teaching them, justifying them, critiquing them. 3. Contributes to the exploration and development of new semiotic resources, as well as modern uses of existing semiotic resources. As illustrated in Figure 1, in the social semiotics approach, the basic questions are about meaning, the meaning-making process, semiotic resources used for making that meaning, social agents as meaning makers, and the features of context or environment in which they act (Bezemer & Kress, 2016, p. 16). Jewitt and Oyama (2004) argued that social semiotics of visual communication involve two levels of descriptions: 1. The description of semiotic resources, which includes what can be said and done with visuals and images, how people say and do things with visuals, and how these visuals may be studied and interpreted. 2. Describing how semiotic resources are used in a specific domain. On this level of description, researchers attempt to study the context, the sign system, and their role in the meaning-making process (Jewitt & Oyama, 2004, p. 134). As stated by Harrison (2003), social semioticians apply three important principles (Figure 2) when analyzing a semiotic system: Principle 1: Social semiotics scientists believe that all people see the world through signs. From the social semiotic perspective, there is a kind of “internal representation” of “signs” that involves a highly sensitized understanding of the sign conventions in a communicator’s language semiotic system. According to this principle, communicators or sign makers typically use intuition to imagine the audience and utilize their internal representation of the audience as a guide for constructing visuals. 552

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Figure 1. Basic questions in social semiotics approach

This sensitivity enables signs makers to (re)produce their communities’ discourse in ways that draw the attention of readers or viewers. Principle 2: The meaning of signs is generated by people, and these people are affected by the life of their community and its sociocultural context. Signs are generated by human beings and certainly influenced by societies. Accordingly, signs will have different meanings in different social and cultural contexts.

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Principle 3: Semiotic systems give people an opportunity to use a variety of resources that help in meaning making. From the social semiotics perspective, sign makers can choose a sign or another action to express meaning, which may give sign makers an amount of power to use signs in diverse and unconventional methods. This power of the communicator could affect, or even alter, meanings (Harrison, 2003, p. 48). Figure 2. The three principles of social semiotics

Social semiotics is interested in investigating: 1. How people use semiotic resources, both to create communicative artifacts as well as to interpret them in the context of certain social situations and practices 2. Examines and compares semiotic modes 3. Studies ways in which semiotic resources are regulated in specific locations and practices 4. Practice of analysis and observations to discover new semiotic resources 5. Reads all artifacts as texts 6. Does not study what signs stands for but how they used (Pimenta & Natividade, 2013). As stated by Jewitt and Oyama (2004), social semiotics is not an end to itself; it is a method or a tool that could be used in critical research, as semiotics resources become meaningful just when we begin asking questions about them. Van Leeuwen draws attention as well to the fact that social semiotics is a form of inquiry that does not offer any ready-made answers. It offers ideas for formulating questions and ways of searching for answers Van Leeuwen (2009, p. 1).

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Visual Social Semiotics Visual social semiotics is a new field of study (originating in the 1990s) and has been defined by Jewitt and Oyama (2004) as the description of semiotic resources, what can be said and done with images and other visual means of communication as well as how the things people say and do with images can be interpreted (Harrison, 2003, p. 50). According to Leeuwen (2001), visual social semiotics approach key questions including: the question of representation (What do visuals represent and how?), and the question of the hidden meaning of images (What ideas and values do the people, things, or places that are represented in visuals, stand for?). Visual social semiotics stresses that an image is not the result of a singular, isolated, creative activity, but is itself a social process. As such, its meaning is a negotiation between the producer and the viewer, reflecting their individual social, cultural, political beliefs, values, and attitudes (Harrison, 2003).

Multimodality and Visual Semiotics Multimodal visual semiotics presents a methodological framework for understanding, analyzing, and interpreting how several modes of signs, such as texts and images, work together to make meaning for readers (Leeuwen, 2001) In simple words, Wong (2019) explained the idea of multimodality in social semiotics, saying that the role played by multimodality in meaning-making lies in the process of exploring different potentials for providing means of expressing views, positions, attitudes, or facts; enabling the making of what is best appropriated to a specific task or need (Wong, 2019, p. 3). Simpson and Archer (2017) suggested that using social semiotics to investigate “signs,” social resources, and visual communication has led to a better understanding of social context and its impact on meaning making, as well as a clearer awareness of the multimodal aspect of meaning making. Multimodal social semiotics goes to the heart of why people realize meaning in terms of their interest or motivation, as well as how they realize meaning, in terms of their design of texts. This perspective positions text making as the creation of meaning-making ensembles and places meaning-making decisions within social, institutional, and technological contexts. Multimodal social semiotics also shed light on how symbolic forms, or modes, are shaped by semiotic resources. It allows an investigation of the ways individual sign-makers transfer their knowledge of resources from one context to another, in completely different contexts (Simpson & Archer, 2017). It is important to note that the integration of social semiotic and multimodal studies with the methods of investigation of social media platforms may help us to understand the technological mediation of discourse in such a digital context.

SOCIAL SEMIOTICS: THE ANALYTICAL FRAMEWORK During the scientific contributions presented in the field of social semiotics, some analytical frameworks were developed that will be presented in detail in this section of the chapter.

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Representational Meaning, Interactional Meaning, and Compositional Meaning As an analytical framework, visual social semiotics was developed by Kress and Van Leeuwen (2020). In their book, Reading Images, they offered a model of three components for image social semiotics analysis, including: representational meaning, interactional meaning, and compositional meaning (Kress & Van Leeuwen, 2020).

Representational Meaning The representational meaning is about the people, places, and objects within an image—the representational meaning is related to represented participants, and answers the question, “What is the picture about?” (Harrison, 2003, p. 50). The representational dimension relates the represented participants to each other. The representational meaning is communicated mainly by the participants depicted (abstract or concrete participants, such as people, places, or things). Jewitt & Oyama (2004) suggest that narrative representations are related to “happenings” or “doings” of participants during the occurrence of events, actions, or operations. On the other hand, conceptual patterns describe the participant in terms of their stability, timeliness, or more generalized nature. According to conceptual representation patterns, participants are not presented, and therefore analyzed, as doing an action, but as being something, referring to something, meaning something, belonging to some category, or having certain identities, components, or characteristics. Representational meaning as a category of social semiotic analysis is divided into narrative representation and conceptual representation. In the narrative representation, the represented participants are depicted in the process of doing something and involved in actions, events, or processes of change. Action processes, reactional processes, speech processes, mental processes, and conversion processes are different examples of narrative processes. The conceptual representation deals with the generalized, stable, and timeless positions of participants (Bakhtiari & Saadat, 2015, p. 12). Stoian (2015) argued that narrative and conceptual structures could appear individually or together, the same as simple or complex sentences in the language. As stated by Jewitt and Oyama (2004) social semioticians should be aware that the communicator’s decision to represent anything in a narrative or conceptual manner is very important, since it allows an opportunity to understand the discourses presented (Jewitt & Oyama, 2004, p. 141). Harrison (2003) proposed some core questions for analysis of the representational meaning in visual social semiotics. These questions included: (1) Who are the represented participants in the image, including both human and non-human objects?; (2) Does the image contain any vectors that imply action? If there is an action, what sort of meaning does this action say?; (3) Are the humans represented participants looking at each other, creating a kind of eye contact and eyeline vectors? If so, what does this reveal about the background of those people?; (4) If there are no vectors, what is the visual element trying to tell related to social and cultural context? and; (5) Is the visual element complex, with multiple processes incorporated in it? If this is the case, how do these embedded processes contribute to the overall comprehension and interpretation of that visual element? (Harrison, 2003, p. 52).

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Interactive Meaning This dimension deals with the relationship between the represented participants and the interactive participants; in other words, the focus of the interactive meaning category is on the relationship between the image producer and the viewers. The interactive meaning is realized by the gaze, size of the frame, perspective, and horizontal and vertical angle (Bakhtiari & Saadat, 2015, p. 12). Although the emphasis of semiotics is on the reading of visual elements rather than their making, this does not mean it is concerned with the private responses of viewers. Because all meaning-making processes depend on the codes and rules that govern what we communicate or represent, the viewer in the semiotic analysis is considered as the channel or the conduit through which meanings flow. Therefore, the viewer here is not a reader, but a function (Hatt & Klonk, 2006, p. 200). Hatt and Klonk (2006) stated that semiotics differs from many other approaches in that it focuses away from the communicator and investigates the “system,” which enables individual expressions and interpretations. The French critic Roland Barthes described this as ‘the death of the author” and “the birth of the reader.” Interactive meaning sub-categories include the followings: 1. Image act and gaze: The image act involves the eyeline of the represented participants in relation to the viewer. Participant gaze refers to the direction of the gaze of the participant(s) in the photo. The act in a photo can be emphasized as well by facial expressions or gestures. 2. Contact: This refers to the way in which the subject is interacting with the viewer. 3. Distance: This category refers to social distance and intimacy. Social distance is determined by how close represented participants in an image appear to the viewer, thereby resulting in feelings of distance or intimacy. The distance refers to the distance between the participant(s) in the photo and indicates the distance between the participant(s) and the camera lens as well. The analysis of the distance focuses on participants relationships, in order to identify whether a participant is alone or accompanied in the photos and if a participant is accompanied, what is the level of intimacy or closeness expressed in the photo? This sub-category includes also the social relationship with the viewer as for the frame, size of the photo, and the perceived distance of the subject in the image; Closeup (intimate), Mid Shot (social), and Long Shot (Impersonal), As a result, changing frame sizes, such as close-up, medium, and long, alter distance. These dimensions apply not only to persons, but also to things, structures, and landscapes. (Harrison, 2003, p. 53; Oyama et al., 2004; Stoian, 2015). 4. Point of view: Participants can be shown from numerous perspectives, each representing and expressing a different relationship. There are two kinds of images: subjective, which convey everything from a specific point of view dictated by the image-maker, and objective, which depicts everything there is to know about the subject (Kress & van Leeuwen 2020). From another analytical perspective, there are three dimensions to the interactive meaning of images: image act, social distance, and point of view. The image act is linked to the gaze direction of the represented participants, which can be directed at the viewer (demand) or not (offer). Demands are considered to develop an imaginative relationship with the viewer, addressing her/him directly (Stoian, 2015, p. 26). Therefore, images are divided into two categories according to the presence or absence of gaze. The first category is “demand,” in which the represented participants demand something from the 557

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viewers by looking in a straight way at their eyes. The second category is “offer,” in which the represented participants do not look directly at the viewers and are offered to the viewers as an object or something to be seen by the viewers. (Bakhtiari & Saadat, 2015, p. 12; Stoian, 2015, p. 26). Demands are considered to establish an imaginary relationship with the viewer since they address her/him directly. The act can also be emphasized by facial expressions and gestures. Offers, on the other hand, address the viewer indirectly, as there is no “gaze” contact between participants. Both demands and offers depict the represented participants impersonally as items of information or objects for contemplation. It is important to highlight that the choice of image act can suggest different relations between participants, such as engagement or detachment (Stoian, 2015, p. 26). The size of the frame, ranging from a very close shot to a very long shot, is used to establish varying social distance between the represented-interactive participants. Perspective refers to subjective or objective representations and specifies the point of view. The horizontal angle deals with the oblique or frontal angle between the represented participants and the viewers, which brings about detachment or involvement, respectively. The vertical angle may also manipulate the power relations between the interactive participants and the represented participants by depicting the latter from the high angle, low angle, or eye level (Bakhtiari & Saadat, 2015, p. 12).

Compositional Meaning Compositional meaning deals with how the representational and interactive elements are made to connect to one another and how they are connected to make up a meaningful whole. Information value, salience, and framing are the three key elements that can manipulate compositional meaning in social media images (Bakhtiari & Saadat, 2015, pp. 12–13). Stoian (2015) argued that all these elements and aspects work together to determine the reality encoded; the interaction and relationship generated between participants and the meaning created visually. Informational Values Different zones of an image have different informational values. While the right zone is the area of the new, the left is the zone of already known information with which the viewer is supposed to be already familiar. While the top section shows the ideal information, the bottom section is the place of the real information. The center is the place of the core, and the margin is the zone of dependent parts. Salience Salience refers to such factors as size, color contrasts, and placement in the visual field of pictorial elements, which are intended to attract the viewers’ ‟attention.” Size, tone, sharpness of focus, color contrast, positioning in the visual field, perspective, and specific cultural factors are all examples of visual clues or that indicate salience (Stoian, 2015, p. 28). Framing Another system that ties representational meaning to interactive meaning is framing. The term “framing” refers to elements of the composition that can either be given separate identities or represented as relating to each other, which means that the term “framing” reflects the “connection” or “disconnection”

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of visual elements (Jewitt & Oyama, 2004, p. 149). Framing can connect or disconnect the represented participants by concrete frame lines, white space between elements, and dividing lines produced by means of other devices (Bakhtiari & Saadat, 2015, pp. 12–13). During analysis, the framing category brings attention to the connection or disconnection of the elements in the image. The elements in an image or page can be either connected or disconnected by frame lines. In addition, framing affects the sense of continuity and discontinuity in an image (Stoian, 2015, p. 26). Modality Kress and van Leeuwen incorporated into their proposed model of visual design the concept of modality, arguing that modality is constructed by a complex interaction of markers and cues (Stoian, 2015). Kress and Van Leeuwen (2020) noted that there are certain means of visual expression that, when increased or decreased, express an increase or decrease in the degree to which an image or a photo is to be taken as being real. Kress and Van Leeuwen use the terms realism and naturalism in their examination of visual modality. They defined “realism” as a representation made by a specific group based on the practices by which the group is formed and characterized; in other words, the representation that is created by a specific group. Kress and Van Leeuwen (2020) suggested that these are what they call modality markers. They argued that modality is expressed visually through resources indicating increases or decreases in as how real the image should be taken. The gradable modality markers include: 1. Color saturation: a scale running from full-color saturation to the absence of color, i.e. black and white. 2. Color differentiation: a scale running from a maximally diversified range of colors to monochrome. 3. Color modulation: a scale ranging from completely modulated color to plain, unmodulated color. 4. Contextualization: a scale ranging from the absence of background to the most fully articulated and detailed background. 5. Representation: a scale ranging from maximum abstraction to maximum representation of pictorial detail. 6. Depth: a scale ranging from the absence of depth to maximally deep perspective. 7. Illumination: a scale ranging from the fullest representation of the interaction of light and shade to its absence. 8. Brightness: a scale of brightness ranging from a maximum number of distinct degrees of brightness to only two degrees, such as black and white, or dark grey and lighter grey (Brady, 2015; Kress and Van Leeuwen, 2020; Maagaard, 2015). Representations and judgements of modality depend on the purpose of the representation, the needs of the users and conventions of representation as they are constrained by the genre and the medium. Thus, the modality value of a given configuration depends on the kind of visual truth which is preferred in the given context. Kress and Van Leeuwen adopted the concept of “coding orientation” and proposed four coding orientations as principles derived from the needs of specific social groups in specific contexts. 1. The naturalistic coding orientation, which pertains to the commonsense way we see the world as defined by the standard of photography. The naturalistic, which could be found in photographs,

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relies on the correspondence between the object of representation and how it would be viewed by the naked eye. 2. The sensory coding orientation, in which naturalistic standards of modality are exceeded in order to appeal to the senses. Sensory modality depends upon the ability of the image to awaken sensory responses. 3. The abstract coding orientation, which abstracts from the naturalistic standard in order to appeal to an underlying truth. The abstract, as in works of art or science, is evaluated based on its fidelity to abstract ideas or essential qualities of phenomena. 4. The technological (or scientific) coding orientation, which concerns the degree to which a representation can be used as a blueprint. The technological evaluates the modality based on the representation’s effectiveness as a blueprint for the user. Coding orientations mean that modality markers mentioned above in isolation are not enough to evaluate truth value or credibility, but that the purpose and the context are necessary factors as well. For example, a high degree of color saturation may mark low modality in the naturalistic orientation, but a higher one in sensory or abstract texts (Brady, 2015; Kress and Van Leeuwen, 2020; Maagaard, 2015). The figure below identifies categories of analysis in visual social semiotics as proposed by Jewitt and Oyama (2004).

Inventorising Semiotic Resources In order to conduct a social semiotic analysis, the researcher should be familiar with the various possible articulations of a semiotic resource, as well as the potential meanings given to these articulations. These are sometimes referred to as system networks and organized as schematic diagrams by social semioticians who have examined certain semiotic resources. The system network diagram for “point of view,” i.e., the position (e.g., low or high angle) that a viewer has in regard to the represented participant in a photo, can be seen in the following figure presented by Jewitt and Oyama (2004). A social semiotic researcher who is interested in analyzing the usage of a certain semiotic resource needs to develop an inventory of the various articulations or permeations of such semiotic resource; that is, its semiotic potential. According to Van Leeuwen (2009), the process for constructing an inventory requires that the researcher must first develop a collection of different examples of the semiotic resource in use. Once the different kinds of framing are determined from the collection, the social semiotic researcher establishes a more formal systematic inventory of the framing. The different articulations in the inventory are then given names or labels that represent their general essence and differentiates them from the other articulations. The inventory can then be summarized as a system network diagram, as shown in Figure 4, or in the form of a list (Leeuwen, 2004; Porteous, 2020).

The Tripartite Division of a Sign: The Three Categories of Visuals An important point in the analysis of visual social semiotics is to identify and understand the three categories to which the visual element being analyzed may belong. There is a tripartite division of a sign that has been widely accepted by semioticians who divide a sign into an icon, index, and symbol. This division is called “Peirce’s Trichotomy.” Peirce (1965) proposed that there are three kinds of signs that 560

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include icons, indexes, and symbols (Hatt & Klonk, 2006, p. 213). As Peirce suggested, icons signify by resemblance, indexes signify by cause and effect, and symbols signify on the basis of the convention. Figure 3. Categories of analysis in visual social semiotics. (Jewitt & Oyama, 2004)

Icon A visual element becomes iconic if it bears a resemblance or similarity to what is already known or conceived about an object or person. Peirce pointed out that an iconic sign represents its object by its similarity. A sign is an icon for an object insofar as it resembles that object and is utilized as a symbol for it. The icon is considered a mode of representation in which the “signifier” is perceived as imitating or resembling the “signified” (recognizably looking, sounding, feeling, tasting, or smelling like it)—being similar in possessing some of its characteristics, such as a portrait, a cartoon, a scale-model, onomatopoeia, metaphors, “realistic” sounds in a program, or sound effects in a radio drama. Photographs and classical paintings are obviously icons as they visually resemble their objects.

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Figure 4. System network for the semiotic resource “point of view.” Square brackets refer to “either-or” choices, whereas the curly brackets refer to “both-and” options (Jewitt & Oyama, 2004)

Index A visual element is considered an index if it is recognizable, not because of any similarity to a person or object, but because we recognize a kind of relationship between the visual element and the concept it represents. Indices always point to, reference, or suggest something else. A sundial or a clock indicates the time of day... a rap on the door is an index; anything that focuses the attention is considered an index. The index is a mode of representation in which the signifier is not arbitrary but is directly connected in one way or another way (either physically or causally) to the signified. This relationship can be observed or inferred, e.g., “natural signs” (such as smoke, thunder, echoes, footprints, flavors, and

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non-synthetic odors), medical symptoms (pain, pulse-rate, a rash), measuring instruments (weathercock, clock, thermometer, spirit-level). Also, “signals” (a knock on a door, a phone ringing), pointers (a pointing “index” finger, a directional signpost), recordings (a photograph, video or television shot, a film, an audio-recorded voice), personal “trademarks” (handwriting, catchphrase), and indexical words (“here,” “there,” “this,” “that”). While symbols cannot be signs in the absence of an interpreter, indices cannot be signs in the absence of their objects (therefore, no interpreter or a reader is necessary). For example, there is a piece of mold with a bullet hole in it as an indication of a shot. Without the shot, there would have been no bullet hole, but there is a hole, whether anyone has the sense to attribute it to a shot or not.

Symbol The symbol is a type of sign that acquires its meaning through usage or convention rather than resemblance or indexical trace. A symbol is a mode of representation in which a signifier does not resemble the signified, but which is purely conventional or fundamentally arbitrary. Therefore, the relationship must be learned, e.g., language in general, in addition to specific languages, alphabetical letters, punctuation marks, words, phrases, sentences, numbers, Morse code, national flags, and traffic lights. A visual element is considered a symbol when it has no visual or conceptual connection to a person or an object. We know the meaning of a visual element only because of convention, which is something we have been taught. A word, for example, is a symbol because it does not like what it stands for, nor does it have an indexical relationship to what it represents. The word “rose,” for example does not look like a rose or bear any relationship to the concept of a rose, therefore it should be considered, from social semiotics perspectives, as a symbol. Languages are possibly the most important symbolic sign systems; each ordinary word, such as “bird,” “marriage,” or “family” is an example of a symbol. In addition, every alphanumeric character on a computer keyboard is a symbol, as are those things not specifically alphabetic or numeric, such as $, &, %, @, #, and so on. Unlike icons or indices, symbols are not signing without an interpreter or a reader. As a completely abstract system, the tripartite division of a sign, icon, index, and symbol applies equally to any and all media or form of communication (Berger, 2011; Chandler, 2007; Harrison, 2003, p. 50; Hatt & Klonk, 2006, p. 213; Huening, 2006; Peirce, 1965).

Analyzing Connotation and Denotation Meanings Exploring the denotation and connotation meaning of the signs is one of the essential steps in the semiotic analysis. Denotation meaning refers to the literal meaning of a term or subject. It is descriptive. On the other hand, connotation focuses on the cultural meaning that becomes applicable to the sign (Berger, 2011, p. 55). The analytical distinction between denotation and connotation meanings is important for the interpretation in semiotic analysis. Denotation and connotation are two levels or layers of meaning based on signs and codes, and are culturally relative, although denotation is less related to culture than connotation (Ali & Aslaadi, 2016, p. 55). Leeuwen (2001) pointed out that denotation could be seen and analyzed in visual semiotics as the first layer of meaning, of ‘what or who is depicted in the image?”. Connotation meaning is a powerful kind of meaning. The connotation is considered the second layer of meaning. It focuses on: what ideas 563

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and values are expressed through what is represented, and through the way in which it is represented? (Ali & Aslaadi, 2016; Leeuwen, 2001, p. 94). Leeuwen and Jewitt (2004) believe that denotation does not cause a problem during the analysis, because it is close to the analogous perceived reality, as is the case in a photograph. In connection with the distinction between denotation and connotation meanings is what the French literary theorist, essayist, philosopher, critic, and semiotician Roland Barthes referred to as myth. Barthes pointed out that we usually associate myths with classical fables. However, for Barthes, myths were the prevailing ideologies of our time. Barthes concluded that the orders of signification called denotation and connotation combine to form the dominant ideology, which has been defined as a third order of signification (Chandler, 2007). Such a process creates the tremendous illusion that denotation is a truly literal and universal meaning that is not ideological in any way, and that the connotations that appear most clear to individual interpreters are just as “natural.” According to Chandler (2007), Louis Althusser (1918–1990) believed that when one first learns denotation meanings, they are also being positioned within ideology by learning dominant connotation meanings at the same time. As a result, while theorists may find it analytically advantageous to distinguish connotation from denotation, such meanings cannot be easily separated in practice. Most semioticians believe that no sign is completely denotative or lacks connotation, and that there can be no neutral, objective description that is free of an evaluative component.

Peircean Triad In relation to analyzing connotation and denotation meanings, semiotics, Peirce (1965) was concerned with studying primary deep meaning from human action, which requires looking at the sign during analysis as a tool for reaching deep meanings, connotations, and systems in the process of meaning making. Peirce created the three Peircean categories, which are: firstness, secondness, and thirdness. He described firstness as the mode of being that is such as it is, without reference to anything else. “Secondness” is the mode of being, which is such as it is with respect to a second, but regardless of any third. “Thirdness” is referred to as the mode of being, which is such as it is in bringing a second and third into relation to one another. In other words, Peirce proposed that firstness is anything that exists in itself, secondness must be related to something else, and thirdness requires a more complex relationship, whether it is a relation between three things, a relation between relations, or both at the same time (Sonesson, 2013, p. 306).

Social Media: A Semiotic Perspective The rising interest of researchers in social media and its widespread use in society have both highlighted new challenges for data analysis. Social media from semiotics perspectives are considered an environment for the dissemination of multimodal, multilayered content (Mirsarraf et al., 2017, p. 3). Abdalla Mikhaeil & Baskerville (2019) argued that, semiotically, social media communications are incredibly complex in content, form, and meaning, in addition, it offers us multimedia abundant data consisting of multiple layers of meanings. Social media consists of digital texts related to discussion threads (such as posts and comments), photos, videos, hypertexts, emojis (pictograms, logograms, ideograms, or smileys), likes, etc. Furthermore, continuous innovations in social media platforms extend our modes and capabilities of representation of our social environment in different ways. 564

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In addition, social media has made it significantly easier to build and share content other than written texts. These digitally enabled social environments broaden the definition of digital messages. The process of meaning making on social media has two key features: (1) the multimedia nature of data (e.g., words, images, videos, hypertexts) and their integration, and (2), the overlapping, multiple layers of meanings generated when senders and receivers interact (Abdalla Mikhaeil & Baskerville, 2019). Instead of texts from a few known sources, social media data are characterized by representational complexity: multimedia data (e.g., photos, texts, videos, external links) brimming with interactions and thus, multiple meanings. Because data from social media are representationally complex communications, such data should be analyzed accordingly (Abdalla Mikhaeil & Baskerville, 2019). As stated by Poulsen et al. (2018) social media is incorporated into everyday social practices, offering users many types of semiotic resources from which they can (re)construct these resources into multimodal meaning possibilities on the social media platforms, such as posts, written text, images, emoticons, gifs, and so on (Poulsen et al., 2018, p. 593). Moreover, social media is characterized by multiple layers of meaning. Researchers now agreed that there is a kind of overlapping layers of meanings in the social media environment. None of the digital texts is a stand-alone text. Social media are highly interactive media, as the contents generated by users are co-produced by more than one user. Users can create content for an intended audience (e.g., “Friends” on Facebook) or for an unknown audience (e.g., public pages on different social media platforms). In addition, users can easily address multiple other users, the audience can reply, comment, evaluate, and compliment the content initially posted. Therefore, in social media, there are layers of representations of meanings, like other phenomena (Abdalla Mikhaeil & Baskerville, 2019). Abdalla Mikhaeil & Baskerville (2019) conclude that online social media provide access to rich, unstructured social meanings in a multimedia nature. This representational complexity is a challenge for deeper explanations of social media visuals. Elhamy (2017) concluded that the semiotics research of social media visuals assume that signs, semiotic resources, non-verbal symbols, and visuals on these sites carry more than what may appear on the surface, so that represented social media visuals and signs carry deeper psychological, social, and cultural connotations that must be discovered, analyzed, and interpreted. Accordingly, the meaning of social media signs is a result of a synthesis of the input of many people and the meaning is determined not by a conscious decision of an individual or group, but rather by the clicks of thousands of people all over the world (Abdalla Mikhaeil & Baskerville, 2019). From a social semiotics perspective, Chen & Cheung (2020) pointed out that the transformation of social networking sites into social media platforms and applications is a critical point since it affects meaning-making processes. This shifting influenced not only the flow of information, but also the relationships of power among the participants in the communication process. Bezemer & Kress (2016) indicated that the components of social semiotics are responses and reflections of recognizing the social needs and interests of the communities whose members have developed, shaped, and re-shaped these resources. In the same way, semiotic resources on social media platforms and ways of shaping these resources must be viewed, analyzed, and interpreted as a kind of expression of personal and social needs, seeking self-presentation and building our mental self-image in others’ minds. Using social semiotics in social media analysis and interpretation is important for several reasons and considerations. 1. Social semiotics is best equipped for understanding different modes of expressing meaning through all the senses. 565

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2. Social semiotics is particularly useful in making mute mundane objects speaking, bringing their unnoticed significance and functionality to light. 3. Social semiotics is highly eclectic, and it can be used easily in combinations with a broad variety of other methods, approaches, and perspectives. 4. Social semiotics focuses on meanings in context and therefore on situated practices of communication, rather than merely on abstract, structural and formal methods (Bezemer & Jewitt, 2009)

Examples of Applying of Social Semiotics to Social Media Djonov & Van Leeuwen (2018) examined how social media, as semiotic technologies, not only provide sets of semiotic resources and media for texts production, but also serve as platforms for the transformation of social practices. The researchers demonstrate how ResearchGate, as a social media tool, contributes to transforming the practice of peer-reviewing. They extended their model for analysis of semiotic technologies, by incorporating into it Van Leeuwen’s framework for discourse analysis. Porteous (2020) evaluated which types of images the viewers of Instagram Fashion Influencers were more inclined to “like” and why. The analysis primarily involved statistically examining the way that five different semiotic resources (i.e. participant distance, participant gaze, participant relationship, participant clothing, and color) were used in 1000 Influencer images, in relation to the number of likes the images received. This quantitative analysis was then followed up by a qualitative examination of those images that were statistically prominent, in order to consider the particular context of the image, the type of product being promoted, the identity represented by the Influencer, and the lived-moment that the image captured. It was found that a follower’s response to a semiotic choice (e.g., the portrayal of their gaze directed at the viewer) was largely related to the specific identity or lifestyle of the Influencers. The study found as well that, in many examples, the type of semiotic choices most frequently made by the Influencers in their pictures often received the fewest number of likes. It was also found that semiotic realizations that Kress and Van Leeuwen (2020) conceive as creating greater “involvement” or “contact” with the viewer, when used in the Influencers’ images, did not, on average, receive the greatest number of likes. As Shane (2018) pointed out, the semiotic analysis approach for social media content allowed researchers to avoid the issues and problems that arise when seeking to determine the core meanings of philosophical terms. Using a semiotic analysis based on the theory of Charles Sanders Peirce, an American philosopher, logician, mathematician, and scientist, Shane (2018) studied the main authenticity cues in US President Donald Trump’s tweets and discovered their semiotic mechanisms using a semiotic analysis based on Charles Sanders Peirce’s theory. He found that Trump’s authenticity was dependent on the use of indexes, signs that have a causal link to the object to which they referred. Trump’s indexes of the self—the typographic texture, the tweets’ timestamps, and the operating system tags—combined to create a kind of authentic form for Trump’s tweets to inhabit (Shane, 2018). Bevins (2014) conducted a visual social semiotic analysis of Instagram accounts for a corporation. A company Instagram account was examined using five dimensions of Instagram activity: photos/videos, captions, likes, hashtags, and comments. These dimensions were then analyzed in relation to a set of specific brand equity assets, such as awareness, quality loyalty, and brand associations. The study found that visual social semiotics helped in creating an effective brand personality. Using the semiotic method of Roland Barthes, Turnip, Wulan and that the meaning of denotation from images on a babystagram account posting children photos includes a variety of goals such as enjoy566

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ment, entertainment, sympathy, and a sense of pride. The meaning of the connotation is for showing off, popularity, satisfy self-obsession, for economic interests such as getting many endorsement, advertising offers, movies, brand ambassador, model, and result with the main goal is for economic interests or money. Results showed a luxurious lifestyle that culminated in the establishment of an ideology; narcism, hedonism, and consumerism which caused an emergence of commodification content in photos on the account. Findings revealed also that denotative meanings created from the photos on the baystagram account demonstrated celebrities’ feelings of pride of the celebrities on everything done by their offsprings which is usually entertaining. The study concluded that the denotative meaning portrays how the celebrities show their love and affections to their kids by always giving them top quality goods. Mjdawi & Jabi (2020) investigated the pragmatic and semiotic functions of emoticons in social media. Results showed that using emoticons with text improves the expressivity and an overall sentiment scores of positive and negative opinions more than the neutral opinions. The study showed also that emoticons are preferably used with text or other emoticons. Chen & Cheung (2020) conducted a social semiotic multimodal analysis of interactive banner ads. They found that internet banner ads make and convey meaning through a combination of textual, audiovisual, and interactive modalities. The study concluded that the ads have been incorporated into a kind of gamification design to encourage consumers to spend more and buy things they may not need. It is something that contributes to the creation of new cultural forms that encourage consuming and spending. Using the social semiotic theory Ferguson & Greer (2018) examined how commercial stations in the United States represent themselves through posts on Instagram. results of the study revealed that radio station Instagram posts served as signs that lead viewers to an interpretation of what is seen, also there were two dominant themes of station posts that represented the essence of radio stations, these themes include station promotion and station community.

REFERENCES Abdalla Mikhaeil, C., & Baskerville, R. (2019). Using semiotics to analyze representational complexity in social media. HAL. doi:10.1016/j.infoandorg.2019.100271 Ali, R. H., & Aslaadi, S. (2016). A cognitive semiotic study of students’ reading a textless image versus a verbal image. (2016). Advances in Language and Literary Studies, 7(5), 1–13. doi:10.7575/aiac. alls.v.7n.5p.1 Andersen, T. H., Boeriis, M., Maagerø, E., & Tonnessen, E. S. (2015). Social semiotics: key figures, new directions. Routledge. doi:10.4324/9781315696799 Bakhtiari, S., & Saadat, M. (2015). Gender representation in interchange series: A social semiotics analysis. Iranian Journal of Applied Linguistics, 18(2), 1–39. doi:10.18869/acadpub.ijal.18.2.1 Berger, A. A. (2018). Media and communication research methods: An introduction to qualitative and quantitative approaches. Sage Publications. Bevins, C. (2014). Get Schooled: A Visual Social Semiotic Analysis of Target’s Branding using Instagram (Master of Arts in Communication Studies). Liberty University, School of Communication Studies.

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Bezemer, J., & Jewitt, C. (2009). Social Semiotics. Handbook of Pragmatics, 13, 1–14. Bezemer, J., & Kress, G. (2015). Multimodality, learning and communication: A social semiotic frame. Routledge. doi:10.4324/9781315687537 Brady, C. K. (2015). Representing truth, creating identity and forming a brand through album cover designs: A visual modality analysis of Blue Note Records and Factory Records album covers (Master of Arts in Applied Linguistics). University of Birmingham, College of Arts & Law. Chandler, D. (2007). Semiotics: the basics. Routledge. doi:10.4324/9780203014936 Chen, Z. T., & Cheung, M. (2020). Consumption as extended carnival on Tmall in contemporary China: A social semiotic multimodal analysis of interactive banner ads. Social Semiotics, 1–21. doi:10.1080/1 0350330.2020.1720992 Djonov, E., & Van Leeuwen, T. (2018). Social media as semiotic technology and social practice: The case of ResearchGate’s design and its potential to transform social practice. Social Semiotics, 28(5), 641–664. doi:10.1080/10350330.2018.1504715 Elhamy, H. (2017). Non-verbal symbols on social networking sites: An analytical study of Facebook in the light of the semiological analysis. Scientific Journal of Public Relations and Advertising Research, 2017(10), 32–382. .2017.88581 doi:10.21608/sjocs Ferguson, D., & Greer, C. (2018). Visualizing a Non-Visual Medium through Social Media: The Semiotics of Radio Station Posts on Instagram. Journal of Radio & Audio Media, 25(1), 126–141. doi:10.1 080/19376529.2017.1385617 Harrison, C. (2003). Visual social semiotics: Understanding how still images make meaning. Technical Communication (Washington), 50(1), 46–60. http://stc.uws.edu.au/ popcomm/assets/week12_harrison.pdf Hatt, M., & Klonk, C. (2006). Art history: A critical introduction to its methods. Manchester University Press. Huening, D. (2006). Symbol-index-icon. The University of Chicago. https://csmt.uchicago.edu/glossary2004/symbolindexicon.htm Jappy, T. (2013). Introduction to Peircean visual semiotics. A&C Black. Jewitt, C., & Oyama, R. (2004) Visual meaning: A social semiotic approach. Handbook of Visual Analysis, 134, 156. Kress, G., & Van Leeuwen, T. (2020). Reading images: the grammar of visual design. Routledge. doi:10.4324/9781003099857 Leeuwen, T., & Jewitt, C. (2004). Semiotics and iconography. In Van Leeuwen, T. & Jewitt, C. The handbook of visual analysis (pp. 134–156). SAGE Publications. doi:10.4135/9780857020062.n5 Leeuwen, V. T. (2001). Semiotics and iconography. In V. T. Leeuwen & C. Jewitt (Eds.), Handbook of Visual Analysis (pp. 92–118). Sage.

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Maagaard, C. A. (2015). “Modality.” In Key Terms in Multimodality: Definitions, Issues, Discussions. www.sdu.dk/multimodalkeyterms Meinhof, U. (1993). Facts—factions—fictions—fakes social semiotics and problems of representation. Social Semiotics, 3(2), 201–217. doi:10.1080/10350339309384417 Mirsarraf, M., Shairi, H., & Ahmadpanah, A. (2017, July). Social semiotic aspects of Instagram social network. In 2017 IEEE International Conference on innovations in intelligent systems and applications (INISTA) (pp. 460–465). IEEE. 10.1109/INISTA.2017.8001204 Mjdawi, A., & Jabi, S. (2020). A Pragma-Semiotic Analysis of Emoticons in Social Media. Education and Linguistics Research, 6(2), 139. doi:10.5296/elr.v6i2.17887 Moerdisuroso, I. (2014). Social semiotics and visual grammar: A contemporary approach to visual text research. International Journal of Creative And Arts Studies, 1(1), 80–91. doi:10.24821/ijcas.v1i1.1574 Peirce, C. (1965). Collected Papers. The Belknap Press of Harvard Univ. Press. Pimenta, S., & Natividade, C. (2013). The semiotic construction of masculinity and affect: A multimodal analysis of media texts. Ilha do Desterro, 0(64). Advance online publication. doi:10.5007/21758026.2013n64p173 Porteous, M. (2020). Instagram likes and the images posted by fashion Influencers: A social semiotic analysis (Doctoral dissertation). Auckland University of Technology. Poulsen, S., Kvåle, G., & Van Leeuwen, T. (2018). Social media as semiotic technology. doi:10.1080/ 10350330.2018.1509815 Shane, T. (2018). The semiotics of authenticity: Indexicality in Donald Trump’s tweets. Social Media + Society, 4(3), 205630511880031. doi:10.1177/2056305118800315 Simpson, Z., & Archer, A. (2017). Combining autoethnography and multimodal social semiotics: Potentials for theory and method. Social Semiotics, 27(5), 656–670. doi:10.1080/10350330.2016.1264689 Sonesson, G. (2013). The natural history of branching: Approaches to the phenomenology of firstness, secondness, and thirdness. Signs and Society (Chicago, Ill.), 1(2), 297–325. doi:10.1086/673251 Stoian, C. E. (2015). Analysing images: A social semiotic perspective. Buletinul Stiintific al Universitatii Politehnica din Timisoara, Seria Limbi Moderne, 14(1), 34–42. .2016.e1162 doi:10.7748/phc Turnip, L., Wulan, R., & Malau, R. (2016). Babystagram Phenomenon Among Indonesia Celebrities Instagram Accounts: Semiotic Analysis on Photographs at Babystagram Account. In The 3rd Conference on Communication, Culture and Media Studies (pp. 167-171). Yogyakarta, Indonesia: Department of Communications, Islamic University of Indonesia. Unsworth, L. (2008), Multimodal semiotic analyses and education. Multimodal semiotics: Functional analysis in contexts of education, 1–12. Van Leeuwen, T. (2005). Introducing social semiotics. Psychology Press.

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Wong, M. (2019). Multimodal communication: A social semiotic approach to text and image in print and digital media. Springer.

KEY TERMS AND DEFINITIONS Semiotics Resources: A concept used in social semiotics to describe a means for a meaning-making process. Semiotic resources are the materials, actions, and artifacts one can use for communicating, whether they are produced physiologically, such as voice apparatus, making facial expressions and gestures, or technologically, such as pen, ink, or computer applications, as well as the methods by which these semiotic resources could be organized. Social Semiotics: Social semiotics is a method, an approach, an analytical perspective, and research strategy that enables researchers to investigate the systematic relationships between social reality and signs, texts, and discourses. Social semiotics is considered a body of critical and interpretative theory for examining the meaning-making process in order to demonstrate how meaning is constructed in social actions and contexts. Visual Social Semiotics: A research field (originating in the 1990s) that focuses on the description of semiotic resources, what can be said and done using images and other visual means of communication as well as how what people say and do with images that may be interpreted within a social context.

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Unveiling Network Data Patterns in Social Media Maria Prosperina Vitale University of Salerno, Italy Maria Carmela Catone University of Barcelona, Spain Ilaria Primerano University of Salerno, Italy Giuseppe Giordano University of Salerno, Italy

ABSTRACT The present study focuses on the usefulness of social network analysis in unveiling network patterns in social media. Specifically, the propagation and consumption of information on Twitter through network analysis tools are investigated to discover the presence of specific conversational patterns in the derived online data. The choosing of Twitter is motivated by the fact that it induces the definition of relationships between users by following communication flows on specific topics of interest and identifying key profiles who influence debates in the digital space. Further lines of research are discussed regarding the tools for discovering the spread of fake news. Considerable disinformation can be generated on social networks, offering a complex picture of informational disorientation in the digital society.

INTRODUCTION The development of the Internet and ICT have opened up new channels and contexts, such as social media, through which individuals express their actions, feelings, and information, for example writing status updates, commenting, liking content, and posting photos. In these spaces, our opinions and news develop dynamically, impacting everyday life activities, social relationships, and identities. DOI: 10.4018/978-1-7998-8473-6.ch033

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Particularly, social media during the spread of the pandemic further contributed to the generation of practices and rituals of socialization that allowed people to stay in touch due to the impossibility of realizing them in the typical physical contexts of everyday life (Addeo et al., 2020). During the COVID-19 pandemic, the sharing of information, emotions, and opinions through the multiplicity of languages and codes that characterize social media acted as important connectors and relational aggregators, with strong emotional and informative connotations. In other words, social media played a fundamental role as a social glue, as people from all over the world stayed in touch by sharing news about a dynamic and changing emergency situation, developed a common feeling and solidarity using #stayathome hashtags and participating in flashmobs, and reinvented some social rituals, for example, through video calls (Addeo et al., 2020). Conversely, another important consequence generated by social media during the COVID-19 pandemic concerns information overload, which has contributed to the creating and spreading of fake news, causing uncertainty and confusion about the credibility and truthfulness of the information in an unprecedented and emergency situation. Within this context, this study deals with the articulate issue of unveiling network data patterns and detecting information/disinformation propagation in social media, i.e., an important knowledge and methodological challenge in the analysis of the so-called digital society; specifically, the study concerns the use of network analysis tools to identify the patterns underpinning social media interactions and the forms of misinformation that can be generated. The first and second sections, adopting a distinction provided by Marres (2017) on the concept of digital society, provide a theoretical overview of the impact of the digitalization process and social media on everyday life; conversely, it introduces some characteristics of social research in the digital age also due to the vast amount of social big data available on the web, provided by the development of digital technologies. These aspects are further deepened in the third section, which focuses on the features of Social Network Analysis (SNA) as a useful methodological approach capable of identifying the conversational patterns in social media data. Starting from a description of different social structures and shapes for social media crowds and conversations on Twitter, the authors present the main findings of a network analysis conducted on Twitter posts related to the Covid-19 vaccine debate. Finally, the last section discusses further research lines connected to the potentials of network analysis in the study of fake news and information/disinformation propagation on social media.

CHALLENGES AND OPPORTUNITIES OF DIGITAL SOCIETY The extensive process of digitalization that reshapes the activities we usually conduct, the environments in which we are embedded, and the objects we use are among the most significant characteristics of contemporary society. There are many examples of how digital technologies are integrated into multiple dimensions of everyday life, such as in credit card transactions, the geolocation of our movements, the images captured by video surveillance cameras, and the smartphones and tablets we carry around with us. Specifically, the Internet and ICT have contributed to the creation of a “complex and multifaceted arena” (Roberts et al., 2016, p. 3) in which different environments, tools, and infrastructures dynamically converge and has deeply impacted the reconfiguration of daily habits regarding time, space, activities, social relations, and identity representations. These issues are among the so-called “digital society” which, according to Marres (2017), has different and interconnected meanings. One definition focuses on how the digitization process affects 572

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multiple spheres of our daily lives, determining social, cultural, political, and economic transformations. Digitalization touches most dimensions of social life at the individual and global levels, changes our ways of being, and our practices of knowing, sharing, and learning, and thus can be defined as a “total social fact” (Marres, 2017, p. 26). A main feature of digital technologies is the culture of sharing: they have opened new channels and contexts through which individuals can express the narratives of their everyday lives regarding experiences, opinions, symbolic artifacts, events, and activities. In this perspective, social media especially embodies this culture by increasing users’ “ability to co-operate with one another” (Shirky, 2008, p. 20) and enhancing human networks (Van Dijck, 2013, p. 11). In particular, social media platforms act as places of connection that foster collective processes of content construction and revision through interactive discussion and exchange based on sharing, and thus, particular scenarios of proximity and rituals of interaction are generated among users. Interactions in this context can occur in different ways through specific objects and semantic units, such as the opportunity to comment and “like” posts, and to congregate around issues or themes using hashtags (Seargeant & Tagg, 2014). Thus, social media environments act as a connector of experiences and feelings, which enables the construction of symbolic sharing and affects identity representation, which itself can be conceived as the visible result of our connections. Other characteristics of social media, such as its horizontal flows, low cost, ease of use, interaction speed, and collective nature, favor multi-level forms of communication, which simultaneously increases the risk of information overload exponentially. Through the plurality of these interactive dynamics, the connected user assumes the role of “prosumer” who, far from being a passive actor, actually creates, consumes, produces, and shares information (Boccia Artieri, 2012). While this induces the spread of participatory cultures (Jenkins, 2006), it supports the circulation of unfounded news and a multiplicity of alternative truths and misinformation (Salzano & Napoli, 2020; Salzano et al., 2017) because the Internet, particularly social media, acts as an accelerator that created a communication snowball effect (Gili & Maddalena, 2018). Relatedly, social media conversations reportedly occur within echo chambers (Sunstein, 2009) in which online communities, characterized by homogeneous beliefs, disseminate information with which they agree and exclude opinions with which they disagree (Salzano et al., 2017). In addition to the different impacts that digital technologies have in our daily lives, which are increasingly considered by researchers as important phenomena that require investigation through the theoretical lens of the social sciences, the second meaning of “digital society” provided by Marres (2017) concerns the need to reconfigure the traditional empirical apparatus of the social research process and deal with the ways — regarding methods and techniques — needed to collect, analyze, and interpret these quantities and varieties of online information and traces. The discussion on the methods and techniques for the analysis of social phenomena in the digital age is very broad and concerns the changes in the way of making research in its different phases: from the construction of the empirical base, and the availability of already “available” data to the delicate phases of interpretation needed to identify a common thread able to connect the multiplicity and heterogeneity of traces that characterize digital contexts. In other words, the scenario in which social research operates becomes more articulated and complex, requiring the social researcher to consider a complex assemblage of different elements, such as the human being, technological devices, infrastructures, and data. In this perspective, for example, Rogers (2013) provided a useful classification between the “digitization” of methods and “natively digital” methods. The first refers to extant empirical research techniques adapted to the digital environment, while the second concerns “new” methods specifically designed ac573

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cording to the distinctive characteristics of digital devices. Undoubtedly, a challenge of social research in the digital age is the need to handle an exponential increase in unstructured information, digital traces of which are liquid, permeable, and mobile (Lupton, 2014), and to grasp, explore, and understand informational mobility, which is the ability to travel through different analytical situations, reusing and relating as many types of data possible (Leonelli, 2018). Starting from the importance of digital information mobility and the relationships that bind them, the following sections address the contribution of network analysis in detecting conversational patterns through the exploration of social media data.

DISCOVERING NETWORK DATA IN SOCIAL MEDIA ANALYSIS A primary consequence of living in a media-saturated social world lies in the digital traces that each user, consciously or not, leaves on the web (Hepp et al., 2018). These footprints produced by people on social media — mainly sharing user-generated content, such as Facebook posts, tweets, and hashtags — enrich the repository of online available data. This considerable amount of unstructured or semi-structured data represents a key challenge for both researchers and data analysts (Kambatla et al., 2014) focusing their analyses on computer-mediated social interactions. Data arising from users’ social media activities are usually referred to as Social Big Data, whose definition comes from the combination of three different concepts: Social Media, Big Data, and Data Analytics (Olshannikova et al., 2017; Bello-Orgaz et al., 2016). The intersection of these three concepts represents the primary purpose of any empirical applications regarding knowledge extraction into the theoretical frameworks of Social Media research (Kapoor et al., 2018), Computational Social Science (McFarland et al., 2016, Lazer et al., 2009), and Digital Sociology (Marres, 2017; Lupton, 2014). Thus, identifying the most appropriate analytical tools for extracting and analyzing online data (Corposanto & Molinari, 2015), reducing the risks of data quality (Lombi, 2015), is a challenging task. Given the high relevance of social networks in everyday life, the analysis of social media data has attracted the attention of researchers from various disciplinary fields. Generally, any website that provides a social experience in the form of user interactions allows researchers to focus on the data-centric issues that may arise regarding online social networks (Aggarwal, 2011). Researchers have focused considerably on the definition of network data structures (Wasserman & Faust, 1994) where nodes are the users, and links are defined by considering one or more relationships connecting them. Data extracted from social media can be processed as network data analyzed with SNA (Wasserman & Faust, 1994) and visualized as a graph. Different kinds of network data can be derived from social media. Users replying to other users, friends tagging other friends, hashtags and conversation participants, co-occurring hashtags are some examples of network data that can be extracted from online conversations (Vitale et al., 2020). Unlike traditional methods of data collection and analysis, limited to the description of the actors’ characteristics, the SNA methods can provide insights into the structure of online interactions by visualizing networks and computing measures able to identify key actors in the network and conversational patterns. Moreover, through specific software1, it is possible to visualize and analyze huge amounts of social media data and highlight the evolution of relationships over time and/or according to specific topics of interest. The SNA approach to social media data has been extensively applied in several application fields. Among others, it has been used to study computer-mediated communication in virtual communities (Garton et al., 1997), investigate Twitter communication networks regarding international agreements 574

 Unveiling Network Data Patterns in Social Media

(Schuster et al., 2019), determine a suitable indicator for characterizing places according to tourist’s pictures shared on Instagram (Giordano et al., 2020), assess the impact of social media on brand-centric networks connected by discursive relationships among organizations and stakeholders on Facebook (Chen et al., 2021), and examine how professional roles affect the conversational dynamics of an online community of people interested in exploring social innovation in health care from the social media traces left through Twitter posts (Gruzdand et al., 2013). Recently, the main topics addressed by researchers are related to the ongoing COVID-19 pandemic. It has been shown that, during this period, people got informed mostly by connecting with their profiles on social networks (De Santis et al., 2020). Hence, several studies have been realized by extracting social media data related to COVID-19 facts, such as new, daily confirmed cases, lockdown policies, and vaccine debate. Additionally, Twitter data are extracted to analyze people’s sentiments and attitudes toward the coronavirus and identify how tweets can define connected communities of users by using network analysis techniques (Jamison et al., 2020; Treceñe & Abides, 2020) to unveil network data patterns in online debates.

UNVEILING NETWORK DATA PATTERNS The analyses of online social interactions in computer-mediated environments connecting people around specific topics of interest are useful for understanding emergent community network properties, identifying social roles and structures, and discovering information dissemination patterns (Gruzd & Haythornthwaite, 2013). By exploring online interactions, the key factors for understanding how collective involvement occurs in the digital space can be investigated by tracing the exchanges between users who interact across several aspects. The different kinds of data gathered through social media channels and social networks are then used to gain insights into the birth and maintenance of online communities (Gruzd & Haythornthwaite, 2013). These data involve different kinds of interactions: dyadic and close group interactions are produced in email exchange; larger and more unknown memberships are derived from discussion forums; web pages show explicit and implicit connections between people on wider societal levels, across regions, nations, and the world. The analysis of the social and contextual factors shaping community behaviors, their activities, and dynamics, is key to discovering the presence of particular interactions patterns on the web. In this scenario, social media traces from a social network such as Twitter can reveal the presence of conversational patterns and their implications on community structures. Through “Mentions,” “Retweets,” “Replies to,” and “Follows” relationships, this platform allows the description of public opinion, behavior mining, and exploration of communication patterns in online conversational dynamics (Housley et al., 2014). Our choosing of Twitter to unveil the presence of network data patterns is motivated by the fact that it is configured as a container of information through hashtags. Twitter users put the # hashtag symbol before a relevant keyword or topic to index keywords or topics on their tweets. These hashtags can be used to follow a discussion between people, encouraging others to participate as well. These topics being discussed can be widely popular like gossip or current events or customized reflecting users’ interests. Thus, communication flows can be followed by considering a single hashtag identifying a specific topic of interest. Some functions in this platform induce the analysis of relationships between users. Every update sent to Twitter, published by users from any part of the world, can be immediately indexed and used for extracting information in real-time, becoming a sort of search engine to find what is happening 575

 Unveiling Network Data Patterns in Social Media

and the public debate around a specific topic in a precise time frame. This possibility allows people to easily follow topics of interest in the digital space. Himelboim et al. (2017) introduced different social structures and shapes for social media crowds and conversations on Twitter: divided (polarized crowds), unified (tight crowds), fragmented (brand clusters), clustered (community clusters), and inward and outward hub and spokes (broadcast network and support network, respectively).2 Conversational archetypes on Twitter are then described at least by these six distinctive structures depending on the specific topic discussed, the people talking about it, and the presence of key actors in the network. Each type of conversation network shows how it is shaped by the topic, and the people motivating the online debate: from the polarized crowds, described by opposed discussions of large and dense groups with few connections between them, to community clusters, where popular topics may develop multiple smaller groups formed around a few hubs. Posts/Tweets define network data as users’ replies to and mentions (retweets) of one another in their posts. These connections are visible in the text of each tweet or by considering the lists of users following a specific Twitter profile. Hence, detecting structures and patterns in online conversations through network analysis tools can furnish a strategy of analysis for detecting influencers, community profiling, and trends in user-generated content around a topic (Himelboim et al., 2017; Milani et al., 2020). Additionally, innovative analysis methods are developed to integrate the network analysis of social media data with qualitative research. Insights from ethnographic research can be used to identify key users and extract network data, and, simultaneously, Twitter data is used in ethnography to identify actors to interview and topics to be discussed (Ustek-Spilda et al., 2021).

THE VACCINE DEBATE ON TWITTER During the COVID-19 pandemic, several studies were proposed to analyze the Twitter data regarding people’s sentiments and attitudes toward the coronavirus, and to identify how tweets defined connected groups of users through text mining techniques and network analysis (Jamison et al., 2020; Treceñe & Abides, 2020) according to the related literature (Himelboim et al., 2020; van Schalkwyk et al., 2020; Bello-Orgaz et al., 2017). In the following, we present an example of the kind of data that can be extracted by Twitter messages using the hashtag #Covid19 Vaccine, and the analysis performed to unveil conversational patterns discovering the different community network structures through network analysis tools. We examine the tweets in a two-day time span related to this topic. A comparison of what emerged during the period of Moderna vaccine introduction in autumn 2020 (17 November, 2020) until the current phase characterized by different vaccines and a high rate of people vaccinated worldwide (8 July, 2021) is illustrated. The data from the hashtag #Covid19 Vaccine are extracted, visualized, and analyzed using NodeXL (Hansen et al., 2010), a software adapted to support social network and content analysis for social media data, placing network analysis inside the context of the Microsoft Excel spreadsheet. For simplicity, the networks (Table 1) defined by considering the relationships of “Mentions” and “Replies to” among Twitter users interacting on the vaccine topic in the two days are visualized in Figures 1 and 2. Networks have different structural characteristics. The former, realized starting from the data extracted in the first period, involves 1954 users, while the latter involves 1665 users. More interactions are present among users during the first period (2190 links versus 1540 links). This aspect is also confirmed by the smaller number of connected components detected in the first and second networks (257 576

 Unveiling Network Data Patterns in Social Media

versus 323). The giant components of the two networks are different in size, indicating a substantial difference in the users’ interactions. The first network involves 883 users connected by 1333 links (i.e., mentions or replies to), while in the second, the giant component comprises a smaller number of users (205) linked by few links (208). Moreover, although both networks show a very sparse structure, as also evidenced by the low-density values (0.001 for both), they also differ regarding inter-community connections that are much more evident in the first network, where some users play a brokerage role in connecting different communities. Applying social network analysis tools to these data enables us to identify Twitter profiles where relationships among users originated from. In particular, indegree centrality scores (Freeman, 1979) are computed to highlight the presence of some prominent influencers in the vaccination debate (Tables 2 and 3), i.e., those profiles that are most targeted by other tweeters and re-tweets. Groups are also identified according to the community detection hierarchical agglomeration algorithm (Clauset et al., 2004), obtaining clusters of users with a higher density of links within groups than between them. Figures 1 and 2 show users colored and grouped according to these clusters, while nodes’ sizes depend on the indegree centrality scores. Within the vaccine and coronavirus debate, we notice that: − in the first period, emerges, on the one hand, the role played by the Agency Press (The New York Times, The Associated Press, British Politics Polls) and TV Broadcasters (CNN Breaking News, Reuters Breaking News) mainly located in the United States or the United Kingdom, and, on the other hand, American politicians (Donald Trump and his Vice President Mike Pence, The Whitehouse) and the Moderna Twitter profile (Table 1). The network pattern (Figure 1) resembles the conversational archetype called community clusters (Himelboim et al., 2017), where the public popular topic about the introduction of the first kind of vaccine developed in medium-sized groups formed around few hubs, each with its audience, influencers, and sources of information. This archetypal conversation profile is characterized by multiple centers of activity, each promoting its audience and community as well as some isolated users (depicted on the right side of Figure 1). These behaviors show diverse angles of a subject based on its relevance to different audiences, revealing various opinions on a social media topic in the public debate; − the recent vaccine debate seems to be concentrated on individual Twitter profiles, such as a Trump supporter (David Weissman), Human Rights lawyer (Qasim Rashid, Esq.), a journalist and director of French TV (Elise Blaise), and people involved in health and medical departments (Siddharth Sridhar, HKU Medicine) (Table 2). The network patterns resemble the fragmented brand clusters (Himelboim et al., 2017), with users talking about the vaccine creating a very sparse network. This conversational structure is characterized by many disconnected groups that do not interact with each other and few small clusters where a limited number of users interact among themselves about the vaccination topic. In summary, social media network analysis seems to be an effective method to study the spreading of the vaccine topic on Twitter, as it investigates the distribution of tweets and retweets discovering the key actors that could affect the conversational patterns. Even if our illustrative example is limited, some network patterns in Twitter conversations appear. Future studies could extract more tweets to gain more interesting insights into these preliminary and exploratory results. Furthermore, studies investigating pro-and anti-vaccination users in Twitter conversations (Milani et al., 2020) as well as understanding the drivers of misinformation propagation and fake news in social 577

 Unveiling Network Data Patterns in Social Media

media (see, for instance, the 5G COVID-19 conspiracy theory discussed in Ahmed et al., 2020) can be deepened to discover the mechanisms underlying these processes in digital environments, as further discussed in the last section. Table 1. Networks characteristics of Twitter users who tweeted #Covid19 vaccine in the two days Network characteristics

November 17, 2020

July 8, 2021

Vertices

1954

1665

Edges

2190

1540

Connected Components

257

322

Maximum Vertices in a Connected Component (Giant component)

883

205

Maximum Edges in a Connected Component (Giant component)

1333

208

Maximum Geodesic Distance (Diameter)

14

11

Average Geodesic Distance

5.10

2.76

Graph Density

0.001

0.001

Table 2. Ranking of Twitter users who tweeted #Covid19 vaccine with the highest indegree centrality scores and Twitter Analytics - November 17, 2020 Twitter profile

In-Degree

Followed

Followers

Tweets

whitehouse

328

14

25680388

23528

realdonaldtrump

238

51

88955328

58486

mike_pence

148

47

5831904

12770

ap

116

6960

14780868

278185

reuters

113

1125

22703629

613035

nytimes

42

900

48129451

413699

sailorrooscout

39

870

16155

19585

moderna_tx

37

432

47516

1090

spectatorindex

34

0

1823626

5077

politicspollss

34

5212

5158

4107

cnnbrk

31

119

59567627

73329

RECENT DEVELOPMENTS IN FAKE NEWS DETECTION In the previous sections, the importance of social media as a modern form of human communication and interaction has been envisaged, and emerging networks in form of social relationships derived from material and/or virtual actions are useful, providing evidence and measurable consistency of human behavior in a virtual society.

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 Unveiling Network Data Patterns in Social Media

Table 3. Ranking of Twitter users who tweeted #Covid19 vaccine with the highest indegree centrality scores and Twitter Analytics - July 8, 2021 Twitter profile

In-Degree

Followed

Followers

Tweets

davidmweissman

158

11526

332641

277748

qasimrashid

48

335

307102

1641

blaiseelise

33

751

22242

5594

waytowichneil

26

12290

24597

38752

satyakumar_y

25

655

35856

12137

sid8998

25

472

1278

523

hkumed

25

327

12810

2141

rita_banerji

22

385

12882

112384

laurie_garrett

22

1831

242327

80004

bogochisaac

21

978

131865

7726

hhsgov

21

340

1091509

24692

coronapasscy

19

900

1495

1079

Figure 1. Connections among Twitter users who tweeted #Covid19 vaccine on November 17, 2020

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 Unveiling Network Data Patterns in Social Media

Figure 2. Connections among Twitter users who tweeted #Covid19 vaccine on July 8, 2021

Networks’ data provide rich and powerful information about the shapes, strengths, contents, and dynamics of social behaviors and people interactions. Alongside helpful and useful material, the spread of information and news on the web also involves the propagation of misrepresentation and misinformation. In particular, social media platforms are usually used to disseminate misleading information influenced by simplifications and prejudices that undermine the veracity of the news. Thorson (2008) highlighted how news posted on social media is more likely to receive attention from others when the post is accompanied by many likes, shares, or comments, and therefore more likely to be further liked, shared, or commented on. More recently, the development of so-called news bots automates this cycle, adding what the unwary reader of the news might interpret as the item’s legitimacy (Lokot & Diakopoulos, 2016). Thus, when news appears on social media, the web also acts as an amplifier. Since information spreads very rapidly, it succeeds in reaching a large portion of users and manifests itself in various types and actions, reaching a wide audience with different cultures and discernment faculties. For example, also during Covid-19 pandemic, the use of social media favored the spread of unreliable information on different aspects, such as the vaccine hesitancy debate, the restrictive measures, the opening of schools and universities, the use of personal protective equipment. In order to better explore this phenomenon, it is worth noticing that the concept of fake news has been revised and detailed in several levels of misinformation (Tandoc et al., 2018): from misinformation

580

 Unveiling Network Data Patterns in Social Media

(inaccurate information that is communicated regardless of an intention to deceive) to disinformation (misinformation that is deliberately deceptive) and from propaganda (used primarily to influence an audience) to pseudo-science (statements or practices that are claimed to be scientific but are incompatible with the scientific method). According to Allcott & Gentzkow (2017), fake news is defined “to be news articles that are intentionally and verifiably false and could mislead readers”. The issue of detecting fake news hidden in the sea of news surfing the web has been addressed in the literature through different approaches. The most recent ones highlight that it is how traces in the network spread and the speed and variety of communication channels that turn out to be useful in identifying fake news. In other words, it is the shape of the footprint that the flow of information leaves behind to signal clues about possible fake news. Recent approaches to fake news detection have been based on three main aspects: the content, the social context, and the propagation form (Zhou & Zafarani, 2020; Shu et al., 2017, 2018a). − Content-based approaches rely on lexical and syntactic features that can intercept misleading signals (Pérez-Rosas et al., 2017; Afroz et al., 2012). The success of this approach depends on the sophistication level of the fake news, and since it is linguistic dependent, its generalizability is limited. − Social context approaches include auxiliary information on users (age, gender, education, and political affiliation (Shu et al., 2019; Long et al., 2017), social network structure (data on friendship or follower/followed relationships), and user reactions, that is, actions accompanying a news story, posts or “likes” (Tacchini et al., 2017). − Propagation-based approaches are the most promising research direction based on the study of the news proliferation process over time (Monti et al., 2019). They stem from the empirical evidence that fake news propagates differently from real news (Vosoughi et al., 2018), forming diffusion shapes that can be exploited for automatic fake news detection. A different characterization of fake news detection methods follows depending on feature extraction. It can be related to the Source, Headline, Body text, and Image (or Video) of the news. − Linguistic features can be used to detect fake news: lexical feature (total words, frequency of large words, and unique words) and syntactic feature at the sentence level (bag-of-words approach, partof-speech tagging), as in Mahyoob et al. (2021). − Visual-based features are extracted from visual elements (e.g., images and videos) to capture the different characteristics of fake news. Fake images were identified based on various features using a classification framework (Gupta et al., 2013). Recently, various visual and statistical features have been extracted for news verification (Cao et al., 2020), including visual clarity score, visual coherence score, visual clustering score, etc. − User-based features represent the characteristics of those users who interact with the news on social media. These features can be extracted to infer the credibility and reliability of each user using various aspects of users, such as age, number of followers, number of tweets the user has authored, etc. (Castillo et al., 2011) − Post-based features help find potential fake news via reactions from the general public, as expressed in posts. These features can be categorized as post, group, and temporal levels. The aforementioned linguistic-based features can also be applied to each post. There are unique features for posts that

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represent the social response from the public. Topic features can be extracted using topic models, such as latent Dirichlet allocation (LDA) (Gautam et al., 2021). − Network-based features: fake news dissemination processes tend to form an echo chamber cycle, highlighting the value of extracting network-based features to represent these types of network patterns for fake news detection. Network-based features are extracted by constructing specific networks among the users who published related social media posts. Different types of networks can be constructed. The stance (opinion) network can be built with nodes, indicating all the tweets relevant to the news, and the edge, indicating the weight of similarity of opinions (Shu et al., 2018b; Tacchini et al., 2017). Another type of network is the co-occurrence network, which is built based on the user engagements by counting whether those users write posts relevant to the same news (Ruchansky et al., 2017). Additionally, the friendship network indicates the following structure of users who post related tweets. A particular case is the diffusion network, which tracks the trajectory of the spread of the news, where nodes represent the users and edges represent the information diffusion paths among them After these networks are properly built, existing network metrics can be applied as feature representations. For example, degree and clustering coefficients have been used to characterize the diffusion and friendship networks (Kwon et al., 2013). However, most existing approaches are supervised, which requires a pre-annotated fake news dataset to train a model. Thus, scenarios where limited or no labeled fake news are available must be considered and unsupervised models can be applied. While the models created by supervised classification methods may be more accurate given a suitable training dataset, unsupervised models can be more useful because unlabeled datasets are more available. A promising class of models based on machine learning and a statistical approach are those based on deep learning (Islam et al., 2020) and geometric deep learning (Monti et al., 2019). Specifically, the latter is the element that completes the picture, unifying the aspects of machine learning and social network analysis. Nevertheless, it is still an active research topic and needs to be extended in future studies.

CONCLUSION Nowadays, the analysis of social big data is a main methodological challenge that social scientists in different application fields are faced with. The increasing propensity of people to share their affairs (e.g., activities, emotions, and thoughts) online, in real-time with other users, has made social media among the main information source of our time, contributing spontaneously or not to the enrichment of huge repositories of data. The rapid propagation of news and fake news online is, in many cases, attributable to the mass of users who, by updating their social private profiles or writing something about a particular event or news, also inform other users who are not directly involved. This sharing of updates has attracted the interests of researchers from several disciplinary fields interested in extracting the different kinds of data users leave on social media through their daily online activities. The methodological challenge in conducting social research on online network data is the possibility of exploring real-time data regarding a specific phenomenon of interest through advanced statistical techniques, such as the SNA. It allows to face the complexity of emergent network data, for example, by analyzing conversational dynamics around a trending Twitter topic.

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In this study, we have presented a first illustrative example on the Covid-19 Vaccine hashtag by extracting data from Twitter, with a comparison of network data structures emerging from two periods. This strategy of analysis permitted to detect structures and patterns in Twitter conversations and identify the key users in the network, the influencers, around which densely communities emerged, characterized by their tweets and re-tweets contents. Given the interesting results, the study could be deepened by extracting Twitter data over a longer period to cover a greater amount of information and also applying sophisticated SNA tools to discover the presence of communities on social media debates. Finally, the brief review presented for detecting fake news on social media highlights the opportunities of an approach capable of unifying the aspects of machine learning and SNA as a promising research topic to be extended in future studies.

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Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press. doi:10.1017/CBO9780511815478 Zhou, X., & Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys, 53(5), 1–40. doi:10.1145/3395046

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Different software are available for analyzing network data, such as Pajek (Batagelj & Mrvar, 1998), UCINET (Borgatti et al., 2014), NodeXL (Hansen et al., 2010), Gephi (Bastian et al., 2009), Polinode (Pitts, 2016), and specific statistical packages implemented in the R software (SNA, Butts, 2008; and igraph, Csardi, & Nepusz, 2006). For more details: https://www.pewresearch.org/internet/2014/02/20/mapping-twitter-topic-networksfrom-polarized-crowds-to-community-clusters/

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Fostering Imagined Economies on Twitter: The Case of the Food Startup Economy in Italy Vincenzo Luise University of Milan, Italy Patrizio Lodetti University of Florence, Italy

ABSTRACT Startups are entrepreneurial organisations that aim to develop a scalable and disruptive business. However, these small ventures operate in an environment of extreme uncertainty. The startup economy takes place in the present but is directed towards the future. This chapter critically investigates in online and offline realms the circulation of imagined futures that create causal links to bridge the gap between the present economic scenario and potential futures in the Italian startup food economy. This work adopted a mixed-method approach framed in a qualitative exploratory strategy which was designed to integrate qualitative techniques and digital methods. This work concludes by highlighting the co-evolutionary process between online and offline realms. On the one hand, online narratives allow economic actors to perform in radical uncertain economic contexts, while, on the other hand, the offline practices give legitimacy and credibility to these potential future scenarios.

INTRODUCTION1 Neoliberal policies and the free market ideology have transformed the nature of labour (Harvey, 2005). The individualisation and entrepreneuralisation processes of neoliberalism have produced unstable and project-based careers (Hesmondhalgh & Baker, 2013) as well as new forms of nonstandard employment (Cappelli & Keller, 2013). Contemporary flexible capitalism (Sennet, 2007) has produced a shift from an era of corporate loyalty and job stability to an economic system in which insecurity and personal high DOI: 10.4018/978-1-7998-8473-6.ch034

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risks predominate. The opportunity of finding job positions that match knowledge workers’ aspirations have become increasingly difficult. The startup economy (Luise, 2019) has emerged as a new entrepreneurial paradigm, one which promises to fill this gap by allowing the perpetuation of a creative class (Florida, 2002) to fulfil both personal and professional aims. Mainstream narratives about the myth of successful Silicon Valley entrepreneurs, the generic development of skills and the lowering of the capital requirements for creating business activities have fostered a new wave of small and highly innovative digital ventures (Arvidsson, 2019) rooted in the Californian ideology (Barbrook & Cameron, 1996). Startups are entrepreneurial organisations that aim to develop a repeatable, scalable and disruptive business (Blank & Dorf, 2012). These ventures require a significant amount of external funding in order to develop their innovative services or products. Startuppers spend most of their time working on their presentation skills, promoting the business ideas by leveraging on the image of a team capable of executing their projects in a trustworthy manner. Startuppers seek to convince venture capitalists, angel investors and accelerators to provide them with the economic and cultural resources needed to develop their innovative services and products. But business growth cannot be based entirely on future cash flow. Startuppers need to consider other elements that will allow them to organise and plan their innovative economic activities. Through different communicative devices, such as pitch presentations, business plans and growth metrics, startuppers foster imagined economic futures in which their startups are qualified as high-reward businesses. The startup economy takes place in the present but is directed towards the future (Mische, 2009) and it is based on a ‘regime of hope’ (Martin, 2012). However, these future economic scenarios are also a source of radical uncertainty and are vulnerable to contradictions. Startups perform in technologically turbulent and innovative markets where customers, markets and other economic elements of businesses can be completely unknown (Casper, 2007; Ries, 2011). This article critically investigates the production and circulation of imagined futures in the Italian food startup economy that create causal links to bridge the gap between the present economic scenario and possible future developments. This work was driven by three main research questions. Who are the economic actors that articulate discourses about future economies and through which strategies do they promote the circulation of these narratives in the online and offline domains? To what extent do imagined futures qualify the startups as potentially profitable businesses that can foster the creation of new markets? To what extent does the production and circulation of narratives about future economies foster the institutionalisation of a specific imaginary? The theoretical cornerstones that supported this work concern two study areas within the sociological field. The imagined futures approach (Beckert, 2013, 2016) allows the framing of economic behaviours as a form of fictional expectations in uncertain economic contexts. The cultural political economy (Jessop & Oosterlynck, 2008) perspective explores the process through which imagined economies are discursively constituted and materially reproduced. This article is organised as follows. First, an overview of the conceptual background adopted by this work is presented. Second, the methodological framework and research techniques are described. In this section, the methodological challenges that characterised the research subjects are discussed, as are the reasons why a mixed-methods approach, one that combined qualitative and digital data, was adopted. Third, the results of this work are explored along two dimensions: how imagined futures create new markets, and the communicative practices that permit specific economic actors to colonise the imaginaries pertaining to innovative solutions in the food economy. Finally, the mechanisms and practices through

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which imagined futures are created and managed are critically discussed, as is their performative power in directing financial investments towards specific startups.

CONCEPTUAL BACKGROUND Imagined Futures As Beckert (2013) argued, any economic actors can be motivated by fictional expectations that permit them to direct their innovative businesses towards future success. Such fictional expectations in the knowledge economy assume a narrative form as stories, theories and discourses that can create causal links to bridge the gap between the current present and the potential future. Beckert (ivi) defined fictions as ‘images of some future state of the world or course of events that are cognitively accessible in the present through mental representation’ (p. 220). Fictions provide plausible reasons for believing that potential futures can become real, thereby generating expectations about future trends in an innovative economy. But, these possible future economies must have correspondences with reality. They must be compatible with the current present, but at the same time they are not entirely based on empirically observable truths. Indeed, fictional expectations are not applicable under conditions of certainty and risk – in other words, in situations where a possible future can be already known. This condition gives credibility to the predictive power of fictions. Economic actors compare fictions with reality to understand whether the predicted states will become concretely real. During this process, disbelief is suspended because the possible futures must appear plausible with reality for becoming true. In this way, startuppers are able to act according to an uncertain future perceived as if it were true, even if this future is unknown and unpredictable. Indeed, fictions do not have to be true, but they must be convincing. Since these fictions are not confined to empirical reality, they are also a source of creativity in the economy. It is worth to underline that these reflections are inscribed within a constructivist framework, according to which reality is a social construction (Berger & Luckmann, 1969). The attribution of meanings which people gives to events and facts, shape and define what is considered as real. From this assumption can be said that language does not only describe the world, but it does builds it. In this regard, Austin (1978) comes to define language as a performative act: it is never neutral and produces interpretations of reality, which consequently trigger actions and practices. In any narrative there is a connotative level, that is, the meaning that the sign takes on for those who receive it. If we want to reason sociologically on this perspective, we cannot ignore the context of production and use of messages and the conventions that lead to certain types of interpretation. Thus, the uncertainty about future states can be a source of innovation that encourages deviant imaginaries, economic risk-taking behaviours and the development of innovative products or services. This creative power makes it possible to link facts in different ways. In this sense, these fictions can be subversive of the established order. Indeed, the plurality of fictional expectations is also a source of uncertainty. This happens for two reasons. On the one hand, the various imaginaries provide an overabundance of possible futures that can be perceived as incompatible; and, on the other hand, these imaginaries are open to the influence of collective beliefs and to manipulations by powerful actors. Indeed, fictions are also vulnerable to contradictory experiences in the real world and are potentially open to adaptation. The

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belief in a particular possible future remains fragile, and it can also be contested, because of its narrative form. Indeed, the development process remains constantly open.

Cultural Political Economy According to Jessop and Oosterlynck (2008), the cultural political economy approach is characterised by continuous interaction between the semiotic and extra-semiotic dimensions in a complex co-evolutionary process. The authors combined critical semiotic analysis with an evolutionary and institutional approach to political economy. This allowed them to recognise the semiotic dimensions of the political economy and to understand the process through which only some economic imaginaries come to be selected and institutionalised among the various imaginaries that have been created. In this sense, on the one hand, the imaginary is a semiotic system that works as a cognitive framework; while, on the other hand, the extra-semiotic dimension provides the means to act within reality. This approach focuses on the coevolutionary role of semiosis and the emergent extra-semiotic dimensions of social relations that impact the dynamic of capitalist formations. Imagined economies are discursively constituted and materially reproduced: there is no economic imaginary without materiality. When an imaginary is institutionalised, it transforms these elements and instrumentalities into a specific economy with specific properties. These imaginaries, as semiotic systems, provide frameworks through which one can interpret the social and economic world, while institutions provide the means of embedding lived experience in broader social relations. Thus, the reduction of complexity and uncertainty in the economic sphere involves discursively selective imaginaries and structurally selective institutions that can reduce uncertainty in economic practices. In this sense, imagined economies are discursively created and materially reproduced in different economic contexts and temporal horizons. Economic imaginaries also have their constitutive force in the material world. According to Beckert (2013), economic imaginaries do not deny the relevance of the social and economic structures that represent their material configurations. Indeed, economic imaginaries permit the identification of some of these economic activities, turning them into objects of observation, calculation and governance. Thus, the cultural political economy approach distinguishes two types of economy: one is the ‘actually existing economy’, which represents all economic activities, and the second are the ‘economies’ as a narrated system, one which is a more or less coherent subset of all these activities within specific spatio-temporal frameworks.

METHODOLOGY The main research aim was to understand and assess to what extent economic actors in the food startup economy, in Italy, produce and articulate discourses about economic future scenarios in the online and offline domains. To tackle this multi-dimensional perspective, the research project was grounded in an interpretative paradigm (Schwartz-Shea & Yanow, 2013), one which allowed for the investigation of the research object via reflexive interpretation. The exploratory strategy adopted a qualitative mixedmethod approach with a concurrent nested strategy (Creswell, 2002) in order to simultaneously collect both qualitative and digital data during the data collection phase. More precisely, 42 semi-structured interviews were conducted with different economic actors of the Italian food startup scene, such as food startuppers, venture capitalists, angel investors, incubator managers and startup community managers. The sample of startuppers interviewed is mainly composed by 592

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white men, aged between 20s and 40s and they have earned at least a university degree. They were the founders or co-founders of the startups which represented their first experience as self-entrepreneurs. The sample of venture capitalists, angel investors and incubator managers is mainly composed by white men, aged between 40s and 60s, with university degrees earned in international business schools. Eight months of multi-sited ethnography2 was also conducted, which involved following the work activities of startuppers in different incubators and various events, such as Startup Grind, the Startup Crush Test and FuckUp Nights. Moreover, according to the digital methods paradigm (Rogers, 2013), the Twitter narrative production of Seeds & Chips 2017, the most important Italian event on food, digital technologies and startups, was analysed. Twitter was chosen (Caliandro & Gandini, 2016) as it is the social media platform most used by organisers and participants for sharing information, opinions, links and images about food innovation. The data analysis was based on a dataset of 28,428 tweets collected from 1 May 2017 to 18 May 2017 (one week before and one week after the event). Towards this end, all tweets containing the hashtag #SaC17 (the official Twitter hashtag of Seeds & Chips 2017) were collected using the T-CAT software (Borra & Rieder, 2014). On the methodological side, the research had to tackle many issues related to contextual specificities. Indeed, the choice to focus on startuppers as the subject of the study challenges some traditional assumptions regarding the value of the time–space coordinates of fieldwork and the position of the researchers. Startuppers are characterised by non-linear working practices in a fragmented online and offline environment: they are a despatialised, individualised, highly performative and networked workforce. In order to address these methodological constraints, a context sensitivity perspective (Cardano, 2011) was adopted and the inventive methods approach (Lury & Wakeford, 2012) was pursued. These methodological devices respond to the uncertainty of the social world and represent a contingent solution to specific obstacles. The unpredictability of the field was not considered as a condition to be solved but rather as a productive state for exploration. The data analysis adopted a triangulation approach (Yin, 1994) in three steps. In the first one, explorative semi-structured interviews have been conducted with relevant actors of the Italian startup scene. The thematic analysis of the first 15 interviews allowed to identify the main mechanisms of narratives creation, the communicative devices and moments trough which future imaginaries were shared among the members of the startup community. In the second step, a quantitative and qualitative analysis of the Twitter narrative production of Seeds&Chips 2017 was performed. The former consisted of network analysis (Hogan, 2008) applied to two categories of metadata: mentions and retweets, and hashtags. The latter involved a content analysis of the most re-tweeted tweets, which in turn allowed for the identification of main recurring themes. This event has been also investigated through participant observation. The concurrent adoption of digital and qualitative techniques allowed to gather data from both online and offline realms. In the last step, a thematic analysis of the 42 interviews was triangulated with the digital analysis of Twitter data about Seed&Chips 2017 and with ethnographic observations of the events promoted by the Italian startup community in order to enhance understanding of the co-dynamic interaction between the offline and online narratives.

IMAGINED FUTURES FOSTER MARKET CREATION Economic actors constantly compare fictions with real facts, in order to remain convinced that the futures they have predicted will be realised in the present (Beckert, 2013). Startuppers must convince 593

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themselves and the other economic actors that their economic imaginaries will become real through their performance. Investors do not require proof of the startupper’s assumptions because they know that it is not possible to know where the future will be directed. However, investors do need the startupper’s economic projections to be coherent with the current present; they need the startupper to propose a realistic business plan with credible growth estimates. A simplified and convincing version of the future allows economic actors to believe that they are able to plan for business activities based on this vision. As Esposito (2011) proposed in her studies on derivatives, in order to legitimate and encourage risky economic actions, the future needs an aura of objectivity. Otherwise, it would depend purely upon ‘the idiosyncratic estimates of each individual’ (p. 136). Startuppers need to create and sustain the realness of their imagined economies. As Searle (1975) argued, this credibility is not embedded in the fictions but is instead anchored in the specific attitudes of the actors who proposed and shared the specified imaginary. Moreover, the exchange of information, opinions or suggestions within startup communities during public events can have a stabilising effect. It can align expectations about futures within a specific economic context, consequently increasing the economic actors’ awareness about the possible outcomes of their business investments. The ecology of narratives fosters the assimilation of potential futures (Holmes, 2009), helping economic actors make business decisions within an economic, social and institutionally coherent framework3. Startuppers need not propose exponential business estimates in their pitches, but they do need to be credible. They must offer something that is achievable, because realness is achievable. In presenting their business idea, startuppers use available data produced by other actors. This makes the economic future that they present credible because if others have already demonstrated that there is a potential market, it becomes more plausible that the startuppers’ future business opportunities will potentially become real as well. Startuppers refer to general market benchmarks in order to present potential business opportunities. The main reason for doing so is that no available data exist about their potential market because this market will only become real in the future. Thus, they use approximate data about similar or contingent markets to sustain their business vision. Moreover, they can also combine the analyses of different market trends in different markets, as in the case of social eating startup services. In this specific case, they can combine data about the attitudes of Italian people concerning outdoor eating with data on the use of sharing economy services. These two types of benchmarks ideally constitute a potential market. Yet, they have no evident and measurable relations with social eating services. Thus, benchmarks function to create the horizon, to make visible a potential future through data from similar markets. These data are characterised by their generality. This is because a horizon with punctual data is one that has already been discovered and investigated. On the contrary, the generality of data shows that in this potential future there are still business opportunities because they have not yet been exploited. Likewise, the lack of data about a potential market also means that there are business opportunities that no one has yet exploited. But, through the use of data on similar markets, startuppers can also show that this horizon can become real. On the one hand, benchmarks demonstrate the realness of the economic horizon; but, on the other, the lack of measurable data – and, broadly, the lack of elements for an empirical evaluation – means that business opportunities can be discovered. Imaginaries must be created, sustained and performed. Startuppers must therefore develop narratives about futures for create ‘coming markets’: markets that are located in the future.

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THE CASE OF SEEDS AND CHIPS 2017 To explore this co-evolutionary constitutive process in the startup food economy and to understand which types of economic actors influence these cultural frameworks, the online and offline narrative dimensions of Seed&Chips 2017 (hereafter #SaC17), the most important Italian event on food innovation, were analysed. Every year, a significant number of food startups, incubator managers, financial investors, food experts and public stakeholders gather at this event from all over the world to discuss the new frontiers of technologies and food sustainability in the global food system. Unlike previous editions, #SaC17 was part of another, bigger event, named TuttoFood. TuttoFood is the largest annual event of Italian food producers. Exhibition areas at this event were divided by market types: seafood, meat, green and bio, oil and pasta. In this context, #SaC17 was the exhibition area dedicated to the future of food production, qualifying #SaC17 as an event dedicated to a new food vision. Indeed, #SaC17 was focused on vertical and indoor farming, an emerging trend in mainstream food narratives. But, another two elements characterised this edition: the presence of Barack Obama and what the organizers of #SaC17 called ‘teenovator’. As Marco Gualtieri, the founder of #SaC17, said during the opening speech: ‘Our priority is to give young people a voice and a leading role at Seeds & Chips. We feel that by giving them the opportunities we can truly help them to shape their future. We cannot talk about innovation and be innovative without them.’ Discourses also include representations of how things are. These imaginaries may be enacted as actual, and they can become real activities, institutions and social relations. In order to explore the representations of economic discourses, three types of analysis outcomes are presented: the network structures, the semantic network and the narrative representations levels.

The Network Structure Of #SaC17 The analysis addressed the following research questions: What is the structure of the online communication flow around #SaC17? To what degree is each user central or peripheral? Who are the main speechmakers? What impact do they have? In order to investigate the communicative centrality of users, the entire network of #SaC17 was visualised. Cluster structures were also identified through the Gephi software. The resulting graph revealed the existence of four main clusters. This visualisation represents what Venturini et al. (2015) called ‘hairball networks’ (Fig. 1). As the figure shows, the ‘Seeds&Chips’ cluster represents almost one-half of the entire network (46%), and the most relevant account is the official account of the event: @SEEDSandCHIPS. The ‘Obama’ cluster constitutes the 24.3% of the entire network, while the ‘Social Reporters’ cluster the 7.9%. Social Reporters is a web agency developed by the organisers of #SaC17 for producing real-time digital content about the event and for supporting online communication. The ‘Ethical Food’ represents the last cluster (3.9%). In the latter cluster, one of the most influential account is @FoodTank, a non-profit organisation that aims to develop a global food community for safe and healthy eaters. Moreover, in order to determine how many influential users structured the narrative of #SaC17, was measured the weighted in-degree score4 of any user mentioned in the sampled tweets . As graph 1 shows, only a small percentage of users had very high weighted in-degree score increases. This result indicates the existence of a set of influential users around whom most of the interactions 595

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Figure 1. Network of #SaC17

Figure 2. Weighted in-degree score

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Figure 3. Weighted out-degree score

were concentrated: these users, therefore, act as hubs of the network. Indeed, only 25 of the 1,119 total users had in-degree scores > 200 (the 2.1%). The weighted out-degree score5 was also measured to better understand how many users produce interactions within the network. Graph 2 indicates that a small number of users were responsible for producing discourses and enhancing the popularity of the event. Out of a total of 3,206 users, only 39 had an out-degree score > 200 (the 1.2%). These 39 users generated 55.7% of the interactions (24,914 @ + RT compared to 44,700 @ + RT generated by the overall network), leading the entire narrative production6. Moreover, these influential users were often those who had high in-degree scores, namely those few users to be retweeted (The high percentage of RT among the total sampled tweets is also notable: out of 28,428 tweets collected and analysed, 22,525 are RT, or 79.2%). To gain a clear comprehension of the relevant accounts for #SaC17, the ego-network structures of the six most influential accounts in the entire network were analysed and compared (Tab. 1). There were two accounts that belonged to the Seeds & Chips cluster: the official account (@SEEDandCHIPS) and the Seeds & Chips founder’s account (@_MarcoGualtieri)7. The second most influential accounts were those of the web agency Social Reporters: @Valerypetrone93 (one of the two supervisors of the Social Reporters team) and @RuralHackit (a research project on digital technologies and farming)8. The third most influential account was the official account of Barack Obama. The ego-networks (Caliandro & Gandini, 2016) of these accounts were also analysed, specifically taking into account the ties that were created when a user mentioned them or retweeted their contents. To acknowledge the actors who were more involved in producing reputation, the first 10 users were identified by the number of directional ties (number of mentions + number of retweets)9 towards each of the six most popular accounts. Moreover, to better understand to which cluster these users producing reputation belong, in table 1 we used the same formatting criteria: bold for Seed&Chips cluster and italic and underlined for Social Reporters cluster. Tab. 1 shows that the users who produced reputation were almost the same (such as @tiziano281078; @giacomo_cav; @PakoMarzocchell; @maricabarile)10 and belonged to only two clusters (Seed and chips, and Social Reporters). This means that a reputation strategy exists: these actors intentionally participated in the narrative around #SaC17 to support the accounts belonging to their own clusters. For example, tiziano281078 and giacomo_cav supported the official account of the event (@SEEDandCHIPS) and the #SaC17 founder’s account (@_MarcoGualtieri). Likewise, @PakoMarzocchell and @maricabarile,

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Table 1. Ego networks of the six most influential accounts: for each, the first 10 users were ordered by the number of ties (number of tweets containing a mention to them or a retweet of their content)

supported the accounts of the Social Reporters web agency (@Soc_rep), one of the two supervisors of Social Reporters team (@Valerypetrone93) and the research project Rural Hack (@RuralHackit). Social Reporters is a web agency developed by organisers for supporting online communication about #SaC17. Why, then, its official account does not support the @SEEDandCHIPS and @_MarcoGualtieri accounts? The Social Reporters agency sought to exploit the #SaC17 for branding activities (Arvidsson, 2005): to make their presence visible during the event. From the analysis of the communicative flow emerged that the structure of #SaC17 was vertical: it was almost closed in on itself and it was animated

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and managed mainly by a few accounts. From this communicative flow, paradoxically, the food startups were excluded. It was intentionally managed by organisers of #SaC17 and the Social Reporters team attempting to control the narrative and to direct the interactions. Another interesting element was the production of Barack Obama’s reputation. Indeed, his account was the third most influential, but @BarackObama had not tweeted once about the event, and the hashtag #SaC17 was never used. But despite this, his official account was not just the third most influential but was also the most relevant in the second largest cluster of the entire network. On the one hand, this could be the effect of Barack Obama’s reputation: He is a powerful actor who was able to influence the online narrative even if he did not tweet about the event. However, considering the actors who were interacting with the Obama account (see Tab. 1), it is possible to affirm that the organisers and the web agency sought to make Obama’s presence visible in order to increase the relevance of the event, thereby giving more credibility to the imagery produced by #SaC17. As Pablo, the founder of a vertical farming startup, affirmed: ‘I am sure our startup will reach economic success. I mean, today Barack Obama talked about vertical farming as a future solution for facing the climates changes’. The will and capacity to control the narrative production about food innovation also emerged in the offline dimension. To understand the structure of the communicative flow of #SaC17 in the offline realm, the exhibition area was considered as a platform in which different actors produced various narratives. This allowed the recognition of three different levels through which the communication was organised: the Vàzapp arena, the Iimerge matching areas and the food experts panels. Vàzapp is a rural hub based in southern Italy that gathers young farmers and creates knowledge exchange through the creation of relationships. The founder aims to create new ideas and motivation in agriculture. In the #SaC17 exhibition area, the Vàzapp team created an arena composed of hay bales. This space was organised and prepared as a little farm. During the events, the Vàzapp team organised discussions mainly focused on the ethic of the new food economy. It was during these discussions that the affective investment of participating in the #SaC17 community was shared and sustained, and in which different types of participants (farmers, politicians, startuppers, food experts) conversed about how to combine new technologies with local traditions. The Vàzapp space was meant to be the ethic cluster in the exhibition area, where participants were expected to produce narratives about the values of the new food economy. However, these meetings were managed and oriented by the Vàzapp team who capitalized the outcomes of the discussions. Another level of the communicative structure was created around the matching areas of Iimerge, which is an Italian online food trading platform. Iimerge gives opportunities to startups to meet potential investors or commercial partners. Thus, this area was characterised as producing narratives about the food startup economy and towards which future it was directed. The third level was characterised by the food experts panel discussions. #SaC17 organised many of the discussions with food stakeholders, but these were mainly focused on understanding and demonstrating food production. Thus, these panels represented the moments in which a new vision of the food economy was created. There was also a fourth level, which was composed of all startups that had a stand in the exhibition area. These startups participated in the offline narrative of #SaC17, but as single entities. This means that Vàzapp, Iimerge and the panel discussions can be considered as cluster areas aggregated around specific topics and interactions, whereas startups only promoted their solutions. Yet, they were still a part of the narrative.

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Table 2. The actor who directs the communicative flow in offline and online dimensions OFF LINE

ON LINE

Seeds&Chips

Social Reporter

Iimerge

Rural Hack

VàZapp’

During the analysis emerged clearly the intention of these actors to control the narratives about food innovation. As Giorgio, who works for the Future Food Institute (which is an important partner of #SaC17), remarked some days after the event: ‘This is our mission! We want to create an active dialogue between small companies like startups and big institutions like the Ministry of Agriculture! Our goal is to involve the institutions and innovators, overcoming every kind of distrust’. On the one hand, the online dimension was controlled by actors who were part of the event: the official account of #SaC17, the Social Reporters web agency and the Rural Hack research project. Actors who knew each other in real life and who had planned before the event knew how to manage communication online. On the other hand, the offline narrative was also organised around specific areas and topics. The organisers divided the exhibition areas in order to cover different dimensions of the new food economy: innovative technologies, ethics and future scenarios. But, startups were quite absent, as they did not participate in the common narrative as individual entities focused on proposing their own solutions. In both dimensions, the #SaC17 organizers seemed to hegemonize the narratives about the future of the food economy. They sought to exploit the free labour of participants (Terranova, 2000) by, on the one hand, controlling information and knowledge produced around specific topics, and, on the other, by controlling the ethic surplus (Lazzarato, 1996). The Seeds & Chips organisers aimed to be the hub of the entire network of food innovation, also with the support of Social Reporters and Rural Hack, in order to catalyse the affectivity of participants. This is because, as Arvidsson (2018) argued, the ability to acquire a reputation can be capitalised on elsewhere.

The Semantic Network of #SaC17 Hashtags coordinate conversations around a specific issue and also shape the dynamics of the publics that can be activated around them. The use of hashtags is a popular way to represent the context of a tweet. They can also support the spontaneous creation of networks based on shared interests. Indeed, #SaC17 can be considered as a ‘praeter hoc’ hashtag (Bruns & Stieglitz, 2014), which is a specific type of hashtag adopted and used during particular events. Thu far, the analysis of hashtags related to #SaC17, starting from the reconstruction of the semantic network, was discussed. From the dataset, 1,060 unique hashtags, which occurred 78,681 times (the same hashtag could occur several times in the same database), were identified and extracted. The 10 most used hashtags were as follows: #SaC17 (28428), #FoodInnovation (7252), #FoodTech (4855), #SocialReporters (4643), #AgTech (1908), #RuralHack (1682), #SeesdsandChips (1321), #Food (1152), #Obama (988), #Startup (965). These 10 hashtags revealed an interesting dynamic. As mentioned above,

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hashtags coordinate conversations around specific issues. #FoodInnovation, #Obama or #Startup represented topics of the #SaC17 event. #SocialReporters and #RuralHack, in contrast, were not expressions of discourse semantics, but were rather the outcome of a branding strategy. In other words, although Social Reporters and Rural Hack sought to direct the online narrative as Seeds & Chips partners, they also attempted to exploit this narrative for branding purposes. They used their account names as hashtags in order to associate their brands with the #SaC17 narrative. Tab. 2 measures the frequency of the hashtags that were associated with the hashtag #SaC17 (one of the outcomes related to the co-hashtag network analysis). This table allowed for the identification of how Social Reporters and Rural Hack tried to link their brands in different ways to the issues covered in the event. Considering the number of times these associations occurred, it is clear that they intentionally used this connection strategy. Table 3. Hashtags associated with #SaC17 Hashtags

Times

#sac17; #socialreporters

1,335

#socialreporters; #ruralhack; #sac17

1,121

#SaC17; #FoodInnovation

794

#SaC17; #FoodTech

743

#RuralHack; #socialreporter; #FoodInnovation; #SAC17

420

#SAC17; #foodinnovation; #socialreporters

307

Once the specifics of these two hashtags were clarified, the analysis continued, producing lists of hashtags that co-occurred with #SaC17. The co-occurrence analysis, which consisted of counting the number of times that a given hashtag co-occurred with another11, allowed for the identification of a cultural discourse around a specific issue (Marres & Gerlitz, 2015). In this research, the co-hashtag analysis was developed as follows. First, from the dataset of 1,060 unique hashtags, the hashtags that occurred more than 10 times with #SaC17 were extracted, totalling 382 hashtags. Then, these hashtags were identified and classified into seven issue typologies: #foodinnovation, #liveevent, #startup, #madeinitaly, #socialchange, #obama, #innovatoridentities. Finally, the relative frequency of each typology was assessed with descriptive statistics. The #foodinnovation category collected hashtags that were linked to new technologies or services in food production and consumption, such as #bigdata, #verticalfarming and #AI. The second typology was the #livevent. Hashtags in this typology were not a matter of discussion; they simply informed the audience, the online publics, what was going on during the event. The third typology was #startup, which collected a number of issues related to the startup economy and some other relevant dimensions, such as #siliconvalley culture, #investments and startup performativity with #speech and #networking. The fourth typology collected hashtags that emerged around #madeinitaly and #expo2015. Indeed, in this typology there were discussions not only about the quality of Italian food, but also about the role of Milan as a food city. #Socialchange is the fifth typology which collected all the hashtags that expressed the need to change the agrifood structure towards a more sustainable food system. The sixth typology was characterised by hashtags that mentioned Barack Obama and US international organisations. The last typology was #innovatoridentities. As mentioned above, the organisers of #SaC17 portrayed the

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Figure 4. Co-hashtag network typologies

event as an occasion for a new and young generation of innovators to present their visions and ideas. #Teenovators, #eroicontrolafame and #youngeneration are all hashtags that express the will to qualify #SaC17 as an event dedicated to the future of the food economy. Table 4. The most 12 used hashtags for each typology

Moreover, to understand whether these cultural discourses were similar across the four network clusters (Seeds&Chips, Obama, Social Reporters, Ethical Food), the narrative production of each was analyzed12. The following pie charts (graph. 4, graph. 5, graph. 6, graph. 7) highlight how a common cultural narrative about the new food economy was shared among the four clusters. Some slight differences were evident, such as the high percentage of hashtags about #Obama in the Obama cluster, but were attributable to the effect of the clusterisation process.

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Figure 5. Co-hashtag network typologies - Seeds&Chips cluster

Figure 6. Co-hashtag network typologies – Social Reporters cluster

The Narrative Levels of #SaC17 In order to understand the narrative levels around #SaC17, a content analysis technique was employed. The focus was placed on understanding what type of discourses the four clusters produced. To do so, the

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Figure 7. Co-hashtag network typologies – Obama cluster

Figure 8. Co-hashtag network typologies – Ethical Food cluster

sample was selected choosing a number of tweets proportional to the different narrative production of each cluster13. Through text coding, the tweets were classified using four categories: new technologies, Obama, publicity and vision.

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Figure 9. Narrative levels – Seeds&Chips cluster

Figure 10. Narrative levels – Social Reporters cluster

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Figure 11. Narrative levels – Obama cluster

Figure 12. Narrative levels – Ethical Food cluster

The categories differed reasonably among clusters. In Seeds & Chips, 72% of the tweets were about

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publicity. In other words, these tweets made the event visible. The organisers produced tweets about what was happening during the event, such as a new panel discussion, the presence of relevant speakers, updates about the program, and speeches by young entrepreneurs. Indeed, in order to qualify Seeds & Chips as an event dedicated to young entrepreneurs, the organisers also coined a new term: #teenovators (fig. 13). Moreover, competition among the most active online users was another element of publicity. The organisers pushed participants through a public ranking to produce tweets (fig. 14). In the Social Reporters cluster, there was also a high percentage of publicity tweets. This underlines, once again, their common marketing strategy. Meanwhile, tweets about technologies had a low presence among the clusters. These tweets had the function of anticipating and explaining the future of food (fig. 15), developing a narrative that aimed to create the coming markets for innovative solutions. If the future will be as they showed, then people will need specific products and services. Another element was the presence of tweets that sought to inspire the participants of Seeds & Chips. The vision category collected all the tweets mentioning the future of food and how people must act for a better world, especially for young people (fig. 16). Indeed, as Beckert (2013) argued, imaginaries do not need to be right or wrong in order to affect the future. It is sufficient to inspire beliefs and actions by some powerful actors. Meanwhile, the tweets collected in the Obama category were mainly referred to two moments of the Barack Obama speech: the ‘waiting for Obama’, when users tweeted about waiting for Obama’s speech, creating a growing emotional tension of expectation, and the ‘Obama speech’, when users simply quoted Obama’s speech.

CONCLUSION The article addresses two main points concerning the study of the narratives about imagined economies. The first is methodological. A triangulation approach was employed with qualitative and digital data, exploring to what extent economic actors produce and articulate discourses about economic future scenarios in the Italian new food economy. This methodological procedure allowed to tackle two main challenges. The first challenge is linked to the nature of the research object. Indeed, startuppers are a highly flexible workforce that produces discourses in both the offline and online realms. In order to follow the production and co-production of future imaginaries on both levels, was designed a methodological strategy to integrate qualitative techniques, such as semi-structured interviews and participant observations, and digital methods. The second challenge concerns the effect of what Marres (2017) calls the ‘power law’. In her view, focusing only on the online dimension, as a privileged place for the production of discourses, would have generated only a partial view of the research object. This is because Twitter does not contain the entire landscape of imaginaries about food innovation. Thus, in order to investigate the co-dynamic interactions between the offline and online narratives, in this study both dimensions were considered. The second challenge is linked to the mechanisms and practices through which these discourses and stories were produced. It was shown that #SaC17 does not represent a hybrid forum (Burgess et al., 2015), such as a free environment where different types of actors are involved in vibrant discussions about the future of the food economy. What emerged, instead, was the will of the organisers to control and direct the entire online and offline narrative production. Indeed, they are supported by a web agency that produces a relevant amount of digital content in real time. The main strategy to develop the reputation of #SaC17 takes place through three mechanisms: exploiting the presence of a powerful actor such as Barack Obama; engaging teenage entrepreneurs (#teenovators); producing a huge number of online interactions. As the analysis has shown, the startups do not contribute to the online 607

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Figure 13.

common narrative. They are only engaged in discussing their business ideas offline just to make their presence visible. Thus, #SaC17 organisers use the presence of startups to legitimize themselves as the main hub of the Italian food innovation economy. This means that #SaC17 organizers aim to bridge the gap between startups and public institutions. In order to do so, they must be credible on both sides. For public institutions, #SaC17 is a trustworthy partner because it appears as the hub of the innovative food startup economy. While, on the other side, the presence of relevant actors who inspire and sustain

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Figure 14.

the startup vision, such as Barack Obama, represents an element of credibility for startuppers. Another outcome of the analysis deals with the creation of the market for innovative food solutions. The future markets are created by startuppers and the other economic actors (incubator managers, angel investors, venture capitalists, etc.) through the production of narratives in the online and offline domains. Their contribution to sustain positive imaginaries of the food economy can be framed as ethical labour

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Figure 15.

(Coleman, 2008). These discursive practices generate an ethical surplus (Virno, 2002; Hardt & Negri, 2004) which create and maintain a shared ethos (Arvidsson & Peitersen, 2013) within the Italian food innovation community. As Arvidsson and Peitersen (2013) point out, the main virtue of a community ‘is related to contributions to the ability to imagine the ethical potential of a common project’ (ivi., p. 299). This emotional climate allows economic actors to cooperate for the creation of future markets and allow them to manage conflicts linked to market competition. In the case of #SaC17, the construction

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Figure 16.

of a common project emerged quite clearly, for example in the extensive use of the ‘change the world’ narrative rooted in the Californian ideology (Barbrook & Cameron, 1996). Moreover, the presence of Barack Obama lent credibility to the market creation, which is reinforced by the sense of belonging widespread among the economic actors involved in the food innovation community. In conclusion the work highlights a co-evolutionary process between online and offline realms. On the one hand, online narratives allow economic actors to perform in radical uncertain economic contexts, such as the startup

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economy while, on the other hand, the offline practices give legitimacy and credibility to these potential future scenarios.

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The article was conceived and discussed jointly by the two authors. Vincenzo Luise curated the following paragraphs: ‘Imagined Futures Foster Market Creation’, ‘The Case of Seeds&Chips 2017’ and ‘Conclusion’. Patrizio Lodetti curated the following paragraphs: ‘Introduction’, ‘Conceptual Background’ and ‘Methodology’. The 42 interviews and the eight months of multi-sited ethnography were carried out by Vincenzo Luise. Despite uncertainty, individuals act according to a shared narrative of the future, transforming imaginaries in present realities. This applies primarily to behaviours in the economic sphere, but not only, as emerges, for example, from studies that explain fertility decisions within a narrative framework (Vignoli et al.,2020). In-degree is calculated for each user and consists of counting the number of sampled tweets in which they had been mentioned (@) or retweeted (RT) by other users. Out-degree is calculated for each user and consists of counting the number of their tweets mentioning (@) or retweeting (RT) other users. The same result was generated when checking those accounts that tweeted more than 100 times: 39 out of a total of 3,206 accounts (the same who have a high out-degree score). These 39 users produced 16,930 tweets out of a total of 28,428 – that is, 59.5% of the entire narrative production. In tab. 1 these accounts are marked in bold. In tab. 1 these accounts are marked in italic and underlined. In tab. 1 this information is shown in brackets next to the users’ name The accounts marked in bold, and italic and underlined are mentioned at least three times names of these reputation’s producers are mentioned in the ego-network of at least two of the six most influential accounts There were 22,102 tweets that contained both the hashtag #SaC17 and at least one other hashtag, representing 77.7% of the total dataset of tweets. The first 100 most used hashtags for each cluster were extracted and classified according to the seven typologies obtained from the co-hashtag network analysis (graph. 3) The selection process was different for each cluster because of the number of tweets each cluster produced and because of the need to have the same number of tweets for each cluster. For the Seeds & Chips cluster, 470 tweets that were re-tweeted more than seven times were analysed; for Social_Reporters, 432 tweets that were retweeted more than four times were analysed; for the Obama cluster, 378 tweets that were retweeted more than two times were analysed; for the Ethical food cluster, 48 tweets that were retweeted more than one time were analysed.

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Chapter 35

To Be #Celts Today:

Features of a Neopagan Cult in the Social Media Francesco Amatruda University of Salerno, Italy

ABSTRACT The aim of this chapter is to define the characters of the online neo-Celtic Italian society, especially their religious beliefs, through the observation of their activities on blogs and social media such as Facebook and Twitter. Social media became, in fact, the main diffusion channel for these religions, replacing forums and other kinds of online communities as virtual places where people are allowed to interact with others who share their own spirituality. Within neo-paganism, some groups belonging to this religion started, during the last decade, identifying themselves with a more specific name, that is neo-Celtic instead of neo-pagan, that clearly defines the group as a part of pre-Christian cultural heritage. In this chapter, the author will attempt to define the characteristics of these neo-pagan groups focussing on their selfconstructed identity and their relationship with the larger society.

INTRODUCTION The fascination of ancient Nordic culture today imposes itself on the masses in many different ways. It can be noted that in videoludic, cinematographic and literary productions the interest in the world of the Celts and ancient Nordic civilizations increases over time. In this context are inserted successful television series such as Vikings and the very recent Barbarians, which narrate in a fictional way the heroic deeds of warriors of an ancient world who impose themselves as models of honor and courage in every part of the world; a world populated by people who admire the warrior ethic, determination and greatness displayed by these Nordic heroes. Among the users of these productions inspired by the ancient world of northern Europe we also find people eager to imitate the heroes of these narratives, or who try to forge their appearance, lifestyle, even spirituality, on models from the ancients Celtic and Norse worlds. Among those who are fascinated by the Celtic and Norse world we find not only fans of DOI: 10.4018/978-1-7998-8473-6.ch035

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specific entertainment products, but also people who have decided to start a spiritual journey modelled on the religiosity of the peoples of ancient northern and central Europe, i.e. practitioners of neopagan religions. The aim of this work is to provide some information about some Italian neopagan communities that use social media as the main communication channels and meeting points. By analyzing the characteristics of some groups chosen as a field of study, the author will indicate which are the main characteristics of these groups and how the members build their identity with reference to their religious life. This analysis will be conducted by collecting data from some social pages selected by the author based on the productivity of the material, the activity of the members, the frequency of the interjections and the relevance with the objectives of the research. The collected data will be processed purely offline in a qualitative survey aimed at identifying political, psychological and cultural inclinations of the members of these groups in order to provide elements on which to make an analysis of the identity of the users and the group itself and to recognize the mechanics behind the process of self-determination of the individual in this given cultural context.

BACKGROUND In recent years, with the spread of social media, the distances between individuals have narrowed and new methods have emerged to aggregate and to live group experiences. Social media has been one of the most important resources for the entire world community during this period of pandemic which has caused the inability of individuals to physically come together regardless of the reason that motivated these meetings. An interesting detail, in this regard, was the attitude of the various religious groups on the occasions of particular festivals or sacred periods that without the restrictions in force would have represented opportunities for meeting. The rituals that before the pandemic usually took place in the presence of the religious community have moved online, an example is the live videos broadcast on the various social networks by the priests who officiated these rites and which were followed by the practitioners of the cult. Although the current period has corroborated the tendency to live part of one’s spiritual life on social networks, the phenomenon is not new. The presence of religion on the internet is a known and attested phenomenon, as well as much discussed in recent decades. The history of studies on religious phenomena on the web is retraced by Giulia Evolvi, (2021) who traces in detail the evolution of the problems and research themes by describing the methods applied in the various works and some examples on some cases of study. Evolvi, and not only in the article previously mentioned, takes up the image already used by Højsgaard and Warburg in a volume edited by them in 2005 as a “wave” to frame what the different themes and methodologies relating to research have been in recent years on religion on the net and recognizes four. Giulia Evolvi recognizes that in the fourth wave the focus of the research produced has shifted towards problems such as identity, ethnicity and authority, and in a 2017 article by Campbell and Lövheim the authors underline that in this phase research is no longer conducted only online but also face to face with the cases examined. The author of this paper, for reasons related to the pandemic, decided to carry out a more classic analysis than the most recent ones on the neopagan communities on the net with which he came into contact, focusing his interest on the theme of identity, of his construction, negotiation and self-affirmation by reading the posts and analyzing the images and videos shown in the virtual spaces. Starting therefore from the definition of cyber-religion offered by Højsgaard (2005, p. 62) and passing through Lövheim and Linderman (2005), we can define the boundaries of what to mean as religion in 616

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a virtual space and on what are the main mechanics behind the construction identity of users on the network. To these reflections must then be added the investigations that have followed over time on the problem of identity with particular regard to the neopagan context.

IDENTITY IN NEOPAGAN COMMUNITIES ON LINE Already in the last years of the last century there were several websites dedicated to neo-pagan culture and religion. These virtual spaces, which Cowan calls cyberhenges in a 2004 study of the same name, are seen by the author as spaces where followers of neo-pagan cults meet to perform magical rituals and discuss their religious reality. In the work, Cowan highlights a very interesting theme about the presence of neo-pagans on the net, namely the theme of identity. Cowan recognizes the possibility offered by the web to build one’s own new identity with which to interact with the reference community. He noted, in fact, that the creation of an avatar and the possibility of assigning it appearance, gender, race and more in an arbitrary way allowed users to build an identity different from their own, to change it and modulate it according to the context and to give it a name which he preferred. Cowan, in this regard, tells us: Rooted in the recovery of a “true persona,” modern Pagan identity is often regarded as the instantiation of a more authentic self, a self that reflects the spiritual path one has chosen to follow and is often reflected in the Pagan name by which one has chosen to be known. Cowan, however, after introducing the concept of performance of identity, explains how much in his opinion the creation of a virtual self cannot completely disregard who builds it. In the space of a few years, however, the situation changed considerably. When the network of contacts on social networks coincides with those that an individual has in their offline life, the information about themselves offered in the creation of a multimedia identity becomes more corresponding to reality (Ellison et al 2007). Marwick in 2013 to move the discourse on identity and authenticity of the same on the level of the modulation of interjections that on social networks becomes difficult but necessary to keep the various aspects of a person’s life well separated. These concepts of identity, as well as the increasingly widespread tendency to offer truthful data and names on Facebook and other social networks opens the way to the possibility of analyzing cultural elements of individuals and groups without incurring errors of evaluation dictated by the arbitrariness of the posted material and the identity of the individual.

ONLINE COMMUNITIES AND SOLITARY PAGANS Thanks to the diffusion on the net of practitioners of neopagan religions, we have witnessed the consequential birth of groups on the main social platforms and among the forums that make religion their main topic of conversation, to which historical insights and discussions on the most disparate topics go hand in hand, like cooking, fashion and gold smithing connected by the red thread of the ancient origin of the techniques used in these fields, as well as the materials and ingredients with which products of all kinds are made. From the early 2000s to today, the neopagan interest groups on the internet have had a very wide diffusion, there are dozens of forums and blogs, and they range from those more rigorously focused on religion to those who totally abandon spirituality to devote themselves to esoteric practices 617

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such as reading of tarot cards or crystallomancy. Over the last few years, the virtual meeting place of these groups has changed. In fact, if in the early 2000s it can be witnessed a very strong participation in blogs, thanks to the fact that the main social platforms to which we are used today, namely Facebook, Twitter and Instagram, were not yet widespread or were not yet born, now the groups meet on social networks or communicate directly with each other via instant messaging apps. Many blogs are still active, albeit with a much lower participation than in the past, and currently often perform an archive function. Already at a first glance at these groups we notice the tendency of the members to define themselves with ethnonyms that refer to ancient civilizations. Precisely through the observation of people who embrace the identity of an ancient population in so many aspects of their lifestyle that we have decided to analyse the link between religion and the construction of the identity of individuals who proclaim themselves as Celts. Professing a religion on internet, choosing a virtual place as a meeting place, is not only an alternative to traditional ways of worship, but also a necessity. In fact, in addition to the function of gathering those who already adhere to a specific cult that also finds its place on the web, the places of discussion dedicated to a religion also perform an “evangelical function”, attracting new practitioners who see the network as the only channel of communication suitable for talking about one’s own religious life. The search for online groups is very common among adepts of various forms of neopaganism, such as Wicca, Druidism, or other religions that aim at the reconstruction of ancient spiritualities, and it is thanks to the internet that many users who are not yet followers of these religions who adhere to these types of cults and integrate into circles and covenants. This trend, however, does not imply that every aspect of the individual’s religiosity takes place online, many rituals of Wicca or other neopagan religiosities require the presence of the entire group in a specific place and therefore the virtual religious experience cannot make up for it. As shown by the study by Helen A. Berger (2019), a large proportion of the practitioners of pagan religions can be identified as solitary pagans1. The term was born to indicate practitioners who, while not disdaining the sharing of their religious life, decide or are forced to live an important part of their spirituality in isolation. This tendency occurs due to the pagan’s need to undertake a spiritual journey of study and selection of ideas and rituals to be accepted in their religious life, but the isolation is also often motivated by the practitioner’s fear of being victim of abuse or discrimination from part of the community in which he lives which in most cases has difficulty in approaching pagan relations without prejudice (Higginbotham 2002). Since the religious path to be undertaken is potentially different for each individual, we find ourselves having to divide neopaganism into different religions which, although sharing common values, evolve in a diametrically opposite way from each other, often also entering into ideological conflict between them.

CASE STUDY Neopaganism is the term commonly used to define religions which, in the rejection of Christianity and in the attempt to establish a closer relationship with the divine, refer to the ancient pre-Christian cults, especially European and Middle Eastern cults. Although not in an openly declared manner, the term appears to be rejected by the groups examined, while the term Paganism is accepted and often used. The same use of the word “pagan”, however, is not the only way in which the practitioners of these religions want to be called, often, as anticipated, the practitioners of neopagan religions tend to refer to themselves with ethnonyms. In a Facebook group whose interests range from Celtic religion and Norse way of life to medieval times, for example, a survey was administered by some administrators to ask 618

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members how they identified themselves. The vast majority proclaimed themselves Viking, tending to be unaware that the Vikings were not a people but the warriors who made raids on various coasts of Central and Northern Europe and the British Isles in medieval times, others decided to name themselves as Norsemen, others as Celts, up to those who chose to identify with a specific Celtic tribe that occupied the territories in which these users currently reside. It should then be clarified that within the neopagan currents there are fringes of practitioners of very specific religions of various types, such as Roman, Greek or Celtic, and Wicca. The different neopagan groups are divided more or less clearly according to the practitioner into two religious orientations, the reconstructionist one and the eclectic one. These two terms indicate the method according to which the ritual and doctrinal corpus that each group or individual decides to follow is formulated. The reconstructionists tend to draw their knowledge and religious action from a single ancient religion, collecting the most spontaneous elements of the reference religion through literary, archaeological, epigraphic and iconographic sources in an attempt to re-propose the rites in the manner most similar to the original. The more eclectic fringes, on the other hand, tend not to choose a single religion to inspire them, but in a very wide-ranging study path they frame some elements of the cult that they decide to adopt by inserting them into their religious life. These two tendencies of worship, although distant, do not necessarily exclude each other, but rather, the specificity of the cult, which comes to differentiate from individual to individual, opens the possibility to the coexistence of these two different realities. However, what is interesting to note is that the different religious orientation is often accompanied by very extreme ideological and political positions. Those who adhere to a Celtic-style religiosity can have both reconstructive and eclectic approaches, and in addition to adhering to stricter forms of paganism we also find them among the ranks of Wicca. The field of observation investigated consists of some groups on Facebook, in pages oriented to interest in paganism that we meet in other famous social networks, and finally blogs and forums of neopagan groups. To these groups has been added the official sites of internationally recognized religious bodies. As regards the methods of approaching these groups, it must be clarified that the researchers had various difficulties in integrating. Groups that openly profess themselves neopagans have been reluctant to let the researchers use the information gained from the observations of the pages they published and, in some cases, have decided to dismiss the observers. In a first phase of the selection of the field, in fact, the author had tried to be accepted by some neopagan groups by openly declaring his intent to observe. In 2 Facebook groups that have joined the field, the author has not met with resistance from the administrators, but has had difficulty interacting with some members of this community who avoided interacting with him. Other groups did not accept the presence of the author inside, some replying that it did not seem right to welcome someone with this intent and others not responding to the request for access. Following these experiences, the author decided to join a group without declaring his intent as an observer a priori. Having detected the presence in this community of interesting observational elements such as the respect and public observance of holidays through livestreaming, of lessons given by a priestess on various aspects of their religiosity and spirituality, and other events with a goliardic character such as masked parties and historical re-enactments, the observer decided to ask the administrators to use the collected material. In this case, the consent was given as long as the anonymity of the group and its members was respected. A final case to report is that in a group in which the observer had entered without expressing his intentions, after being accepted, he was promptly thrown out by another administrator for gender reasons. The researchers therefore examined 4 groups on Facebook, 2 personal profiles on Twitter and 2 between blogs and forums. The Facebook groups recorded a greater number of men ranging in age from 20 to 50 619

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years of age with very few members younger or older. The observation period lasted six months, from the beginning of May to the end of October, plus a few days to record the data about the celebration of Halloween (Samhain for most of the members of the group), in which the catch of the new shared material is displayed daily. Similar speech applied to the two pages on Twitter, held by two women between 30 and 35 years old. The taking of data from blogs and forums took place in a totally asynchronous way. The choice to practice a religion on the net is also motivated by the will of the individual to keep himself anonymous. The first survey aimed at selecting a field of study moved on the main social networks, within which we were able to notice the presence of a large number of groups of people who identify themselves as Celts and who follow pagan themed pages. Most of the groups that deal with paganism and Celticism are private and often in order to select new members they propose short questionnaires aimed at sifting through the reasons that push users to join these groups and here they report some regulations to which it is necessary declare to comply in order to have the application for entry approved. A very interesting detail that emerges from the regulations and from the questions asked at the entrance to users is the tendency of these groups to clarify that they are not willing to welcome people who do not have a keen interest in paganism and who therefore could take the issues discussed in the posts little seriously. The tones adopted are often very aggressive, demonstrating the tendency of the leaders of these groups to protect themselves and other users. The manifest aggression in addition, is often accompanied by undemocratic statements in reference to the organization of the group: the leaders rule, the others follow the rules. The penalty for those who question the decisions of the directors is expulsion. We find similar characteristics in Wicca groups, with the substantial difference that the regulations are much less cumbersome and are limited to suggesting behavioural guidelines aimed at guaranteeing a peaceful coexistence between users. Analysing the material shared on the numerous groups on Facebook it emerged that the religious theme is treated in very few posts and conversations rarely arise when these are published. On the other hand, although there are fewer posts with a religious theme than those of a commercial nature or that aim at sharing in-depth articles on the history of the Celts, content that advertises internationally recognized Italian pagan groups appears quite regularly. The groups the authors refer to are the OBOD (Ordine dei Bardi, Ovati e Druidi) and the Collegio Druidico. The forums examined have a similar structure to each other, that is a subdivision into topics that mostly deal with themes related to pagan religions. They deal with worship, festivals, history, and both groups have a section dedicated to the exchange of books and the advice of useful readings in order to enrich the spiritual life of the adept. Other sections are used as spaces for the socialization of members. These spaces are called in both cases “Salotti” (lounges) and are the most exciting part of the forum. In these spaces, users feel free to share life experiences, projects, rituals developed by them or spells they formulate, and certainly cordial conversations are often initiated between group members who move away from the religious theme2. These spaces are therefore one of the contexts in which users show themselves more spontaneous, giving free rein to their own expressiveness, also launching into reflections on pagan worship and ideologies. Coming instead to the purely religious sections, one can see subtle differences between the pagan and Wicca groups. The pagan group reports a macrosection called “initiatory path” within which there are various conversations about the history and ideologies of paganism, descriptions on the different orientations that this religion can have and vademecum on initiatory rituals. There is no lack of advice on preliminary readings and books necessary for the beginning of the personal search for one’s own spirituality. The topics are numerous, concerning celebrations, principles of faith, the bases of worship, ritual action or the equipment necessary for these performances. Everything is seasoned with the most 620

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disparate questions from the initiates, and it is precisely in the initiatory character of the cult that a very important theme comes to light, that of the hierarchy. Previously has been said about the rejection of democracy by some pagan groups on Facebook, and in these forums the researchers witnessed a hierarchy of followers: the hierarchies are not only of a religious nature, perhaps dependent on positions of different prestige or the degree of initiation, but they also reflect the role within the forum. Administrators, moderators, senior members, neophytes, are all positions present on the personal profiles of the followers who maintain the same hierarchy sanctioned by the diversity of the status of the individual within the religious group. From here it can be seen a certain intolerance towards eclectic attitudes. Solitary practice is not encouraged, administrators tend to criticize members who deviate from their view of the cult, in some cases even aggressively. The hierarchical organization system, at least with respect to the roles that users play within the forum, is also present in the Wicca channel. The differences between these two realities are however substantial since, in the organization of the Wicca forum the rise to the top of the hierarchical ladder is not based on the role that the user plays within a circle, in fact, in the Wicca world there is an egalitarian principle in which each adept chooses his own religious path and follows it independently alone or accompanied by a group; in this forum each member obtains recognition in the form of promotion thanks to their participation in the life of the community and their study. In this perspective it can be seen that there are no real leaders, but expert guides who tend to encourage their companions to continue the spiritual journey undertaken. The climate in the Wicca forum is certainly more tolerant than in the pagan one and a lot depends on the characteristics of the cults to which they refer. From this it can be noted how the reconstructionist orientation is the most widespread among the ranks of paganism. The eclectic fringe, on the other hand, tends to be closer to the Wicca ideologies that encourage the adept to seek and create their own cultic dimension. Regardless of the orientations, both paganists and Wicca follow some fundamental principles which cannot be ignored. They are: respect for nature and the intention to live in community with it, a positive morality for which the individual undertakes to maintain faith in his own nature in harmony with that of the world around him, and the recognition of a divine which transcends gender but manifests itself in both male and female guise. These first three principles should be recognized by all forms of paganism, but Ronald Hutton (2019) adds other principles that are specific to Wicca: the first is the acceptance of the divine immanent in the surrounding world and the rejection of creationism, the second is the rejection of a divine law, as well as of the concept of guilt and punishment. The dichotomy between reconstructionism and eclecticism has been a debated topic among pagans all over the world for years. This depends on the tendency that Pagans have in search of objective truth, which does not concern the Wiccans, who instead try to live their own religious experience in an autonomous and personal way. On the one hand therefore it can be found a religiosity that is respectful of objective principles, on the other a free and subjective, open and inclusive. This division is, in authors’ opinion, the keystone on which to move in order to go deeper into the knowledge of today’s Celts.

THE NEOPAGAN AND THE CONSTRUCTION OF SELF In the period of observation in the field, some events occurred that can be considered representative about the ideology and behaviour of pagans and Wiccans. In a group of historical interest, not necessarily linked to pagan spirituality, but which welcomes a very high number of practitioners of pagan cults 621

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inspired by the Celtic and Norse world, there was a quarrel between various users on the issue of identity and belonging. A user from northern Italy who calls himself a Viking complained that there were no other “true Vikings” in the group. This provocative statement led to a conversation aimed at framing the characters that make an individual a Viking. A user from southern Italy replied that being a Viking, or feeling one, does not depend on a long beard or other physical characteristics, nor on a geographical origin, but on a bond that one feels towards a culture and the proximity to ethical principles. The comment questioned whether the author of the post was a Viking, and a bitter dispute arose. The author has often openly insulted the user of the comment calling him “Saracen” accompanying everything with homophobic insults and insinuations about the sexuality of the southern boy. The calm responses to these insults have gradually become insults and the post, less than two hours after publication has been blocked and deleted. This exchange offers much food for thought. First, there is a very interesting question about the identity of the users. The dispute began because the author of the post did not feel that the group represented him, the author of the comment, however, felt hurt by the denial of his being a Viking. Both users perceive themselves as Vikings, one for boasted birthright, lineage, or for an appearance similar to what stereotypically has the Viking in our imagination and which would demonstrate a genetic link with the Nordic warriors; the other makes it a cultural question. Precisely the geographic origin or the genetic belonging to an ethnic group become the key elements in the practice of identity construction that the Neopagans pass through. In a different group a girl wondered if other members of that community had done the genetic test to find out their ethnic origins. The responses were varied, but many were opposed to this practice. Some claimed to have reconstructed their family tree up to the fourteenth century, and therefore did not need it to proof their ethnicity, while others claimed that coming from a specific geographical area was more than enough to identify themselves as part of a specific ethnic group. However, only one girl in the comments dealt with the matter differently from the others, although it met with great favour among the members of the community. The girl claimed that she did not want to know the ethnicity of her ancestors because she could have undermined the construction of her own identity. What seems to unite the users and all the groups examined is the strong commitment of the individual practitioner to affirm their identity. In the cases of neopagans with a more reconstructive profile, it can be seen that what most influences the practitioner’s perception of their own identity is the sense of belonging to a specific regional rather than national territory. The practitioner who aims to identify with the ethnic origins of his own region tends to distinguish himself from those who do not share his own roots, raising a barrier between his own group and those who are not part of it in a more or less hostile way. To this group we add those who, on the other hand, do not live in the same areas as those whom they view as their ancestors and build their own identity on the basis of a presumed or real lineage. The markedly parochial spirit and the tendency to minimize the value of one’s nationality appears to be consistent with the principles of the Nouvelle Droite and the ideologies of de Benoist which not only concern politics or the national identity of individuals, but also affect religion3. Despite the closeness of ideologies between the practitioners studied and de Benoist, differences emerge in terms of religious orientation and ways of worship. The aspect that unites observed practitioners and the French intellectual is certainly a pantheistic view of the world and the concept of the divine immanent in nature. Instead, what separates these two ideological currents is the ritual practice. De Benoist proposes to review the ancient cults, without remaining anchored to the ritual practices operated in them, while the neopagan’s tendency is to get as close as possible to the ancient ritual practice. Neopagan ideologies are not dependent on the speculations of de Benoist, who offers a very critical vision of religion and inserts it into a political and social discourse; they are mostly 622

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autonomous even if they find their foundation in a spirit that aims to break the bond of the individual with the current political and ideological system.

FUTURE RESEARCH DIRECTIONS This work aims to offer starting data regarding the Italian neo-pagan communities on social networks with particular regard to those who identify themselves with the name of Celts. Thanks to this mostly descriptive investigation of the phenomenon, it is possible to obtain preliminary information useful for the research of future case studies. It should also be remembered that an investigation into a neo-pagan community would require some work carried out offline by applying research models such as those of participant observation and face-to-face interviews.

CONCLUSION The survey, although it led to results, did not fully answer the problem of the identity and self-definition of neo-pagans, putting an important part of the life of an individual, of his daily life, in the spotlight, but not keeping in mind the many elements that can only be examined through a face to face investigation. The individuals examined have in fact confirmed the vision according to which the online life of an individual is now inseparable from the offline one. The survey, not being able to take into account one of the components of this binomial, focused its attention on identity with respect to social networks, despite the fact that this only represents a part of the totality of reality. What emerged from authors’ investigation is that today some exponents of neopagan communities, or individuals with interest in ancient cultures of central and northern Europe, tend to identify themselves and to define themselves as Celts. The nomenclatory choice is very important because having opted for an ethnonym as a term with which to refer to oneself and not to a name that suggests the religious inclination of the individual is a clear signal of how much the link with the ancient world of the Celts is permeating in every aspect of the life of the individual and not relegated only to the spiritual sphere, but indeed, in some cases it can even be separated from it. Those who approach a neopagan religion or Wicca following an inclination towards Celtic and Druidic religiosity tend to cover every aspect of their daily life with elements that recall that world. We see this trend in the attitudes these individuals have towards their community. In fact, in front of pagan reconstructive groups or individuals we also find ourselves dealing in some cases with people who report the rigid selectivity of their religious life also on a political and social level, showing little inclination to tolerance. Within these groups we have seen discrimination against those who showed themselves to be different in religion, sexual orientation and identity, gender or ethnicity. It is therefore impossible to deny that some fringes of these paganists, in a world that makes the narration of the ancient world a model of life, also insinuate ideologies strongly oriented to an almost totalitarian right. This attitude is not new, as we have witnessed in the Nazi and Fascist times the recovery of elements of ancient Roman and Germanic cults, today we are witnessing the search for an ancestral identity that pushes the individual to identify himself with the name of an ancient people because he does not he feels represented in his contemporaneity. The modern Celt seeks a “self” to put in a competition against “someone else than himself”. He launches into a search for identity to represent himself among the shining but romanticised glories of ancient populations, often reading historical documents with 623

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little criticism, constructing a distorted narrative of the events that occurred. The search for an ancient identity is the search for a space and a group to belong to, in which to feel that one’s values ​​are shared, regardless of how acceptable they are or not by contemporary society. However, the discourse is not applicable to every practitioner or every group. In fact, among the ranks of neopagans we meet highly tolerant people and communities, also open and inclusive. On a religious level, this attitude is demonstrated by the eclectic tendency, which does not reject what is outside its own conception of worship and life, but analyses it critically. Even the eclectic case, as far as the normal logic of worship is concerned, has exasperations. Often the eclectics tend to approach irreconcilable rituals and ideologies, resulting superficial or in any case eccentric. Regardless of the differences in orientation, most of the Neopagans are open and tolerant, inclined to dialogue that leads the individual to mature an increasingly articulated vision of the world and of society. Examples of distrust that we have found on our skin even from very open and inclusive groups arise from the need that these groups have to defend themselves and from the need to evaluate anyone who comes very close to their world. And it is also in this that we see that the choice of an individual to approach Neopaganism is, as anticipated, the search for a safe space in which to be able to freely express himself, his spirituality, his creativity and his inspiration, without being judged. The Neopagan builds himself through religion a safe haven in which to highlight all aspects of his individuality and in which he can embrace an identity that he feels intimately his own and that has often been questioned. To conclude, it is necessary to clarify that these first elements derived from netnographic observation reveal only the tip of the iceberg. There is still much to be addressed about the life of the Celts of our time, and there are many realities to be analysed with the aim of fully understanding the characters of these communities, both religious and simply cultural, their way of approaching life, contemporary politics, and the infinite cultural realities that have arisen within those who have decided that the 21st century is close to them, and who seek the true essence of their life in the mists of time.

REFERENCES Beckerlegge, G. (2017). Computer-mediated religion: religion on the internet at the end of the twentieth century. In From sacred text to internet (Vol. 1). Routledge. Berger, H. (Ed.). (2005). Witchcraft and Magic: Contemporary North America. University of Pennsylvania Press. doi:10.9783/9780812201253 Cowan, D. E. (2005). Cyberhenge: Modern pagans on the internet. Psychology Press. De Benoist, A. (1966). Qu’est-ce que le nationalisme. GRECE. De Benoist, A. (1981). Comment peut-on etre paien. Albin Michel. De Benoist, A. (1994). Nationalisme:Phénoménologie et Critique. GRECE. De Benoist, A., & Molnar, T. (1986). L’Éclipse du sacré: discours et réponses. La Table Ronde. Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “friends:” Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143–1168. doi:10.1111/j.1083-6101.2007.00367.x

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Evolvi, G. (2021). Religion, New Media, and Digital Culture. In Oxford Research Encyclopedia of Religion. doi:10.1093/acrefore/9780199340378.013.917 Germinario, F. (2002). La destra degli dei: Alain de Benoist e la cultura della Nouvelle droite. Bollati Boringhieri. Højsgaard, M., & Warburg, M. (Eds.). (2005). Religion and cyberspace. Routledge. doi:10.4324/9780203003572 Højsgaard, M. T. (2005). Cyber-religion. Religion and Cyberspace, 50-63. Krüger, O. (2005). Discovering the invisible internet: methodological aspects of searching religion on the internet. Online–Heidelberg Journal of Religions on the Internet. doi:10.11588/rel.2005.1.385 Lewis, J. R. (Ed.). (1996). Magical religion and modern witchcraft. SUNY Press. Lövheim, M., & Campbell, H. A. (2017). Considering critical methods and theoretical lenses in digital religion studies. doi:10.1177/1461444816649911 Lövheim, M., & Linderman, A. G. (2005). Constructing religious identity on the Internet. Religion and Cyberspace, 121-137. Marwick, A. E. (2013). Online identity. A companion to new media dynamics, 355-364. McClure, P. K. (2017). Tinkering with technology and religion in the digital age: the effects of internet use on religious belief, behavior, and belonging. Journal for the Scientific Study of Religion, 56. Pace, E. (2017). Le religioni in rete: come comunicano e come studiarle. Sociologia Italiana-AIS Journal of Sociology, 1. Sheehan, T. (1981). Myth and violence: The fascism of Julius Evola and Alain de Benoist. Social Research, 45–73. Taguieff, P. A. (2004). Sulla Nuova Destra: Itinerario di un intellettuale atipico. Vallecchi.

ADDITIONAL READING Snook, J. (2015). American Heathens: The Politics of Identity in a Pagan Religious Movement. Temple University Press. doi:10.2307/j.ctvrdf42h Urban, H. (2015). New Age, Neopagan, and New Religious Movements: Alternative Spirituality in Contemporary America. University of California Press. doi:10.1525/9780520962125 Von Schnurbein, S. (2016). Norse revival: transformations of Germanic neopaganism. Brill. doi:10.1163/9789004309517 White, E. D. (2016). Wicca: History, Belief, and Community in Modern Pagan Witchcraft. Sussex Academic Press.

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ENDNOTES 1



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The study mainly refers to the spread of the trend of solitary practice in the states of North America. The communitie examined show themselves to be aware of the spread of the phenomenon and often recognize themselves as an active part of it. The phenomenon of the diffusion of blogs and social pages that have paganism as their central theme is also present in Italy, but the literature on it is relatively poor. (Pace 2017). Magic is a very important part of Wiccan and Pagan culture, and for this reason it was believed for many years that the members of these religious groups were or called themselves witches and sorcerers. The title is not rejected by the members of the groups I have analyzed but there is a widespread tendency to emphasize that it is not suitable for defining them as it is an understatement. See on the subject of magic in wicca and pagan circles: Lewis 1996; Berger 2005. De Benoist promotes the ideal of national identity by emphasizing the bond of the individual to the territory of belonging. The nationalistic discourse is flanked by that on the possibility of adopting paganism for the European peoples as the author sees Christianity as an institution that has led to the impoverishment of the perception of the sacred. According to de Benoist, paganism should return to be practiced in new ways and ideologies. See: de Benoist 1966, 1981, 1994; de Benoist & Molnar 1986; T. Sheehan 1981; Germinario 2002; Taguieff 2003.

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Chapter 36

Methodological Directions for the Study of Memes Giulia Giorgi University of Milan, Italy & University of Turin, Italy

ABSTRACT The chapter proposes an empirically oriented analysis of the memetic production on Instagram. Defined as multimodal cultural artifacts, combining visual and textual material to convey humoristic messages, internet memes proliferate across the web, spawning new popular formats and layouts. However, many scholars still rely on outdated conceptualisations or limited samples for their studies. To anchor investigation on memes to the actual production, the research answers the questions: (1) Which meme formats are currently circulating online? (2) How do popular meme formats convey their message? To this end, a dataset of static images collected on Instagram was examined with qualitative visual and discourse analysis. Findings point at the possibility to adopt a bottom-up approach to recognize and classify memes, exploiting shared features of content and form. Furthermore, this categorization offers insights on the most productive mechanisms of meme production: contextually, results show a tendency towards formats that trigger identification, leveraging on relatable life situations.

INTRODUCTION This chapter seeks to anchor meme research to the actual production, by offering an updated typology of some of the most popular memetic formats spreading online. In so doing, it also proposes a methodological toolkit to analyse Internet memes, addressing the challenges posed by their multimodal and heterogeneous realizations. In a society where “what we see is often more important than what we hear or read” (Rose 2016, p. 2), multimodal user-generated content has carved out an increasingly relevant role in online interactions. In this scenario, Internet memes have gained foot as an engaging tool for users to express themselves in an ironic format, often combining visual and textual material (Milner, 2016; Shifman, 2014). To date, research on memes has been concerned with their contribution to the expression of political ideas and of subcultural identity (Ross and Rivers, 2019; Denisova, 2019; Gal et al., 2016). However, such analyses are mostly based on cherry-picked samples, which hardly account for DOI: 10.4018/978-1-7998-8473-6.ch036

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the wide range of possible meme layouts and patterns circulating on the web. By limiting the investigation to well-known and recognized memetic frames (e.g. the so-called ‘Image Macros”), many scholars have circumvented the question of defining memes from an empirical point of view. In fact, despite the several and partly overlapping theoretical definitions (Knobel and Lankshear, 2007; Davison, 2012), there is little indication on how to empirically approach the study of memes. On a general level, existing research has barely started to explore the boundaries of this complex and multifaceted phenomenon. To address this issue, this chapter undertakes an empirical analysis which combines digital and qualitative methods, investigating which popular memes and recurring formats are circulating on Instagram within the Italian cultural context. With a user base mostly composed by people from 18 to 35 years old (Chen, 2020), Instagram has a strong influence on youth culture, identity, and perceptions of the world. Memes, which cover a considerable percentage of the visual formats of the platform (Hu et al., 2014), are considered not only a form of entertainment but also an identity building device (DeCook, 2018), as well a tool to comment on political and social issues (Fauzi et al., 2020). The initial dataset, which has been gathered with digital methods (Rogers, 2013) following the general hashtag #memeitaliani, features 47.443 static image memes. A sample of 1000 items has been selected by engagement and manually tagged according to relevant features, derived both from existing literature (Shifman, 2013) and the data itself. By adopting this data driven approach, it is possible to identify memes from non-memetic material on the basis of a shared characteristic, i.e. the degree of manipulation. Findings show that manual coding provides the researcher with a systemization of meme production into four standardised formats, showing recurrent patterns of sense-making and irony construction: Mono, Reaction, Panel, and Box. The last section further explores Reaction memes by using Discourse Analysis (Fairclough, 1995). It is argued that Reaction memes trigger the identification with the users, by both representing commonly shared life situations and employing textual linguistic cues, such as the ‘Script’ or of the ‘When’ pattern.

BACKGROUND Cultural and Internet Memes The term ‘meme’ was first coined in 1976, when Richard Dawkins used it to describe the cultural counterpart of biological genes: the meme (shorting for ‘mimeme’, from the ancient Greek mimema, lit. ‘imitated thing’) is a unit of cultural transmission or a unit of imitation and replication, which – just like the gene – replicates itself by exposure to humans (Dawkins, 2016). Following this definition memes can take many forms, among which ideas, symbols, melodies, catch-phrases, clothing fashion or architecture (Shifman, 2013). The concept has since been widely discussed (Aunger, 2002; Blackmore, 2000; Brodie, 1996; Kronfeldner, 2014), often raising more questions than answers. For instance, discourses around the actual nature of memes has generated much dissent, fiercely opposing scholars who define memes as substrate-neutral information patterns or ideas embedded in cultural artefacts (Blackmore, 2000; Brodie, 2009) to those who attempt to concretize memes as a ‘state of mind’ that stimulate the neurons (Aunger, 2002). However, as pointed out by Finkelstein (2008), none of these definitions “is sufficient to allow a meme to be clearly recognized, measured, or provide the basis for scientific research” (p. 11). Similarly, much effort has been devoted to understanding how memes are circulated. In this regard, Dawkins operates a distinction between ‘replicators’ and ‘vehicles’, the former being anything of which 628

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copies are made, whereas the latter is the entity that interacts with the environment. Such distinction has been taken up by Blackmore (2000), according to whom a meme is any piece of information that can be copied via imitation. Her argument relies on the idea that memes – just like genes - are selfish replicators, insofar as their only interest is to spread from brain to brain. Therefore, the success of a meme only depends on its ability to be replicated, retained, and imitated. She also maintains that this mechanism of replication is not perfect: memes are copied with variations, which in turn give rise to competition for survival and, in the long run, to memes and cultural evolution. In this view, humans are defined as meme machines, i.e. “repositories of vast numbers of memes, [which] have come from other people and will, if we speak and write and communicate, go on into yet more people.” (p. 236). As opposed to this view, Distin (2005) argues that memes do not rely on any survival machines: instead, they are representations stored in the brain which preserve their content in a way that can be copied from generation to generation. Therefore, she concludes that humans are not passive meme replicating machines, but rather there is a conscious self behind the fabrication and circulation of memes. The prominent role of human agency becomes all the more evident when considering Internet memes’ production practices, which are based on the creative decisions of individual actions and collective evaluations (Milner, 2016) and may be conceived as acts of ‘vernacular creativity’ (Burgess, 2006). With the emergence of the Internet and in particular of Web 2.0 (O’Reilly, 2005), the term ‘meme’ has come to be applied to certain viral contents that circulate online. The first definition for this particular variation was proposed by Davison (2012), who describes the meme as “a piece of culture, typically a joke, which gains influence through online transmission” (p. 122). In this sense, Internet memes are conceived as digital artefacts created by users combining visual and textual elements, which can come in a variety of forms: e.g. still images, GIFs, hashtags, and videos. Usually, Internet memes are not isolated units, but rather spread within particular socio-cultural contexts in groups, or ‘waves’, i.e. set of memes referring to the same topic or having similar formal features (Börzsei, 2013). It is argued that Internet memes provide a privileged point of view from which to observe the memetic phenomenon and its effects on our culture, in particular the process through which the most popular memes define the culture shared by a community (Heylighen, and Chielens, 2009). For one thing, they reduce much of the fuzziness which has until now surrounded most theories proposed by memetics scholars. As discrete digital objects circulated online, Internet memes are an ideal case study: in fact, they are relatively easily identifiable and traceable, allowing researchers to follow their spreading patterns throughout the web, by keeping track of the transformative processes they undergo (Zanettou et al., 2018). Internet memes also provide an accessible device for users to frame and comment on current events. Existing research points at a growing tendency towards the use of memes as a vehicle for political expression and participation, most commonly in the form of communicating a position through humorous mockery and criticism (for example Ross and Rivers, 2019; Huntington, 2013). Contextually, some scholars have highlighted the role of memes in disrupting the normative narrative (Hristova, 2014), whereas others maintain that memes enhance political engagement (Dahlgren, 2009). In this sense, Dahlgren has underlined the importance of pop cultural references: “popular culture offers a sense of easy access to symbolic communities, a world beyond oneself. This can at times be seen as preparatory for civic engagement, prefiguring involvement beyond one’s private domain, by offering cultural citizenship” (p. 141). This position is in line with Van Zoonen’s (2005) claim that pop culture should be acknowledged as a resource that can make citizenship more entertaining and therefore more inclusive. Nevertheless, participation in memes’ production and sharing practices strongly depends on meeting certain prerequisites, i.e. digital and cultural literacy, and transgression is hardly tolerated. Thus, in op629

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position to the belief of van Zoonen and Dahlgren, scholars have started questioning the participatory and collaborative potential of memes, while more attention has been paid to their gatekeeping function (Milner, 2016). From this perspective, research has shown that the production and circulation of memes demands specific digital and cultural competences. On a general level, media literacy – broadly conceived as the ability to access, understand, and create information and communication online (Livingstone, 2004) – is an essential prerequisite. Yet, users also need to engage with transformative media processes that involve the reinterpretation of the digital material in creative ways, pulling on existing texts to create new ones. Put differently, users should know how to Photoshop, how to play with language and images to create innovative artifacts (Knobel and Lankshear, 2005). Nevertheless, digital proficiency alone is not enough. The required literacy is as cultural as it is technical: subcultural literacy – i.e. the ability to engage with the social language accepted by subcultural insiders (Milner, 2016) – is just as essential as media literacy. In this sense, the creation of visual collages, or pastiches in Jamesonian sense (Jameson, 1991), comes at the same cost as participation in many other social groups: through the assimilation and reuse of communicative practices. In most communities, social bonds are fostered through a common ground of subcultural knowledge and each social group shows its own classification system providing a set of taste distinctions between good and bad culture. In the same way, what binds meme collectives together is the sharing of a certain cultural capital in the Bourdesian sense (Bourdieu, 1984). Specifically, the chances to access online communities depend on how users are able to prove their ‘meme literacy’ (Milner, 2016). As Nissenbaum and Shifman (2017) point out: “the deep connection between memes and the culture of some online communities means that they function as cues of membership, distinguishing in-group members from mere passers-by” (p. 485). Established social conventions therefore not only dictate the norms for transformative creation, but also function as gatekeepers, excluding the uninitiated. Finally, meme literacy is employed to create differential social positions within the community: therefore, those who manage to rightfully interpret the cultural conventions will have a higher status within the community and be respected by others, while those who fail will be downgraded and most likely mocked at (Massanari, 2013). Therefore, even if the processes by which memes are created, circulated, and transformed are open and participatory, the culture regulating their production and circulation is far from being democratic and inclusive (Milner, 2016). Meme literacy, which is collectively defined within the communities, may present significant variations from one group to another. Recalling Thornton’s theory of subcultures (1996), Angela Nagle claims that: “subcultural capital is earned through being ‘in the know’, using obscure slang and using the particularities of the subculture to differentiate yourself from mainstream culture and mass society” (Nagle 2017, p. 96). As it emerges from the not-exhaustive literature review above outlined, scholars seem to have reached a consensus on a definition of Internet meme that touches on at least three points: the multimodal nature, the imitation/remixing practices, and the viral circulation throughout the web. The analysis of this chapter sticks to the theoretical definition proposed by Shifman (2014), who refers to memes as “groups of digital items (such as images or videos) that share common characteristics, are created with awareness of each other and are circulated, imitated, and/or transformed via the Internet by many users” (p. 41).

Research on Memes: A Critical Assessment Despite their centrality in nowadays communication, Internet memes have long remained an understudied and understated phenomenon. Scholars from media and communication research fields have only recently approached the topic, retracing the steps of the development of their aesthetics and addressing memes 630

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as the latest step in the natural evolution of Internet language from textual to visual forms (Applegate and Cohen, 2017; Börzsei, 2013; McCulloch, 2019; Wiggins and Bowers, 2015). To date, most studies investigate memetic production as linked to the affordances and agenda of specific platforms (Burgess, 2008; Milner, 2013; Shifman, 2012) or revising different aspects of the meme theory (Shifman, 2013; Spitzberg, 2014). Contextually, researchers tend to focus either on the playful and disruptive humoristic intent subsumed by the motto ‘do it for the lulz’ (Coleman, 2012; Massanari, 2013), on their political declination (Applegate and Cohen, 2017; Wells, 2018; Heiskanen, 2017; Ross and Rivers, 2019), or on memes as a shared code of online subcultural groups (Gal et al, 2016; Milner, 2016; Phillips, 2015; Nissenbaum and Shifman, 2017; Zittrain, 2014; Nagle, 2017). Internet memes play a relevant role in the Italian context as well, as testified by the emerging literature on the topic (Marino, 2014; Denicolai, 2019; Di Igor Pizzirusso et al., 2019; Bischetti et al., 2021). In particular, the relevance of memes as a means for top-down and bottom-up political communication has been addressed by various authors (Bracciale, 2020; Mazzoleni and Bracciale, 2019; Lolli, 2020). Specifically, Mazzoleni and Bracciale (2019) have identified a process of political ‘memification’ within the contemporary hybrid communicative ecosystem, in which users reappropriate the topics from the public agenda and remix them with pop cultural elements (p. 89). In this sense, the authors suggest that memes are to be considered as an alternative form of participation to the political debate. Over the past few years, some politicians have started recognizing the power of memes and have begun citing them in their discourses (Marino, 2019) as well as implementing them in their campaigns: the now famous Pepe the Frog meme retweeted by Donald Trump, which became the symbol of the 2016 American elections, has paved the way to the use of memes for other politicians. In Italy, the best example is provided by the leader of Lega Matteo Salvini, who has since 2019 been posting memetic videos on his TikTok account (Berra, 2019) and has recently shared a meme on his Twitter page to target the former Prime Minister Giuseppe Conte (Salvini, 2020). The meme, featuring a still taken from the music video of the hit Hotline Bling, authored by the R&B singer Drake, recalls a specific and prolific meme wave, labelled as ‘Drakeposting’ (entry Drakeposting in Know Your Meme). It has been argued that memes “use reference frames from popular culture to come up with multi-layered hybrid blends accessible to large audiences” (Laineste and Voolaid 2017, p. 44). In engaging with other cultural texts, memes create a shared cultural memory and foster affiliation among users with the same cultural imaginary (ibid.). Nevertheless, scholars have just begun to shed lights on the mechanisms that transform memes into multi-referential cultural texts. Within the Italian research area, this aspect has been addressed by the analysis Marino (2019), who recognizes the vital role of pop-cultural contaminations in fuelling users manipulation within meme ‘waves’ (Börzsei, 2013), like the one generated in occasion of the 2015 Gay Pride using the rainbow filter made available by Facebook1. In discussing this example, the author notices how users have been employing this filter to create avatars featuring popular national celebrities, movie, and TV series characters as well as frequently memeified figures which he refers to as ‘social media heroes’ (Marino 2019, p. 114), like TV presenter Giancarlo Magalli or the two marines Latorre e Girone (i.e. ‘I marò’). To the best of the author’s knowledge, there are no other academic works attempting to give an empirical account of the memetic references in relation to the Italian cultural context. Investigating the intertextual dimension of memes represents a hard task for scholars, due to its extremely heterogeneous and rapidly evolving nature: not only would any study addressing this aspect hardly be exhaustive, but it would also require continuous updates to connect with the ever-evolving memetic production. On a general level, the question of the limited timeliness of research on memes means that these studies might become obsolete before they are even published (McCulloch 2019). This is also linked to 631

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another problematic issue, that is the mismatch between theoretical discussion and empirical investigation: while meme theory is continuously advanced by the ongoing debate on participatory culture and gatekeeping function and - more recently - on platformization through ‘meme factories’ (Abidin, 2020), the empirical study on memes presents little innovation. In particular, works exploring case studies often rely on cherry-picked examples (Wiggins, 2014; Bozkuş, 2016; Ross and Rivers, 2017; Mazzoleni and Bracciale, 2019), while others still largely rely on ‘Macro’ formats (Milner, 2016; Dynel, 2016; Yus, 2018). Despite not questioning the validity of these analyses, it is argued that this approach fails to acknowledge the variety of formats that can be found online. In particular, studies analysing Macros crucially overlook the rising popularity of newly emerged layouts, that have quickly taken over old patterns and memetic characters. Undoubtedly, the choice to consider only recognized memes offers the immediate benefit to circumvent the hard task of specifically defining the memetic canon (cfr. Shifman, 2013). However, relying only on online databases like Know Your Meme for validation raises issues concerning the novelty and actual relevance of the considered memes, since memetic templates are usually documented in those archives once they have reached a certain degree of standardization and popularity. In addition, most of these websites are bound to a limited (typically US-centred) perspective on the memetic phenomena, which overlook national realities and trends. As a consequence, this approach deems to oblivion potential meme waves and widely used formal patterns and characters, which have yet to be included in such repositories. In some cases, this results in a perceivable discrepancy between the types of memes sampled and those actually spreading online, therefore risking deriving results which are partial or not up-to-date. A noteworthy exception is represented by the work of Shifman (2013; 2014), who adopts a data-driven approach to achieve a satisfactory systemization of memes. Focussing on the links between memes within the same dataset, Shifman (2013) suggests exploring the degrees of variability among memes through an analysis of content (the themes, ideas, and ideologies embedded in the text), form (the composition of the message), and stance (i.e. the “communicative positioning of the addresser,” p. 369). In so doing, the author provides a general framework for the exploration of memes, which - given its broadness - can be manipulated and applied to other case studies (Literat, 2019; Gal et al., 2016). In a chapter of her 2014 book Memes in Digital Culture, Shifman claims that users tend to follow the “same beaten tracks of meme creation” (Shifman 2014, p. 99), thus giving rise to genres which share not only structures and stylistic features, but also themes, topics, and intended audiences. As an introduction for the lay reader, the author then proceeds to describe nine among the most popular genres emerged in the past decade. However, a quick survey on the web reveals that many of those genres have fallen into disuse, while new layouts and practices have taken over. The fast-pace changes brought by the spread of memes across mainstream media therefore calls for a new exploration of the most popular formats, which is able to link the formal properties of the memes to standardization in the sense-making construction and in the expression of the ironic message. In particular, since the conversion of memes from a niche into a mainstream phenomenon, two questions remain still largely unaddressed: RQ1: Which meme formats are currently circulating online? RQ2: How do popular meme formats convey their message?

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METHODOLOGY In order to investigate the questions above stated, the project is designed as a qualitative inquiry which articulates over three sections of empirical work employing a) digital methods, b) visual analysis, and c) and Discourse Analysis. Data Collection. Following the path traced by previous studies (Massanari, 2013; Tuters and Hagen, 2019), websites such as Reddit and 4chan are considered as the largest meme pools. Nevertheless, mainstream media outlets like Instagram demand equal if not more consideration, given their recent increasing uptick in usage. Ten years after its launch, Instagram reports having 1 billion active users worldwide (Chen, 2020). During this decade, Instagram has become the preferred platform to share everyday life snippets and selfies (Hu et al., 2014; Senft and Baym, 2015) but also to react in real-time to media events (Al Nashmi, 2018). Due to its popularity and the prominently visual nature of its content, Instagram was the platform of choice to retrieve the data for this analysis. The data collection has followed the procedures suggested by Rogers (2013) and Caliandro and Gandini (2016) within the digital methods paradigm, which takes as inputs the native digital objects available in the medium (e.g. links, tags, threads, etc.) to investigate online cultures and social environments. In the aftermath of the Cambridge Analytica scandal, scraping is a controversial topic in academic research. Although not being illegal per se, this practice raises concerns around ethics and privacy matters and it is fiercely adversed by social media companies, in the attempt to prevent researchers from accessing data that they do not intend to share (Bainotti et al., 2020). Nevertheless, Venturini and Rogers (2019) argue in favour of this practice and call for a conscientiously performed scraping for social research. Crucially, they maintain that scraping “forces researchers to observe online dynamics through the same interfaces as the actors they study” (p.536–537), providing a privileged point of view from which to observe how content is generated and circulated across the web. In this case, the data analysed have been collected from Instagram, using a scraper directed at relevant digital objects. Following the literature on using hashtags as an entry point for social research (Omena et al., 2020), the goal was to assemble a wide and heterogeneous dataset of Italian memes, possibly displaying a variety of meme formats and topics. A preliminary exploratory analysis identified #memeitaliani as the most suitable hashtag, as it is sufficiently broad to capture memes related to a variety of topics. Unlike other options like #meme or #memes, #memeitaliani is language specific, therefore reducing the risk of collecting multi-language content. Other similar variants like #memeita and #memeitalia were also valid alternatives, yet often appeared in combination with the one selected. In order to maximize the heterogeneity of the corpus, it was necessary to avoid timeframes in which the memetic production was altered by high impact mediatized events. An exploration of the content associated with the selected hashtag revealed that, starting from the end of February, the memetic scene is largely influenced – if not monopolized – by the outbreak of the Coronavirus emergency. Hence, the decision to restrict the data collection to the months of January and February rests on the assumption that the large number of memes concerning the Covid-19 would have considerably limited the variety of the topics gathered. The initial dataset counts 47.443 static images posts, published from the 1st of January and the 29th of February 2020. The metadata associated with the posts include: the date of publication, the caption of the post, the user ID, the number of likes and of comments. The visual analysis has been performed on a subsample of 1.000 of items, sorted by engagement, which is an index created by summing the number of likes and of comments: in particular, the posts considered feature an engagement index equal or above 5.000. 633

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Visual and Discourse Analysis. In order to analyse the dataset, this article adopts a data-driven approach and proposes a codebook broadly inspired by Shifman’s indications (2013), which account for crucial aspects of memes, including formal properties (layout and manipulation), intertextuality, and irony. Items have thus been manually coded on the basis of eight ad-hoc created categories, which are meant to return an as comprehensive as possible spectrum of meme grammar, as follows: 1. Topic: an open category stating the general subject of the meme, e.g. school, love, family, current events, sports. Memes containing puns and jokes with no clear topic or reference were labelled as ‘Jokes’. The label ‘Life’ identifies memes on shareable everyday life situations, reflections, or sudden realizations. 2. General layout refers to the appearance of memes, which can be labelled as: a. ‘Single’, featuring a single image with or without text. b. ‘Multiple’, including memes consisting of more than one panel (e.g. comics, crescendos, and split reactions). c. ‘Square’, which typically displays two distinct parts: a textual part against white or black background in the upper half of the meme, and one or more pictures (with or without text) below. 3. Text: a binary category indicating the presence of written parts. 4. Text patterns: despite the considerable variety of realizations, eight most frequently used patterns have been pinpointed: a. The ‘Single line’: when the item displays a single line of text, usually placed above or in the lower part of the image. It has three main variants, which can also appear in combination: the ‘When’ pattern. i.e. as the name suggests, it presents a text line which starts with the word ‘when’ and it is normally used to describe a situation; the ‘Script’ is a text pattern mimicking a fictitious dialogic exchange; and the ‘Quote’, which cites lines from the pop-cultural reference of the meme (e.g. movies, video games, cartoons). The ‘Quote’ refers indistinctly to changed or unchanged citations. b. The ‘Top/bottom’ line is often associated with ‘Image Macros’, featuring a fixed top line of text in the upper part of the meme which sets up the joke and the changeable bottom line, which creates the humoristic effect. c. The ‘Boxes’ feature a black text line centred against a white background. This kind of text is found in conjunction with a ‘Multiple’ layout, i.e. consisting of more than one panel. d. ‘Comics’ contain text spoken by some character, either in a balloon or between inverted commas. e. ‘Labels’ are short captions describing the characters, or the scene displayed in the picture. Unlike the ‘Comics’ category, ‘Labels’ do not refer to spoken texts. They are usually found over or below the portion of the image (e.g. the character) that they describe, and often concur to the realization of the humoristic effect. f. The category ‘Combo’ includes items with more than one of the patterns above stated. g. Finally, ‘Only’ features only textual items, with no images, and ‘None’ includes only visual items, without text. 5. Manipulation refers to the degree of modification and is codified into three categories: a. ‘Minor’: memes with low impact changes (i.e. the addition or modification of the text).

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b. ‘Major’: Heavily manipulated texts and digitally altered images: e.g. actors or items edited into the pictures. c. ‘Both’: combinations of low impact changes and image editing as detailed above. d. ‘None’: Non-manipulated content, like drawings, stills from animated content, illustrations, and so on. Following the adopted definition of meme, items marked with this label are not considered memes. 6. Visual references contain a list of the identifiable figures portrayed in the item, including real people (politicians, athletes, celebrities, influencers) or fictional characters from movies, books, comics, videogames and so on2. 7. Textual references: when the name of the above listed typologies of references are mentioned in the written parts of the item. Note that not only the characters, but also the names of the broader cultural reference (e.g. the movie or book title, video games, sport teams, political parties) have been counted3. 8. Irony: in this article, irony is intended as a synonym of humour and not of satire. According to classical rhetoric and linguistic studies (Attardo, 2010; Wilson, 2006; Kotthoff, 2003), irony derives from a perceived incongruity between what is multimodally expressed and what is meant. This category identifies memes with a humoristic undertone, and distinguishes between two types of irony: a. ‘Semantic’, when its decoding leverages on meaning multiplicity, ultimately resulting in polysemy (cfr. Boxman-Shabtai & Shifman, 2014). This label includes memes, in which irony is typically expressed in the form of puns or riddles. Situational irony (Lucariello, 1994), when the ironic effect arises from an unexpected resolution, was also included in this category. b. ‘Echoic’, when material (textual and visual) from pop-culture is quoted in memes, constituting the ‘punchline’ of the joke. This label draws inspiration from the echoic use of irony described by Sperber and Wilson (cfr. Wilson, 2006). In its original conception, it is assumed that the speaker echoes a thought or a sentence, towards which a certain attitude involving ironic distance is expressed. Diverging from this conception, it is argued that in memes the echoed parts are used to reinforce the general ironic message. As noted by Sperber (1984), in order to be successfully ironic the source of the utterance echoed must be recognizable. On the basis of the results obtained with the coding procedure, memes have been identified and extracted from the initial dataset. Drawing upon Shifman’s (2014) definition, which stresses the relevance of transformative practices, only items displaying a perceivable degree of manipulation are considered as memes. As outlined above, manipulation encompasses both minor and/or major changes, i.e. from the insertion of text to more complex image editing (e.g. filters, collages). Following this approach, items like chat screenshots, screenshot of posts from other social media or other websites (e.g. Insegreto.it), comics stripes and non-manipulated illustrations or drawings do not fall within the category of meme and were therefore not considered for the subsequent analysis. The last part of the analysis focuses on how popular memes construct their meaning by means of the Discourse Analysis approach. To this end, it focuses on the top 5 ‘Reaction’ memes ranked by engagement, since ‘Reaction’ resulted to be the most popular template employed within the dataset. Originally devised by Fairclough (1995) for the investigation of text, Discourse Analysis addresses the rhetorical organization and social production of visual, written and spoken materials (Rose, 2016). Partly drawing from Fairclough’s original conception and Rose’s application to visual items, this approach is adopted 635

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with the aim to link the production patterns observed to the underlying messages conveyed by memes. Starting from the quantitative data obtained through the coding, connections have been drawn between formal properties and sense-making strategies, identifying recurring rhetorical and linguistic cues which can be exploited to trigger the users’ identification.

ANALYSIS In the following sections, the results obtained by means of manual coding will be thoroughly discussed, focussing on three relevant aspects: layout patterns, the construction of irony and external references (intertextuality). The aim is to underline the heuristic value of the methodological approach applied. In this regard, the first strong point of the adopted codebook concerns the possibility to identify the actual number of memes present in the dataset. Although all the material retrieved from Instagram was labelled with the hashtag #memeitaliani, images with no sign of manipulation have not been regarded as memes (cfr. Shifman, 2014). Using this methodology, the dataset contains 838 memes and 162 non memes, among which it is possible to find screenshots from other platforms and websites, drawings, and comics stripes. A closer look at the modalities of manipulation reveals that 659 memes (78.64%) show minor interventions, while only 5 of them (0.6%) displays image editing. The remaining 174 memes (20.76%) feature both minor and major changes. Figure 1 shows an example of a meme presenting both minor and major types of manipulation, i.e. the insertion of a text parts and the image editing through filters. This information gives a first insight into meme creation mechanisms, suggesting that most memes entail low effort modifications, like adding a line of text. This tendency also reflects the modalities offered by some of the most widespread online services and apps to create memes (e.g. MemeGenerator), which provides the possibility to insert original text into ready-made popular formats. Other considerations derive from the general topics displayed by the memes. Table 1, which details the most frequent topics, points at a great level of heterogeneity. In particular, three topics appear to be the most used: ‘School’ (148 occurrences), ‘Life’ (131 occurrences), and ‘Gag’ (124 occurrences). As anticipated in the methodological section, the category ‘Life” describes shareable life moments, usually with an ironic undertone. Unlike memes from other categories (e.g. ‘Love and friendship’, or ‘School’), which also often depict relatable situations, the topic of ‘Life’ memes is not clear: rather, they show moments of everyday life, habits, or sudden self-awareness. Figure 2 represents a typical example of such a category: the main character of the meme promises to go to bed early but fails, while the accompanying ‘Reaction’ image depicts a representation of a worn out face, typical of those staying up late (more on the construction of memetic irony in the next section). Finally, the category ‘Gag’ covers standing jokes and puns. Notably this is a popular topic also for non-memetic items, typically featuring funny comics and screenshots. As for the rest, ‘News’ (60), ‘Sport’ (73), ‘Love and friendship’ (95) and ‘Parents, childhood, and generations’ (54) are all relevant topics with more than 50 occurrences within the dataset. By analysing the data presented in Table 1, it is inferred that most topics represented in the memes describe typical teenagers’ life situations. From this perspective, it can be speculated that memes are mostly created and shared by younger segments of users to ironically talk about themselves, their everyday life at school or with friends as well as the relationship with their parents and families.

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Figure 1. (lit. “When your smartwatch tells you to get up from the couch due to physical inactivity”)

Table 1. Topics of the meme (frequency >50) Topic

Freq.

School

148

Life

131

Gag

124

Love and friendship

95

Sport

73

News

60

Parents, childhood and generations

54

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Figure 2. (lit. “Me: I’m going to bed early tonight / Also me at 1:47am:”)

Layout Patterns and the Four Formats Due to its many layers, the visual aspect of memes represents a complex feature to deconstruct and analyse. The adopted codebook comprehensively accounts for the appearance of the memes, by breaking down this aspect into three relevant categories: general layout, text, and text patterns. On the basis of their most frequent combinations, four standardised formats have been identified: ‘Mono’, ‘Reaction’, ‘Panels’, and ‘Boxes’ (reported in Table 2). As a preliminary observation, it is noted that these layouts show different occurrence percentages within our dataset: ‘Reaction’ memes is by far the most widely used format (504 occurrences, 60.14%), followed by ‘Mono’ (151 occurrences) and ‘Panels’ (130 occurrences), while ‘Box’ (53 occurrences) appears to be the least common layout choice. Another interesting remark regards the degree of standardisation displayed: despite presenting some variants, each of the layouts has one or two prototypical realizations. This means that they tend to adopt a similar combination of textual and visual elements, which may arguably lead to a crystallization of the format. Focusing

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on homologation rather than heterogeneity, the following descriptions are thus meant to describe the prototypical versions of the formats analysed and their most frequent variations. Table 2. Number of text patterns occurrences sorted per layout Layout/Text

Box

Mono

Panel

Reaction

Tot.

Balloon

0

6

36

2

44

Boxes

47

0

0

0

47

Script

0

1

1

168

170

Label

0

35

36

2

73

Quote

0

0

1

0

1

Single line

0

27

17

70

115

Top/bottom

0

58

15

3

76

When

1

2

1

106

110

Combo

5

22

17

153

197

None

0

1

4

0

5

Tot.

53

152

128

505

838

Reaction. As shown in Table 2, the ‘Reaction’ layout is the most frequently used. This type of meme usually depicts a single situation described by the textual part, to which the image constitutes the reaction. Given their format and their use, ‘Reaction’ memes may be considered an evolution of the ‘Reaction Images’, e.g. the facepalm, which became a part of image-board websites like 4chan (entry Reaction Images in Know Your Meme). ‘Reaction’ memes typically feature a square white frame or a single white stripe in the upper part of the meme, in which the situation is described either by the ‘When’ or the ‘Script’ textual pattern. The image is placed in the bottom part, below the introductory lines: it can be accompanied by some text (normally a quote or labels), but textless images are common as well. Figure 3 better clarifies the dynamics of such memes. In this example, the situation - introduced by a ‘When’ text type - describes in third person the case of a male friend of the main character getting a girlfriend and losing his own will. The picture reacts to this situation by means of a scene of the TV series Rick & Morty, in which Morty is subjugated by an enchanted crystal and has lost his mind. Another common variation is presented by Figure 4, in which the narrative part of the situation is replaced by the ‘Script’, a fictitious dialogic exchange between the characters involved in the meme: in the example examined, the main character declines an invitation to go out, claiming to have guests. In the reaction image, the guest is revealed to be a kitten. Two aspects slightly differentiate this second example from the previous one: (1) the two types of textual patterns used to present the situation (external narration or dialogue), and (2) the presence or absence of text in the image. These two memes represent the most frequent realizations of the layout ‘Reaction’, which occasionally displays some minor variations: e.g. the presence of more than one image as a reaction, or the presence of labels in the reaction image. Mono. This is a long-time established layout (the website MemeGenerator refers to it as the ‘Classic Layout’), typically involving a single image and a fixed top line of text and a changeable bottom line of text, which contains the punchline. The major deviation from the prototypical example is represented by

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Figure 3. (lit. “When a friend of yours finds a girlfriend and stop having a will of his own”)

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Figure 4. (lit. “Me: I can’t go out, I have guests / The guests:”)

the replacement of the top and bottom text pattern with labels, which produces the ironic effect (Figure 5). Other slight differences concern the presence of black stripes framing the image the upper and lower side of the image or the colour, the font, and the size of the text. Panels. This category includes multi-panels memes in the form of drawings/comics (Figure 6) or real life photos (Figure 7). Items from this category present the situation and the typically ironic resolution through two or more images. There is virtually no limit to the number of panels displayed, which usually ranges between four and six.

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Figure 5. (lit. “Airport security” / “Bottle of water”)

A side note: some non-meme items from the dataset are multi-panel comics and therefore may apparently be confused by ‘Panels’. However, ‘Panels’ memes differ from them, insofar as – as we have seen – they show some degree of manipulation: Figure 6, for instance, depicts a scene from a fantasylike novel. Yet, the balloon in the fourth panel has been modified (as further hinted by the different bold font): a walking route (in Italian route di cammino) is a way of summer camping popular in the Scout movement, which is characterised by long distance walks. Similarly to the previous categories, ‘Panels’ may occasionally display some deviations from the prototypical format: for example, instead or in addition to balloons, the written part may take the form of labels or of top/bottom lines of text. Boxes. The last category emerging from the data is yet another multi-panel type of meme, which given its unique features deserves to be treated as a separate group from ‘Panels’. Unlike ‘Panels’, ‘Boxes’ are vertically split in two halves: one of them - either the left or the right side - contains only text parts, which are typically written in black and centred against a white background. The meme is further cut by horizontal lines, so that each textual box corresponds to another with image. Similarly to the ‘Reaction’ layout – of which ‘Boxes’ may to some extent represent a variation – the written parts of ‘Boxes’ describe a situation, while the corresponding image reacts to it. Unlike ‘Reaction’, however, here the succession of written and image boxes creates a sort of ironic crescendo, where the last image usually reaches the apotheosis of the intensity (or, in some cases, an ironic inversion). In Figure 8, the left side of the meme contains typical remarks of people who despise video games as a waste of time, whereas the latter written box reverses the situation hinting that this person wastes time as well, watching the TV show Temptation Island. Noteworthy, the last pair of textual and image boxes create the ironic effect and give sense to the entire meme.

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Figure 6. (lit. “This sword is used only to kill monsters” / “What about the guy you killed 10 seconds ago?” / “He was the worst monster of all!” / 10 seconds earlier “I always suggest walking routes”)

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Figure 7. ­

The Construction of Irony Irony is not an essential element for a meme to be considered as such since manipulation does not necessarily imply the creation of an ironic message. However, as Milner claims, “memes are almost entirely jokes” (Milner, 2016:48). This statement seems to find support in the data analysed, as 797 out of 838 memes present a humoristic message. On a general level, it can be noted that memes express humour by stating an initial situation, to which an unexpected resolution is given. Starting from a purposely broad conception of humour as ‘incongruity’ (see the section Methodology), the adopted methodological approach further explores the ironic aspect in memes, by identifying two productive mechanisms through which humour is created: semantic and echoic. Semantic Irony. This kind of irony emerges from the incongruity between the situation presented and its final resolution. Typically relying on the polysemic interpretations of the textual and visual elements, this category includes puns and the kind of ‘situational irony’ (Lucariello, 2007) involving an unexpected outcome. Unsurprisingly, in most cases the text plays a key role in constructing the ironic effect, while the visual aspect is either irrelevant for the interpretation or of secondary importance. Figure 9 and Figure 10, for instance, are examples of memes visually depicting verbal puns: the irony is entirely conveyed by the text, whereas the image accompanying it does not impactfully contribute to the humoristic effect. In such cases, the humour resides in the polysemic interpretation of a word (i.e. stufa in Italian means both ‘stove’ and ‘fed up’). In Figure 10, instead, the incongruity - and thus the humour - is conveyed by the clash between the image of an almost entirely clear sky and the text (lit. “When you would like to go jogging but it’s getting cloudy outside”). Looking at the layout, it is worth noting that memes showing semantic irony mostly fall within the ‘Mono’ category and feature the textual pattern ‘Top/bottom’. Some memes, however, show a ‘Reaction’ layout: Figure 11, for instance, bases

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 Methodological Directions for the Study of Memes

Figure 8 (lit. “You are too old for video games / What’s the fun in them? / They are a waste of money / Uh, I’m going to watch Temptation Island!”)

the humoristic effect on the incongruity between the introductory line of text (“The best photos of the Milan Cathedral”) and the foggy images of the document. The meme hints at the fact that the weather in Milan - and in Lombardy in general - is often cloudy and foggy: contextual literacy plays here a fundamental role, since only those who get this implicit reference are able to grasp the joke in its entirety. Echoic Irony. Data from the manual coding shows that echoic irony (584) appears more frequently than the semantic irony (213 memes). Furthermore, this mechanism is frequently associated with the layout ‘Reaction’. The echoic effect involves the quotation of someone else’s thought, assertion, dialogic exchange, gesture and/or facial expression, which constitute the punchline of the meme. It is normally

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 Methodological Directions for the Study of Memes

Figure 9. (lit. “A stove switches off after a while / because it is fed up”)

used to comment and react to the situation depicted by the meme in a funny way. Therefore, the quotation is usually placed in the bottom part of the meme, as it should be the last thing to be seen and read. Unlike semantic irony, in echoic irony the incongruity is given by the quoted part, which is extracted from its original context and placed in the meme. The humoristic effect arises from the fact that, despite the incongruity, the quotation fits in the memetic context giving or adding sense to it. Figure 12 clarifies the functioning of the echoic irony mechanism: the introduction describes a situation in which the viewer of the meme, identified by the pronoun ‘you’ (more on the semantic construction of memes in the next section), arrives late at school and finds his seat has been taken, while the resolution features an image taken from the Shrek video game depicting the main character (Shrek) with a puzzled/annoyed expres-

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 Methodological Directions for the Study of Memes

Figure 10. (lit. “When you would like to go jogging/ but it’s getting cloudy outside”)

sion on his face. The relevant point to note here is that the image is decontextualized and placed in the meme to function as a funny representation of the reaction that the viewer should have in this situation. Another crucial feature of this kind of irony is that the echoed (i.e. quoted) portion does not entail an unexpected outcome or inversion of the initial situation. Rather, the image acts as a reaction deriving from the event presented. Although most echoic memes present this pattern, there are some exceptions, in which the uncertainty results from either the difficulty to recognize the source of quotation or the coexistence of echoic and semantic irony. This observation introduces the last relevant aspect to consider when analysing memes: the external references, or intertextuality.

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 Methodological Directions for the Study of Memes

Figure 11. (lit. “The best photos of the Milan Cathedral from the web:”)

References and Intertextuality Considered a central feature of memes (Miltner, 2014; Laineste and Voolaid, 2017), intertextuality refers to the ability to creatively remix elements from other sources to create new cultural artefacts. In some cases the different parts are simply juxtaposed, while in some others they are more seamlessly blended. Either way, the full enjoyment of the ironic message depends on the correct deciphering of the references.

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 Methodological Directions for the Study of Memes

Figure 12. (lit. “When you are late for school and there is a mate sitting in your seat”)

Looking at the results of the manual coding, the pop-cultural imaginary seems to be the privileged pool for the intertextual elements: characters from TV series, movies, cartoons, and video games are habitually used to create memes, as well as political actors and sportsmen (typically football players). Crossreferences to other memes are also quite relevant: both standardised ‘Reaction Images’ (e.g. ‘Blinking White Guy’) and well-known memetic personas (e.g. ‘Stonks’) consistently appear throughout the dataset, with different degrees of manipulation. Although both visual and textual references have been collected,

649

 Methodological Directions for the Study of Memes

it is worth noting that visual references are more significant, insofar as they are more numerous and show a higher level of variety. On the other hand, textual references mostly function as a reinforcement of the visual references (e.g. a character which is depicted in the meme is also mentioned in the written part). Table 3 shows the most frequently employed visual references, i.e. with 5 or more occurrences: at the top of the list it is possible to find characters from cartoons and video games (Spongebob, The Mandalorian, Simpson, Monsters & Co), meme characters (‘Stonks’ and ‘Loading Cat’), next to Italian celebrities (presenter Alberto Angela, the actor Checco Zalone and the comic trio Aldo, Giovanni, and Giacomo) and the Italian young web star Matty il Biondo. Table 3. Visual references in the memes (freq ≥ 5) Reference

Freq.

Spongebob

85

Stonks

56

Baby Yoda/Grogu

18

Simpson

12

Monsters & Co

11

Alberto Angela

10

Mr. Incredible

9

Lego

8

Matty Il Biondo

7

Loading Cat

7

Aldo, Giovanni e Giacomo

7

Joker

6

Zlatan

5

Tom & Jerry

5

Star Wars

5

Checco Zalone

5

When examining the issue of intertextuality, it is important to consider how these characters situate themselves in the broader socio-cultural context of reference. From this perspective, it can be noted that some references within the dataset display a quite long and established tradition as memetic personas, both in the Italian and in the international cultural imaginary: such is the case for Spongebob (Elkin, 2021) and the Simpsons. Other characters, instead, are definitely more bound to the national context, like Checco Zalone, the trio Aldo, Giovanni e Giacomo or Alberto Angela (Fantoni, 2018), the latter being one of the ‘social media heroes’ described by Marino (2019). Finally, there is a group of references, which, due to their novel popularity, started to be included in memes during the time period analysed: the best example is probably represented by Baby Yoda (aka Grogu), the character of the successful star-wars animated spin-off The Mandalorian launched at the end of 2019, which is one of the most frequently used reference within the dataset. Around the same period (December 2019), the young web star ‘Matty il Biondo’ published his first video on YouTube and quickly became prolific material for memes: this

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 Methodological Directions for the Study of Memes

trend is easily verifiable with a browse on Google Trends, which reveals that the entry ‘Matty il Biondo’ peaked in popularity during the second week of January 2020. The same applies for the term ‘Joker’, referring to the homonymous movie starring Joaquin Phoenix, which was released for the second time in theatres in mid-February 2020. This seems to reinforce the idea that many memes waves (Börzsei, 2013) derive elements from trending events and topics, e.g. a movie release or the latest news. The longevity of such references is another interesting and yet so far neglected aspect of intertextuality: while some characters are still prolific after years since their first appearance, it is assumed that others are more or less quickly abandoned as their popularity fades. While addressing this issue goes beyond the scope of the analysis proposed in this chapter, which returns a still picture of the memetic production pinpointed to a limited time frame, it nevertheless provides an opportunity for future research on the topic. Further considerations concern the degrees of diversity that the same reference can display. The term ‘variability’ refers here to how many different images of the same (visual) reference were found within the dataset. It is argued that this variability can be evaluated on the basis of a scale going from a minimum to a maximum level of variability. In this sense, at the lower end of the scale there are cross-references to other memes, which tend to have a reduced or no degree of variability: for instance, references to the ‘Loading Cat’ meme or to the ‘Blinking White Guy’ meme display the exact same image for each and every occurrence. Most of the references, however, are placed in-between, insofar as there are one or two preferred images and other less frequently used variants: this is the case, for example, of the Spongebob or the Baby Yoda (or Grogu) references as it is shown in Figure 13. Figure 13. Some of the most popular memes with Baby Yoda references

The relevance of these observations becomes evident when considering the memetic phenomenon from a diachronic perspective. It has already been argued that the production of memes tend towards the standardization of templates, i.e. recurrent combination of layout and text patterns as shown in Table 2. A similar claim is now advanced for visual references: while a reference may present different images, only a small number of them actually gain popularity and spread virally on various platforms. This argument may in turn provide some insight on how some Image Macros may be formed and enter the canon, i.e. through a process of progressive standardisation.

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 Methodological Directions for the Study of Memes

Figure 14. (lit. “When they tell you that stress balls are supposed to be squeezed and not thrown at people stressing you”)

Discourse Analysis: ‘Reaction’ Memes and Identification This last section shows how the visual analysis of the memetic production can be integrated with Discourse Analysis, in order to further explore memes’ sense-making mechanisms. To do so, the top 5 most popular ‘Reaction’ memes are examined. Before delving into the Discourse Analysis, it is noted that 4 out of 5 memes depict shareable life situations (Figure 14, Figure 15, Figure 16, Figure 18), while one is a gag (Figure 17). These four memes present an echoic type of irony, while Figure 17 features a situational type of irony (thus semantic), where

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 Methodological Directions for the Study of Memes

Figure 15. (lit. “When your child shatters his pelvis riding a bike but you put on a band-aid, give it a kiss and it’s alright”)

the humoristic effect arises from the incongruity between the image of Queen Elizabeth II and the text (lit. “I remember Jesus… He was a lively child”), hyperbolically hinting at her old age. The remainder of the section will focus on the analysis of the memes depicting shareable life situations (Figure 14, Figure 15, Figure 16, Figure 18). On a general level, all of them – like most ‘Reaction’ memes – tell a joke which is roughly divided into two parts, one which presents the situation, while the other one contains the humoristic resolution. The layout of the ‘Reaction’ meme reflects the structure of the narration, as the introduction normally coincides with the written part and the resolution with the image(s). The situation can be presented either through an external third-person narration introduced by

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 Methodological Directions for the Study of Memes

Figure 16. (lit “You: let’s go have a look at that section [of a store] / the Chinese clerk:”)

the word ‘when’ (Figure 14, Figure 15) or through the ‘Script’, which involves characters taking turns in a dialogue (Figure 16, Figure 18). This section serves to introduce both the scene and the characters populating it: in most cases, the main character is the viewer, who can be either alone in the scene (e.g. Figure 14) or accompanied by other characters, like relatives (Figure 15), friends or an imaginary persona (e.g. Figure 18). The introductory part also provides additional contextual information to understand the setting in which the situation unfolds: for example, the words ‘clerk’ (it. commesso) and ‘section’ (it. reparto) suggest that the situation takes place in a shop. Due to limited space, the description of the scene is generally quite concise and follows a more or less standardised outline. For instance, the ‘When’ pattern mostly displays a structure, whose simplest

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 Methodological Directions for the Study of Memes

version is ‘When X does Y’, where ‘when’ is the only fixed word and X, who is usually the main character of the story, performs some kind of action Y or is in a situation Y, which triggers the resolutive and often humoristic reaction. Looking at the memes considered for the Discourse Analysis, this structure is found – even if with some variations – both in Figure 14 and Figure 15: the first meme describes a scene in which X is told not to throw stress balls at others (Y), while in the second meme X attempts to cure his son’s broken pelvis with a band-aid and a kiss (Y). The structure of the ‘Script’ pattern features two or four lines, in which a fictitious dialogue between X and another character (or more than one) takes place. In this case, the details about the situation are embedded in the dialogue: in Figure 18, one of the characters is labeled as ‘time traveller’ while the main character (‘me’) is studying history, therefore is probably a (high-school) student. It has already been noted that these ‘Reaction’ memes depict shareable life situations: to achieve the identification with users, these memes adopt different strategies. One of them is to represent a scene which is sufficiently plausible and yet vague: this means, on the one hand, to situate the event within widelyknown locations (e.g. in school, at home, in a shop) populated by familiar characters (a friend, a relative, a schoolmate) and, on the other hand, to leave further details unspecified (e.g. the type of school). In so doing, the meme creates a connection between the main character involved in the events and the viewer of the meme, which is assumed to have had similar experiences to those represented. From a linguistic point of view, this is further reinforced by specific linguistic cues. Specifically, the memes featuring the ‘When’ pattern make use of the second person singular ‘you’, adjective and pronouns like ‘your’/’yours’ and the related verb conjugation: e.g. “When they tell you that stress balls are supposed to be squeezed and not thrown at people stressing you” (Figure 14) and “When your son breaks his when riding the bicycle, but you put a band-aid on and kiss it so it’s all right” (Figure 15). Similarly, the pronouns ‘you’ (Figure 16) or ‘me’ (see Figure 18) are always present in the dialogic exchange of the ‘Script’. Once established, the identification triggered by the combination of these strategies (i.e. plausibility, vagueness and linguistic features) is extended to the resolutive part. At a first glance, it may seem that the images have no meaningful connection with the scene described in the written parts, as in the case of Michael Myers in Figure 16 or Baby Yoda/Grogu in Figure 14. That is because the continuity is not given by the image per se but by the echoic sense of irony, which attributes someone’s thought, assertion, dialogic exchange, gesture and/or facial expression to a character present in the meme. As a result, the apparent gap between the written part and the image is cancelled and the consistency of the sense is restored by means of this analogy. In memes featuring the ‘Script’, the link is made even more explicit by the last line, which introduces the image associating it to one of the characters already mentioned (e.g. Figure 18). Allegedly, even without relying on this kind of linguistic indications, the users have no problems in establishing the connection and thus in deciphering the humoristic sense of the meme. However, to be deciphered correctly is not enough for a meme to go viral and it is assumed that popular memes are those who resonate with many users and thus are frequently shared. While investigating the decision-making process leading users to prefer (and circulate) some content instead of others is vital to understand the different success rate of memes, this aspect goes beyond the scope of the present analysis. To summarize, ‘Reaction’ memes displaying relatable life situations rely on both echoic irony and identification with the viewer to construct their meaning and the humoristic effect. In this sense, it can be speculated that the success of the memes in terms of engagement and spreadability depends on the users recognizing themselves in the situation described and, crucially, in the ironical resolution.

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 Methodological Directions for the Study of Memes

Figure 17. (lit. “I remember Jesus… He was a lively child”)

CONCLUSION AND FUTURE RESEARCH DIRECTIONS The analysis undertaken in this chapter answered two questions concerning meme production: (1) Which meme formats are currently circulating online? (2) How do popular meme formats convey their message? The manual coding shows that most of the memetic production revolves around four popular templates, among which ‘Reaction’ memes play the leading role, a tendency which possibly stretches beyond the borders of the dataset considered. The obtained results also point towards the standardisation of identi-

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 Methodological Directions for the Study of Memes

Figure 18. (lit. “Time traveller: What are you studying? / Me: The World War / Time traveller: Which one of the five? / Me:”)

fied formats, which in turn entail fixed mechanisms of sense making and irony construction. With this regard, ‘Reaction’ memes prove to be particularly suitable to trigger the identification with the audience both at a content level, as they mostly deal with typical life situations, and at a structural level, by making use of linguistic cues like personal pronouns and verb conjugations. Echoic irony includes references to pop-culture and current events, which might foster the identification even further, by leveraging on

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 Methodological Directions for the Study of Memes

a cultural imaginary shared by many users. This is believed to play a relevant role in influencing the popularity of a meme, provided that memes that resonate (i.e. trigger the identification) with many users will be shared more. Since this aspect falls beyond the scope of the present analysis, this hypothesis should be adequately tested with a dedicated research design. Future research should also test the replicability of the methodology employed in this chapter with other samples: in particular, modifications to the devised codebook are required in order to analyse datasets containing video memes. Despite the constraints imposed by the dataset and the limited time frame considered, the analysis presented in this chapter has two relevant strength points: on the one hand it proposes an updated typological systemization, giving insights on the patterns through which memes serve as a means of self-expression; and, on the other hand, it seeks to advance the methodology of the visual communication research area, by expanding previous indications (Shifman, 2013) into a reproducible toolkit to analyse memes and their contribution to contemporary visual culture.

ACKNOWLEDGMENT This research received no specific grant from any funding agency in the public, commercial, or not-forprofit sectors.

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Bischetti, L., Canal, P., & Bambini, V. (2021). Funny but aversive: A large-scale survey of the emotional response to COVID-19 humor in the Italian population during the lockdown. Lingua, 249, 102963. doi:10.1016/j.lingua.2020.102963 Blackmore, S. (2000). The meme machine. Oxford University Press. Börzsei, L. K. (2013). Makes a Meme Instead. The Selected Works of Linda Börzsei, 1-28. Bourdieu, P. (1984). Distinction: A social critique of the judgement of taste. Harvard University Press. Boxman-Shabtai, L., & Shifman, L. (2014). Evasive targets: Deciphering polysemy in mediated humour. Journal of Communication, 64(5), 977–998. doi:10.1111/jcom.12116 Bozkuş, Ş. B. (2016). Pop polyvocality and Internet memes: As a reflection of socio-political discourse of Turkish youth in social media. Global Media Journal: Turkish Edition, 6(12), 44-74. Bracciale, R. (2020). Sharing a Meme! Questioni di genere tra stereotipi e détournement. SocietàMutamentoPolitica, 91-102. Brodie, R. (2009). Virus of the mind: The new science of the meme. Hay House, Inc. Burgess, J. (2006). Hearing ordinary voices: Cultural studies, vernacular creativity and digital storytelling. Continuum, 20(2), 201–214. doi:10.1080/10304310600641737 Caliandro, A., & Gandini, A. (2016). Qualitative research in digital environments: A research toolkit. Routledge. doi:10.4324/9781315642161 Chen, J. (2020, May 6). Important Instagram stats you need to know for 2020. Sprout Social. Retrieved March 2021 from https://sproutsocial.com/insights/instagram-stats/ Dahlgren, P. (2009). Media and Political Engagement: Citizens, Communication, and Democracy. Cambridge University Press. Davison, P. (2012). The language of internet memes. The Social Media Reader, 120-134. Dawkins, R. (2016). The selfish gene. Oxford University Press. DeCook, J. R. (2018). Memes and symbolic violence: #proudboys and the use of memes for propaganda and the construction of collective identity. Learning, Media and Technology, 43(4), 485–504. doi:10.1 080/17439884.2018.1544149 Denicolai, L. (2019). Satira grassroot: Il meme come ipotetico discendente (collettivo) del comico, La Valle dell’eden, 34, 73-86. Denisova, A. (2019). Internet Memes and Society: Social, Cultural, and Political Contexts. Routledge. doi:10.4324/9780429469404 Di Igor Pizzirusso, G. S., Meloni, I., Mantovani, F., & Di Legge, M. (2019, March 12). Questa è public history? I meme e la storia. Retrieved March 2021 from http://www.novecento.org/wp-content/uploads/2019/03/meme-per-pdf.pdf Distin, K. (2005). The selfish meme: A critical reassessment. Cambridge University Press.

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Drakeposting. (n.d.). Retrieved March 2021 from Know Your Meme https://knowyourmeme.com/ memes/drakeposting Dynel, M. (2016). “I has seen Image Macros!” Advice Animals memes as visual-verbal jokes. International Journal of Communication, 10, 660–688. Elkin, E. (2021, March 2). SpongeBob’s Meme Empire Put to the Test With Paramount+ Debut. Bloomberg. Retrieved March 2021 from https://www.bloomberg.com/news/articles/2021-03-02/spongebob-smeme-empire-put-to-the-test-with-paramount-debut Fairclough, N. (1995). Critical Discourse Analysis. Addison Wesley. Fantoni, L. (2018, January 18). Fenomenologia di Alberto Angela, tra divulgazione ed emozione. Wired. Retrieved March 2021 from https://www.wired.it/attualita/2018/01/18/fenomenologia-alberto-angeladivulgazione-successo/ Fauzi, A., Riansi, E. S., & Kurniasih, D. (2020). Expressive Action on Meme in Instagram Towards The Election of President and Vice President 2019. AKSIS: Jurnal Pendidikan Bahasa dan Sastra Indonesia, 4(2), 252-269. Finkelstein, R. (2008). Introduction to the Compendium and a Military Memetics Overview. In A Memetics Compendium (pp. 12-20). Retrieved March 2021 from https://robotictechnologyinc.com/images/ upload/file/Memetics%20Compendium%205%20February%2009.pdf Gal, N., Shifman, L., & Kampf, Z. (2016). “It Gets Better”: Internet memes and the construction of collective identity. New Media & Society, 18(8), 1698–1714. doi:10.1177/1461444814568784 Grandjean, M. (2015). GEPHI: Introduction to Network Analysis and Visualisation. Retrieved March 2021 from http://www.martingrandjean.ch/gephi-introduction/ Heiskanen, B. (2017). Meme-ing electoral participation. European Journal of American Studies, 12(2), 12–2. doi:10.4000/ejas.12158 Heylighen, F., & Chielens, K. (2009). Cultural evolution and memetics. Encyclopedia of complexity and systems science, 3205-3220. Holiday, S., Lewis, M. J., Nielsen, R., Anderson, H. D., & Elinzano, M. (2016). The selfie study: Archetypes and motivations in modern self-photography. Visual Communication Quarterly, 23(3), 175–187. doi:10.1080/15551393.2016.1223548 Hristova, S. (2014). Visual memes as neutralizers of political dissent. tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society, 12(1), 265–276. Hu, Y., Manikonda, L., & Kambhampati, S. (2014). What We Instagram: A First Analysis of Instagram Photo Content and User Types. Proceedings of the International AAAI Conference on Web and Social Media, 8(1). Retrieved March 2021 from https://ojs.aaai.org/index.php/ICWSM/article/view/14578 Huntington, H. E. (2013). Subversive memes: Internet memes as a form of visual rhetoric. AoIR Selected Papers of Internet Research.

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Hussain, S., Muhammad, L. J., & Yakubu, A. (2018). Mining social media and DBpedia data using Gephi and R. Journal of Applied Computer Science & Mathematics, 12(1), 14–20. doi:10.4316/JACSM.201801002 Jameson, F. (1991). Postmodernism, or, the cultural logic of late capitalism. Duke University Press. Knobel, M., & Lankshear, C. (2005). Memes and affinities: Cultural replication and literacy education. Paper presented at the annual National Reading Conference (NRC), Miami, FL. Retrieved March 2021 from http://everydayliteracies.net/files/memes2.pdf Knobel, M., & Lankshear, C. (2007). Online memes, affinities, and cultural production. A New Literacies Sampler, 29, 199-227. Kotthoff, H. (2003). Responding to irony in different contexts: On cognition in conversation. Journal of Pragmatics, 35(9), 1387–1411. doi:10.1016/S0378-2166(02)00182-0 Kronfeldner, M. (2014). Darwinian creativity and memetics. Routledge. doi:10.4324/9781315729107 Laineste, L., & Voolaid, P. (2017). Laughing across borders: Intertextuality of internet memes. The European Journal of Humour Research, 4(4), 26–49. doi:10.7592/EJHR2016.4.4.laineste Literat, I., & van den Berg, S. (2019). Buy memes low, sell memes high: Vernacular criticism and collective negotiations of value on Reddit’s MemeEconomy. Information Communication and Society, 22(2), 232–249. Livingstone, S. (2004). Media literacy and the challenge of new information and communication technologies. Communication Review, 7(1), 3–14. Lolli, A. (2020). La guerra dei meme. Fenomenologia di uno scherzo infinito. Effequ. Lucariello, J. (1994). Situational irony: A concept of events gone away. Irony in language and thought, 467-498. Marino, G. (2014). Keep calm and Do the Harlem Shake: meme, Internet meme e meme musicali. In Corpi mediali. Semiotica e contemporaneità (pp. 85-105). Edizioni ETS. Marino, G. (2019). La gente, gli arcobaleni e Salvini. Internet meme, viralità e politica italiana. Rivista Italiana di Filosofia del Linguaggio, 13(2), 103–138. Massanari, A. (2013). Playful participatory culture: Learning from Reddit. AoIR Selected Papers of Internet Research, 3. Mazzoleni, G., & Bracciale, R. (2019). La politica pop online. I meme e le nuove sfide della comunicazione politica. Il Mulino. McCulloch, G. (2019). Because Internet: Understanding how language is changing. Random House. Milner, R. M. (2016). The world made meme: Public conversations and participatory media. MIT Press. Murray, P. J. (1997). Using virtual focus groups in qualitative research. Qualitative Health Research, 7(4), 542–549.

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Nagle, A. (2017). Kill all normies: Online culture wars from 4chan and Tumblr to Trump and the altright. John Hunt Publishing. Nissenbaum, A., & Shifman, L. (2017). Internet memes as contested cultural capital: The case of 4chan’s/b/board. New Media & Society, 19(4), 483–501. Norris, S. (2004). Multimodal Discourse Analysis: A conceptual framework. Discourse and technology: Multimodal Discourse Analysis, 101-115. O’Donnell, N. H. (2018). Storied Lives on Instagram: Factors Associated With the Need for PersonalVisual Identity. Visual Communication Quarterly, 25(3), 131–142. O’Reilly, T. (2005, September 30). What Is Web 2.0. Retrieved March 2021 from http://www.oreillynet. com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html Omena, J. J., Rabello, E. T., & Mintz, A. G. (2020). Digital Methods for Hashtag Engagement Research. Social Media+ Society, 6(3), 1-18. Phillips, W. (2015). This is why we can’t have nice things: Mapping the relationship between online trolling and mainstream culture. MIT Press. Reaction Images. (n.d.). Retrieved March 2021 from Know Your Meme: https://knowyourmeme.com/ memes/reaction-images Rogers, R. (2013). Digital methods. MIT Press. Rose, G. (2016). Visual methodologies: An introduction to researching with visual materials. Sage (Atlanta, Ga.). Ross, A. S., & Rivers, D. J. (2019). Internet Memes, Media Frames, and the Conflicting Logics of Climate Change Discourse. Environmental Communication, 1-20. Rowlett, J., & Harlow, S. (2018). Selfies and Sensationalism on the Campaign Trail: A Visual Analysis of Snapchat’s Political Coverage. Visual Communication Quarterly, 25(2), 82–92. Salvini, M. (2020, December 6). Facebook status update. Retrieved March 2021 from https://www. facebook.com/252306033154/posts/10158266698658155 Senft, T. M., & Baym, N. K. (2015). What does the selfie say? Investigating a global phenomenon. International Journal of Communication, 9, 1588-1606. Shifman, L. (2012). An anatomy of a YouTube meme. New Media & Society, 14(2), 187–203. Shifman, L. (2013). Memes in a digital world: Reconciling with a conceptual troublemaker. Journal of Computer-Mediated Communication, 18(3), 362–377. Shifman, L. (2014). Memes in digital culture. MIT Press. Sperber, D. (1984). Verbal irony: Pretense or echoic mention? Journal of Experimental Psychology. General, 113(1), 130–136.

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Spitzberg, B. H. (2014). Toward a model of meme diffusion (M3D). Communication Theory, 24(3), 311–339. Thornton, S. (1996). Club Cultures: Music, Media and Subcultural Capital. Wesleyan. Tuters, M., & Hagen, S. (2020). (((They))) rule: Memetic antagonism and nebulous othering on 4chan. New Media & Society, 22(12), 2218–2237. Van Zoonen, L. (2005). Entertaining the citizen: When politics and popular culture converge. Rowman & Littlefield. Venturini, T., & Rogers, R. (2019). “API-Based Research” or How can Digital Sociology and Journalism Studies Learn from the Facebook and Cambridge Analytica Data Breach. Digital Journalism, 7(4), 532–540. Wells, D. D. (2018). You all made dank memes: Using internet memes to promote critical thinking. Journal of Political Science Education, 14(2), 240–248. Wiggins, B. E., & Bowers, G. B. (2015). Memes as genre: A structurational analysis of the memescape. New Media & Society, 17(11), 1886–1906. Wilson, D. (2006). The pragmatics of verbal irony: Echo or pretence? Lingua, 116(10), 1722–1743. Yus, F. (2018). Identity-related issues in meme communication. Internet Pragmatics, 1(1), 113–133. Zannettou, S., Caulfield, T., Blackburn, J., De Cristofaro, E., Sirivianos, M., Stringhini, G., & SuarezTangil, G. (2018, October). On the origins of memes by means of fringe web communities. Proceedings of the Internet Measurement Conference 2018, 188-202. Zittrain, J. L. (2014). Reflections on Internet culture. Journal of Visual Culture, 13(3), 388–394.

ENDNOTES 1 2

3



fb.com/celebratepride Note that when a specific character is depicted (or cited), the label indicates the piece of artwork it comes from (e.g. the name of the movie, or the cartoon, and so on). Due to the inevitable limit of knowledge and literacy, the author also relied on external sources to identify some of the references: in particular, tools like online meme repositories (e.g., Know Your Meme), Google and the Google Image reverse search have been useful in this operation.

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Chapter 37

The “Blue Check” Communication on the Pandemia Vaccine Campaign:

Analysis of Tweets From Verified Accounts Domenico Trezza University of Naples Federico II, Italy Miriam Di Lisio University of Naples Federico II, Italy

ABSTRACT This chapter has the exploratory goal of understanding the attitudes and perceptions of ‘verified’ Twitter (VA) accounts about the COVID-19 vaccine campaign. Identifying their sentiment and opinion about it could therefore be crucial to the success of vaccination. A content analysis of tweets from the period December 24, 2020 to March 23, 2021 about the vaccine campaign in Italy was conducted to understand the semantic strategies used by VAs based on their orientation toward the vaccine, whether pro, anti, or neutral, and their possible motivations. Topic modeling allowed the authors to detect five prevalent themes and their associated words. A sentiment analysis and opinion analysis were performed on a smaller sample of tweets. The results suggest that ‘authoritative’ opinion about the vaccine has been very fragmented and not entirely positive, as expected. This could prove to be a critical issue in getting the vaccine positively accepted by the public.

INTRODUCTION On December 27 in Italy was held the Vaccine day, the “symbolic” beginning of the vaccination campaign against COVID-19. It is a historic event because it involves at the same time almost the entire world population. Although the vaccine represents evidence of worldwide cooperation and a great step forward by the scientific community, it has not gained unanimous support from the population. VacDOI: 10.4018/978-1-7998-8473-6.ch037

Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 The “Blue Check” Communication on the Pandemia Vaccine Campaign

cination against COVID-19 is not free from a significant feeling of diffidence. Digital media and social platforms have been the privileged channels for the unfiltered circulation of no-vax feelings. Negative perceptions towards the vaccine are not new, and researchers are constantly studying evidences on ‘novax’ or ‘vaccine-hesitancy’ attitudes. However, widespread distrust of the Coronavirus vaccine campaign has reached very high levels, and this risks compromising or delaying population immunity. Social media represent the ideal environment to study this phenomenon.We know, however, that in the formation of opinions, attitudes and beliefs is very relevant the mediation of actors or groups as credible sources. Studying directly the communication of these sources can better highlight the mechanisms behind the vaccine social narrative. Lack of trust in the health care system appears to be among the triggers for opposition to vaccines (Kata, 2010). In fact, in recent times, a new model of health care has emerged, in which power has shifted from physicians to patients and the validity of science is constantly questioned (Kata, 2012). The Internet has facilitated this process of democratization of knowledge by making the process of discerning authoritative sources complex. About 80% of Internet users search for health information online and about 16% of them search for online news about vaccinations, bypassing direct contact with health care professionals. The communication that circulates online about the Coronavirus vaccination campaign could decisively affect its success. This contribution aims to understand the attitude and perception of actors (public or private organizations and individuals) very popular and with a large following on the social platform of Twitter. They inspire people’s trust likely to influence their beliefs and perceptions towards the vaccine. So identifying their sentiment and opinion about it could be crucial for the success of the vaccination campaign. A text analysis of the tweets of ‘verified’ user accounts (VA (‘Verified Account’) henceforth) on the vaccine campaign in Italy was carried out to understand the semantic strategies used by VAs according to their orientation towards the vaccine, whether in favor, against or neutral, and their possible motivations. Understanding the communication strategies of the very popular actors on the vaccine could be useful to identify at an early stage the trends of public opinion and the most frequently circulating issues. The paper is divided into 4 sections: the first one introduces the theoretical background, describing the evolution of opinion leader in the digital environments and its importance in the construction of attitudes. There will also be room for a brief digression on the typology of attitudes towards the vaccine identified in the literature. The second section describes the methods used, i.e., tweet collection and data analysis tools, and the methods that involved in-depth investigation of a small sample of tweets. The third gives an overview of the results and outputs of the sentiment and opinion analysis. The last part is devoted to the concluding discussion of the results and possible future research.

BACKGROUND The Role of Opinion Leaders: From Off-Line to Digital Environments During the nineteenth century, the scholar John Stuart Mill supported the idea that newspapers could recreate in large societies the same kind of instantaneity and communicative affinity between citizens that in ancient organizations had been ensured by the possibility of physically gathering in a rally. and to interact directly in the agora or the forum. Starting from this assumption, digital media have facilitated the emergence of an “incorporeal forum” of opinions that would have allowed the aggregation of citizens through the public discussion of issues of common interest (Del Lago, 2017). 665

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The innovative role of the network is that which sees users act directly on information: the Internet offers a variety of free sources of information from which to draw that limits the range of traditional media (newspapers and television) and, consequently, their authority. Digital media allow expanding the level of knowledge individually and autonomously, encouraging the dynamics of socialization and cooperating in the creation of representations of reality: the world seen separately, in fact, is represented in organized communities that allow the acquisition of collective existence and its laws (De Biasi, 2002). Not least, opinion leaders can communicate directly with citizens without having to resort to the exclusive mediation of analogue media (Del Lago, 2017). By opinion leader, we mean the individual who can influence the opinion of others, in particular, public opinion: these are people who have the power to influence the decisions of others thanks to their faculty, competence, position, or relationship. From this assumption we understand how anyone can fall into this category in the most disparate ways: one can be influenced and influence other individuals by the position occupied - whether social or professional -, by the knowledge in possession or by the type of relationship (vertical or horizontal). With the birth of the Internet - especially social platforms - and with the emergence of the democratization of knowledge that has allowed anyone to express their opinion on any topic, new figures have emerged, digital personalities believed to be credible: influencers. The latter is nothing more than opinion leaders 2.0 since they perform similar tasks to traditional opinion leaders, being charismatic and accredited personalities, who can count on an important collection from which they are appreciated, which carefully evaluates the news announced by the influencer. In the scenario of web 2.0, the impact of opinion leaders on the formation of beliefs by the population, therefore, has remained unchanged since there has been a simple passing of the baton between influential personalities from the old to the new media. The difference lies in the fact that, unlike analogue communication, digital media allow an unprecedented flow of information in which there is no real information hierarchy, but homogeneity in the possibilities of expressing one’s own opinions and/ or opinions by of any user who can, in turn, become influential. Furthermore, the 2.0 model envisages two communication phases - two-step flow of communication - in which the media do not have a direct effect on the users, but information passes from the latter to the opinion leaders who, in turn, re-elaborate and interpret based on their beliefs and/or opinions and pass them on to their membership groups (Lazarsfeld, Berelson, Gaudet 1944). In other words, the messages are not spread homogeneously, but it is the opinion leaders who spread them to their network of contacts in the way they prefer. It can be seen that the opinion leaders’ way of doing is not so different from that of the average users of social networks, who share content and information with their network of contacts, with the difference that not all social users have the right to influence other people’s opinions. The technological revolution has, therefore, on the one hand made the relations between the whistleblower and informed more horizontal, offering faster dissemination of news in times unthinkable until a few decades ago but has, at the same time, given rise to a sequence of obstructions in the production of information, often giving up, in favor of the instantaneity of communication, filtering, scrupulousness, objectivity, and analysis (Novella, 2016). Personal influence is therefore built on the information and interest of the opinion leader / influencer regarding a specific issue, thanks to which he is recognized as an expert. The relationship that users establish with the opinion leader is based on the trust generated by daily contact: in Twitter - the channel chosen for the analysis in this paper- this trust is established by ascertaining the reliability and agreement of the shared contents by the influencer, as well as through interactionIn this paper, we focused on the

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analysis of tweets posted by verified accounts, or by those accounts that have the “blue check”, or users who presumably have a very widespread perceived credibility.

Attitudes Towards Vaccines on the Internet: NoVax, Pro-Vax and Vaccine Hesitancy The coordination of actions essential to the creation of vaccines is considered by the World Health Organization as one of the fundamental measures in the event of a pandemic. Paradoxically, the vaccination solution is not always frowned upon by the population which is divided between those in favor, those against, and those who are skeptical but do not take a clear position on the matter. The network emphasized the polarization of these personalities who found the online echo chamber ready to reinforce their ideas. The scenario that emerges is very varied, encompassing multiple positions and attitudes - activists, patients, conspiracy theorists, rebel scientists, politicians, charismatic leaders. However, the motivations of no-vax movements have often been understood solely and solely as the result of emotional choices and an incompletely understood how vaccines work at the population level (Blume, 2006). What makes the tolerance of vaccines even more complicated by the less experienced is undoubtedly the enormous amount of uncontrolled and uninformative data that is transmitted online every day. Internet and fake news go hand in hand: the phenomenon has exploded since the beginning, making it increasingly difficult to use correct and reliable information. The Italian Communications Authority (AGCOM) highlighted that disinformation has increased dramatically since the end of 2017 and scientific and technological topics represent about 19% of the total disinformation market, second only to politics (57%). Political and health misinformation became the focus of heated debate in 2017, the year in which a measles epidemic broke out that infected thousands of people (Filia et al., 2017). The controversy on vaccinations was much discussed in the elections (elections of 4 March 2018), especially regarding the Lorenzin Decree (which took its name from Beatrice Lorenzin, Italian Health Minister at that time) which introduced the compulsory vaccination of children up to 12 years of age, to try to reverse the condition of regression that Italy has faced in previous years regarding the vaccine doubt (Chirico, 2018). From that moment until today the situation has not changed, quite the contrary. With the sudden outbreak of the COVID-19 pandemic starting in December 2019, we clung to hope in the vaccine solution as the only way out of the virus that has blocked the lives of the world population for more than a year. Even the skeptics were afraid for a moment but then, with the invention of the COVID-19 vaccine, the situation was restored: the disinformation battle against vaccinations was renewed. The Internet has allowed the sudden disclosure of false and/or misleading information. The network is often a bad advisor when it comes to vaccinations as it is easy to run into falsehoods and lies (Grignolio, 2016). The fear of experts can be found in the quote from journalist Kevin Roose that appeared in an article in the New York Times: “The other night I had a terrifying thought: what if we get a vaccine against COVID-19 and half the country refuses to do it? “. And it is precisely for this reason that the institutions immediately committed themselves, through the use of the official pages and/or on the personal profiles of their exponents on social networks, to make positive publicity for the vaccine, underlining its importance, seeing vaccination as the only solution for the recovery of the country. Even social networks, including Facebook and Instagram, have mobilized to strengthen measures against health disinformation trying to stem the disclosure of misleading news on vaccine remedies. It has been noted that the amount of fake news produced on COVID-19 vaccinations has been excessive and the leaders of the most used social 667

 The “Blue Check” Communication on the Pandemia Vaccine Campaign

platforms have tried to stem the proliferation of false news and no-vax and no-mask ideologies, removing all those posts, stories, announcements, but also accounts, pages and groups deemed “unreliable” submitted to the attention of a scientific committee. Given the mobility of institutions regarding the need for correct information on the vaccine, with this paper, we wanted to investigate the communication of verified accounts concerning the COVID-19 vaccine.

METHODS Data Collection This study considered tweets about the vaccine produced from December 24, 2020 (the start of the vaccine campaign) until March 23, 2021, at the height of the vaccine campaign. The period was rich in social debate about the vaccine. The social platform Twitter is very relevant with these research objectives because according to a ‘follow the medium’ approach we used the advantages of this social to: 1. Index the open-access content through semantic keys (hashtags). 2. Identify the type of actor according to the functionality ‘account verified’ according to our research objectives. 3. Extract tweets automatically thanks to the API (Application Programming Interface) through a few inputs to set the time range, the number and other characteristics of the corpus (only verified actors and no retweets). In relation to the first point, the extraction keys were selected according to the topic trends in Italy over the previous 36 hours and then constantly updated. The starting hashtag base was constituted by generic words related to vaccine (#vaccino, #vaccinoAntiCovid, #vaccini, #vaccin, #vaccination, #vaccinoitalia, #vaccinoCovid), to specific terms based on the debate orientation (#novax, #pfizer, #Pfize rBiontech,#vaccinoPfizer), to first day of vaccinations in Italy (December 27, #vaccineday, #vaxday). The updates of the extraction base were exclusively about the names of the new available vaccines that have generated attention and debate. There were four steps of textual key update: 1. #moderna (January 12, 2021), 2.#astrazeneca (February 5, 2021), 3.#sputnik (March 3, 2021). The extraction was done through the R environment. R is an open software which works on programming strings and operating packages. We used ‘Rtweet’ package to use Twitter’s Application Programming Interface (API), which is the operational interface for scraping tweets. We used ‘Rtweet’ package to use Twitter’s Application Programming Interface (API), which is the operational interface for scraping tweets. The package’s ‘search_tweets’ function allowed for extraction settings, such as textual keys, temporal location, Italian language, and exclusion of retweets to reduce word recursiveness. Ai fini dei nostri obiettivi, abbiamo settato l’informazione “verified” su “TRUE”. For the purposes of our goals, we set the ‘verified’ information to ‘TRUE’. In this way, the extraction was limited to tweets from accounts with the ‘blue check’, i.e. verified account (VA). Introduced in June 2009, the Twitter verification system provides the site’s readers with a means to distinguish genuine notable account holders, such as celebrities and organizations, from impostors or parodies. A blue check mark displayed against an account name indicates that Twitter has taken steps to ensure that the account is actually owned by the person or organization whom it is claimed to represent. This account verification system has also been replicated by other platforms 668

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such as Facebook. Given the limitations of the free standard version of the API (download no later than 6 days before) we extracted daily. The final corpus consists of 11116 tweets, already organized in matrix with 90 default variables. The reduction of the matrix was therefore inevitable: we considered the information related to the tweet (if quote or not, retweet and favorite count, length, text and date) and to the account (nikname, followers, friends and favorites).

Data Analysis Verified Account Classification Plan A classification operation of the users was carried out through manual tagging. It was carried out taking into account the nickname which appeared enough to give a valid category to the user. In most cases the nickname allowed to easily find the type of actor (for example, the nicknames luigidimaio, AmnestySvizzera, RaiNews...). In cases where the nickname was ambiguous, we traced the identity of the user by using Twitter’s search engine. Classification plan has producted 13 categories (tab.1). The high specificity of the categories suggested us to keep a high sensitivity in the classification and therefore we preferred not to aggregate. In order to define the classification plan, we proceeded through in-process coding. On no attribution was there disagreement between the two analysts, so we can assert that the reliability is adequate. Table 1. Classification plan of Verified Account 1

Corporations, private or non-governmental organizations

2

Experts (doctors, virologists, professors, etc.)

3

Government or institutional organizations

4

Health or pharmaceutical organizations or foundations

5

Influencer and show biz people

6

Journalists or news people

7

Local media

8

National and international media

9

Political parties, associations or groups

10

Politicians

11

Religious people, institutions or organizations

12

Television programs

13

Web platforms

Text Pre-Processing and Topic Modeling: Tools and Methods The analyses were done in the R environment. The ‘tm’ package for text analysis, the ‘topicmodels’ package for topic modeling, and the ‘ggplot2’ and ‘wordcloud’ packages for graphical representations were used.

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The text of the tweets was pre-processed through several normalization and textual content transformation functions. In fact, the ‘tm_map’ function of the tm package allowed us to 1.Reduce capitalization, 2. Remove stopwords, 3.Stripping white space, 4. Stemming, 5.Remove Punctuation. We used the Term Frequency (TF) weight for vocabulary and word cloud construction. In the text frequency analysis, we also calculated the TF-IDF1. In this way, we eliminated words that were very common but present in all VA categories, thus not very characteristic2. We applied the topic model which is an unsupervised model for the extraction of relevant topics from the text of tweets. The method applied is Latent Dirichlet Allocation (LDA) (Blei, 2003). LDA model assumes that each word in each document comes from a topic and the topic is selected from a per-document distribution over topics. The algorithm used for topic extraction is Gibbs’ algorithm (Darling, 2011).

Sentiment and Opinion Analysis: Tagging and Manual Classification Procedures on Tweets Sample In order to further analyze the content of tweets, a sentiment and opinion analysis (Liu, 2012) was applied through manual tagging on a sample of 260 tweets. Sentiment analysis systems are being applied in social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. The sample of 260 tweets was drawn from the main database, i.e., 20 tweets from each of the 13 account categories. The ideal solution was to select the 2 most “retweeted” tweets of the 10 users with the most followers. Only two tweets because the share of tweets from individual users was very low (in some cases we observed users with only one tweet about the vaccine) so the share seemed consistent. In some cases, in fact, it was not possible to completely respect this criteria (for example, only one tweet associated to the account). We solved this by ‘scrolling’ the ranking and selecting additional accounts. Sentiment was classified according to 4 categories: no sentiment (-2 code, e.g. news), negative sentiment (-1), neutral (0) in case in the tweet the type of sentiment was not clear, positive sentiment (+1). Regarding opinion analysis, the hermeneutic analysis suggested us to adopt six categories: 1. Against vaccine policy, politics or governance, tweets criticizing political actors, policies, or governance of the vaccine campaign. 2. Debunking or against no-vax, tweets aiming to debunk, i.e., expose supposed fake-news. 3. Affordable vaccine for all people, tweets insisting on a equitable and affordable vaccination campaign for all. 4. Confidence and hope for the vaccine and its outcomes, hopeful tweets for a successful vaccination campaign 5. News, tweets reporting news. 6. No Opinion, tweets having no specific opinion. For each VA, both mean sentiment and prevailing opinion were calculated to understand how vaccine perceptions were articulated by digital actors and how opinion changed by conditioning the sentiment expressed.

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RESULTS Analytical Overview on Tweets and Users When did VA most often discuss the vaccine? In the period considered there were two ‘highlight’ moments in communication that generated two ‘peaks’ as observed in fig.1. The first is related to December 27, 2020, which is the ‘Vaccine Day’ that marked the beginning of the vaccination campaign against COVID - 19 in Italy. Not surprisingly. At that moment the whole mood of the country was focused on this event as the gradual return to everyday life. The second moment of greatest ‘hype’ of the Twittersphere corresponded to the middle of March. In those days, in fact, began a heated debate on the Astrazeneca vaccine and its supposed danger to health. The debate focused on some cases of thrombosis that a part of the public opinion attributed to the vaccine. This situation risked seriously compromising the vaccination campaign. In many cases, in fact, people alarmed by news about the association between this vaccine and health complications, decided to decline the invitation to vaccinate. Many stakeholders in the institutional sphere (and not only) have tried to stop this spreading scepticism against Astrazeneca, however the communication of the same company has led several European countries to suspend it. The two moments are therefore different in terms of communication intensity. During the ‘Vaccine day’ the communication was concentrated in a single day and with a large number of interactions by the VA. During the period of the Astrazeneca block the tweets communication curve was more extended in time and more ‘zigzagged’ as if it reflected the general sentiment of indecision. Who were the high-profile stakeholders participating in the vaccine debate in the Twittersphere? Tab. 2 shows a strong imbalance. More than half of the tweets came only from national and international media accounts. Being news channels, they are also the ones most present and ready to launch news in the form of tweets. The other half is more balanced. All accounts in fact produced less than 10% of tweets. The first are the Politicians (9.4%). Vaccination is also and above all a political issue and we were not surprised that many politicians present on the platform talked about this. This is followed by Local media (9.2%), Government or institutional organizations (8.5%) and Journalists (6.8%). Less than 5% all others.

The Analysis of Corpus Hashtag Analysis The analysis of hashtags is useful for us to understand which are the textual keys with the highest frequency (Term Frequency weight). As we expected, the most frequent hashtags are those related to the word #vaccine and #vaccines (fig.2). This is a seemingly obvious result (these are part of our key extractions) but it comforts us because it would signify the high relevance of the central topic within our corpus. An interesting evidence is the high number of hashtags related to the astrazeneca vaccine. As we also observe in the tagcloud this denotes the great debate that has emerged following the blocking of this vaccine. Among the most mentioned hashtags are some that were not used in the extraction grid, so they are quite relevant. They are hashtags of information (#Ansa, #ioseguotgr) emphasizing the widespread presence of the vaccine campaign in the media agenda; politics (#draghi, #ue); governance of the vaccine campaign, (#sardinia, #lombardia).

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Figure 1. Frequency of tweets over the interval of time

Table 2. Nr. of tweets by user type Nr. Tweets From

a.v.

%

National and international media

5722

51,5%

Politicians

1049

9,4%

Local media

1026

9,2%

Government or institutional organizations

948

8,5%

Journalists or news people

760

6,8%

Web platforms

519

4,7%

Corporations, private or non-governmental organizations

277

2,5%

Television programs

246

2,2%

Political parties, associations or groups

157

1,4%

Expert people (doctors, virologists, professors, etc.)

139

1,3%

Health or pharmaceutical organizations or foundations

105

0,9%

Influencer and show biz people

85

0,8%

83

0,7%

11116

100

Religious people, institutions or organizations tot. tweets

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Figure 2. Hashtag frequency

Wordcloud and Word Frequency The wordcloud in fig.3 shows us overall the most frequently used words by VA. As we expected, Astrazeneca is the most frequent lemma as during the period of the analysis, the debate about the different types of vaccine and the supposed dangerousness of the Astrazeneca vaccine was very relevant and went viral on social platforms. However, the debate also involved other vaccines. In the wordcloud we notice in fact words like Pfizer, Moderna and Sputnik. This is a indication that the VA discussed the different producers of vaccines and therefore their presumed efficacy, ineffectiveness or even, as in the case of Astrazeneca, presumed side effects Several lemmas related to the governance of the vaccination campaign emerge, such as Draghi3, Ema4, Arcuri5, Regions. Not surprisingly, the high frequency of vaccineday used on the first day of the vaccination campaign. We examine the frequency of lemmas (TF) used by each VA. Fig.4 containing the wordfrequency with the five most used words of the VA suggests us that each actor in the vaccine debate uses a language congruent with its category. By reading their vocabulary, it is possible to detect certain topics in advance. An example is the category of political parties and politicians in which we observe lemmas that fall into the dimension of campaign governance (plan, campaign, government, citizens). Other VAs, such as government actors or those in the national media, use language consisting of more formal

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Figure 3. Wordcloud of tweets

words for the bulletin of infections, vaccinated or vaccine stock availability (Pfizer, millions, vaccinated, deaths, swabs...). Also peculiar is the top-five word of religious actors, which, consistent with their value orientation, consists of words that refer to an equitable allocation of vaccines and free access for all.

Topic Modeling The topic modeling allows us to investigate the preliminary evidences emerged from the word analysis and to highlight the most relevant issues. We performed an automatic extraction of the most characteristic topics. The LDA approach returns five topics. This number satisfies us since the terms of each group are semantically linked and it is therefore easy to construct the sense of each topic (fig.5).

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Table 3. Wordcloud Italian original word and English translate Original Word

Translate

campagna

campaign

dati

data

dosi

doses

febbraio

February

governo

Government

medici

doctors

milioni

millions

oggi

today

persone

persons

piano

plan

prima

first

somministrazioni

administrations

Stato

State

vaccinale

vaccinal

Figure 4. VA wordfrequency

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Figure 5. Topics more characteristics

1. ’Allocation and effect on is the group of words on the allocation of vaccines in Italy and on the first effects of the pandemic (fig.6). 2. ’Producer debate’ is the debate on the different vaccines available and their possible efficacy. Astrazeneca, Pfizer, Moderna, Johnson, are now terms ‘known’ entered the lexicon of all people, especially of people of great public interest (fig.7). 3. “News of the vaccination campaign’ is the topic consisting of terms pertaining to the ‘news’ of the vaccination campaign, the vaccinated, the categories to be vaccinated (fig.8). 4. ’Governance and Plan’ is made up of words referring to the government’s strategies on the vaccination plan. In short, these are groups of terms that are related to the governance and management of vaccinations (fig.9) 5. ’Vaxday. Beginning of hope’ is the topic in which mainly include the words used during the first days of the vaccination campaign. These are words that refer to the semantic universe of hope and the vaccine as the first sign of a return to normality (fig.10).

Sentiment and Opinion Analysis on Vaccination Campaign Sentiment analysis on the small sample of 260 tweets6 (fig.11) revealed that in the majority of tweets, 111 out of 260, VAs talked about the vaccine through a positive sentiment, while just over half (61) were tweets carrying a negative sentiment. In the remaining portion of tweets (88), we identified neutral sentiment (i.e., tweets in which no specific sentiment could be identified) or no sentiment at all. In the latter case, these were tweets reporting news stories or simple statistics. This sample implies that the evidence emerging on sentiment and opinion could only be attributed to the most viral VAs (thus those that might have the greatest impact on digital public opinion).

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Figure 6. Topics ‘Allocation and effect on pandemic’

Opinion analysis allows us to interpret the sentiment of the tweet in more depth by going to understand the semantic content. The pie chart (fig.12) shows us that the most frequent opinion is the one referring to hope and trust in the vaccine: 32% of tweets come from this area. Another important share of tweets does not have a particular opinion but represents news (25%). If we aggregate this share to that of noopinion tweets (8%) we observe how the number of tweets that do not present a specific opinion becomes prevalent. A significant share of tweets (about 17%) is devoted to criticism of political figures, policies, or vaccine governance. Still, 11% of tweets acknowledge the importance of vaccine accessibility for all people, while only 7% of tweets contain the intention of debunking or posturing against no-vaxers. Considering the type of VA, how is sentiment articulated and what are the prevailing opinions? The question is very interesting because it allows us to understand the attitudes towards the vaccine and the communication strategies that characterized specific groups of accounts. We calculated the average sentiment (-1 negative, 0 neutral or no sentiment, +1 positive) and classified three macro-groups of sentiment according to the score below, near or above the average. For each actor, we detected the most frequent sentiment. In the area of positive sentiment we find the accounts of politicians, health foundations and web platforms that present Confidence and hope for the vaccine and its outcomes as the prevailing opinion.

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Figure 7. Topics ‘Producer debate’

We assume that the positive attitude of the accounts of politicians is conditioned by the Italian government of broad agreement that took over at the beginning of the year. Health foundations for likely partisan interests. Web platforms are not official sources of information have often expressed opinions (positive) towards the vaccine campaign. In this area we also find the religious actors who, however, have different opinions. They have expressed - positively - a democratic distribution of the vaccine (Affordable vaccine for all). The last category in this area belongs to institutional government actors who by their nature do not present an obvious opinion but their tweets have average positive sentiment (News). The middle area consists of VAs belonging to Influencers and show biz people whose sentiment is not well delineated because many of them do not have a defined opinion about it. There are the accounts of National and International media and Television programs that tend to offer official news about the vaccine, therefore lacking in opinion and sentiment. The tweets of experts are inevitably characterized by confidence and hope for the vaccine, however with a very cautious attitude towards the virus that affects sentiment. In the third area, that of negative sentiment, we observe the VA of political parties and journalists who through their tweets express their disappointment with politicians, policy or governance of the

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Figure 8. Topics ‘News of the vaccination campaign’

vaccine (Against vaccine policy, politics or governance). The other two categories are related to local media and Corporations. Local media mostly tweet news but negatively connoted probably because they report on local poor management and experiences of the vaccine campaign. The prevailing view of the Corporations, on the other hand, is full vaccine accessibility for all. Unlike religious actors, however, this time the opinion is expressed in a critical key and probably as a statement of inequality between poor and rich countries in the allocation of vaccines.

DISCUSSION Result Outcomes As it was to be expected, the vaccination campaign against COVID-19 represented a communication event of a great importance. The vaccine is the only way to get out of the health emergency as soon as possible.

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Figure 9. Topics ‘Governance and Plan’

The awareness of vaccines is therefore an absolute priority for institutions and for the very popular people with the ability to influence people’s attitudes and behaviors. This paper does not have specific research questions, but is driven by merely exploratory intentions. The intention of the paper was to find out how people who receive a lot of public attention and have large followings (thus who can have a large impact on people’s opinions) participated in the vaccine debate on Twitter. Embracing Rogers’ (2016) suggestion of “follow the medium,” we used the tool Twitter uses to recognize verified accounts, the blue check, to identify actors of high public interest. Our assumption was that VAs’ communication mechanisms were similar to those of opinion leaders. However, VAs are a broad category, encompassing a very wide variety of users, from the institutional actor to the influencer. This is why we felt it necessary to perform a side-by-side classification of VAs. Manual tagging returned 13 classes of actors that we did not merge because each actor represented very specific orientations, communication goals, and characteristics. An earlier description of the tweets confirmed two findings we had been expecting. The first is that the hottest moments of the debate corresponded with the first day of the vaccination campaign (the “vax-day”) and with the phase of the blockade of Astrazeneca, arousing great hype even among the VA.

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Figure 10. Topics ‘Vaxday. Beginning of hope’

The second was the national and international media as a category with many more tweets than the others. On the other hand, it is well known that a good part of the registered users on Twitter are users representing the world of information and certainly the vaccination campaign is a very reportable event. Wordfrequency and topic analysis revealed how the field of topics about the vaccine campaign was broad, ranging from discussion about governance (and therefore management of the vaccine campaign) to debate related to vaccine manufacturers, with Astrazeneca monopolizing this type of discussion for the reasons already mentioned. Part of the VA’s vaccine communication also focused on both the distribution and allocation of the vaccine and on reporting the vaccine bulletin or categories to which the vaccine was being made available. This type of analysis, however, returned an interesting but partial view of what our exploratory intentions called for. Sentiment and opinion analysis on a sample of tweets allowed us a useful insight to understand: 1. What was the direction of VA sentiment on the vaccine. 2. How their opinion on the subject was articulated.

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Figure 11. Tweet sentiment (a.v.)

Figure 12. Opinion on vaccine (% of tweets)

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Table 4. Sentiment and prevalent opinion per VA type Sentiment Mean

VA type

Sentiment

Prevalent Opinion

% Opinion Tweet

Politicians

0,65

Confidence and hope for the vaccine and its outcomes

55

Health or pharmaceutical organizations or foundations

0,55

Confidence and hope for the vaccine and its outcomes

45

Religious people, institutions or organizations

0,4

Affordable vaccine for all

55

Web platforms

0,4

Confidence and hope for the vaccine and its outcomes

45

Government or institutional organizations

0,35

News

60

Influencer and show biz people

0,25

No Opinion

30

National and international radio-tv channels

0,2

News

50

Confidence and hope for the vaccine and its outcomes

55

☺



Experts (doctors, virologists, professors, etc.)

0,1

Television programs

0

News

40

Political parties, associations or groups

-0,05

Against vaccine policy, politics or governance

35

Journalists or news people

-0,05

Against vaccine policy, politics or governance

35

Local radio - tv channels

-0,1

News

60

Corporations, private or non-governmental organizations

-0,2

Affordable vaccine for all

50

☹

We have found some unexpected evidence for the first and second points. For the first one, because we did not expect that in 58% of tweets we did not find positive sentiment. For the second because opinion was quite fragmented. This shows how the VA represent a wide variety of profiles: from politicians who have expressed positive opinion and sentiment based on the hope of the vaccine, to those of private companies that instead have communicated on the campaign in a negative way and insisting, in a critical key, the accessibility of the vaccine for all.

Study Limitations and Future Research There are some limitations to the study that will certainly be a starting point for developing and improving future updates to this survey. We have identified three of them. The first limitation is certainly the time period of only three months. The second limitation is also related to time. We realized from the preliminary descriptive analysis that tweets were more concentrated in two moments: the first when the vaccination campaign started, the second during the Astrazeneca vaccine block. Here, it would be interesting to understand topic trends on the time continuum to understand how they have changed or evolved. The third limitation comes from the difficulty of generalizing the results of opinion and sentiment because of the very small sample. A possible solution would be the application of supervised techniques on the whole corpus that however would have to be based on a very large labeled base.

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To make the work more complete, a future development of the research could include two types of comparisons. The first comparison is with unverified accounts to find differences or similarities in content and themes. The second comparison could be developed between platforms to understand how the concept of ‘verified’ user is highlighted in other social networks and through what dynamics.

Conclusion This first exploration has shown us how on digital platforms today the concept of “opinion leader” is no longer similar to the traditional one, i.e. a recognizable figure who acted as a filter between knowledge and common opinion. There is a multitude of actors that today acquire authority on digital platforms through a blue check: they are “verified” because they represent an institution, or they are popular characters, or simply because they have a large fan-base. Their communication strategies and language are very different. We have seen how this heterogeneity has had implications also on vaccine communication on which theoretically there should be maximum agreement. This could prove to be a major criticality in getting the public to positively accept the vaccine. Instead, it would be desirable to have a better agreement among important actors and the adoption of a “communicative pact”, especially on digital platforms, to raise awareness of the vaccine and its positive effects on the world’s health situation.

REFERENCES AGCOM. (2018). News vs. Fake in the Information System. AGCOM. Blei, D., M., Ng, A., Y., & Jordan, M., I., (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993-1022. Blume, S. (2006). Anti-vaccination movements and their interpretations. Social Science & Medicine, 62(3), 628–642. doi:10.1016/j.socscimed.2005.06.020 PMID:16039769 Chirico, F. (2018). The new Italian mandatory vaccine Law as a health policy instrument against the anti-vaccination movement. Annali di Igiene Medicina Preventiva e di Comunità, 251–256. Darling, W. M. (2011, December). A theoretical and practical implementation tutorial on topic modeling and gibbs sampling. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies (pp. 642-647). Academic Press. De Biasi, R. (2002). Che cos’è la sociologia della cultura. Carocci Editore. Del Lago, A. (2017). Populismo digitale. La crisi, la rete e la nuova destra. Raffaello Cortina Editore. Filia, A., Bella, A., Del Manso, M., Baggieri, M., Magurano, F., & Rota, M. C. (2017). Ongoing outbreak with well over 4,000 measles cases in Italy from January to end August 2017 - what is making elimination so difficult? Eurosurveillance. Grignolio, A. (2016). Chi ha paura dei vaccini? Codice Edizioni.

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Istituto Superiore di Sanità. (n.d.). Impatto della vaccinazione COVID-19 sul rischio di infezione da SARS-CoV-2 e successivo ricovero e decesso in Italia (27.12.2020 - 03.05.2021). Valutazione combinata dei dati dell’anagrafe nazionale vaccini e del sistema di sorveglianza integrata COVID-19. Author. Kata, A. (2010, February 7). A postmodern Pandora’s box: Anti-vaccination misinformation on the Internet. Vaccine, ▪▪▪, 1709–1716. Kata, A. (2012, May 28). Attivisti contro i vaccini, Web 2.0 e il paradigma postmoderno. Una panoramica delle tattiche e dei tropi usati online dal movimento anti-vaccinazione. Vaccine, ▪▪▪, 3778–3789. Lazarsfeld, P. F., Berelson, B., & Gaudet, H. (1944). The people’s choice: How the voter makes up his mind in a presidential election. Duell, Sloan and Pearce. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167. Ministero della Salute. (2020). https://www.salute.gov.it/portale/nuovocoronavirus/dettaglioNotizieNuovoCoronavirus.jsp?lingua=italiano&id=5242 Novella, G. (2016). L’opinione pubblica ai tempi del 2.0. Giglamesh Edizioni. Organizzazione Mondiale della Sanità. (2009). Pandemic influenza preparedness and response. Author.

ENDNOTES 1



4 5 6 2 3

TF-IDF is an information retrieval technique that weighs a term’s frequency (TF) and its inverse document frequency (IDF). It measures the importance of a word in a document. Terms with TF-IDF less than 0.20 are: Astrazeneca, doses, anticovid, covid, coronavirus, vaccine. President of Italian Counsil. European Medicines Agency. Special Commissioner of the Italian Government for the Covid Emergency. As written also in the paragraph on methods, the sample of 260 tweets was drawn from the main database, i.e., 20 tweets from each of the 13 account categories. The ideal solution was to select the two most “retweeted” tweets of the 10 users with the most followers. It is a very small share, in fact the analysis was done taking into account the class of the VA and not the individual user. Each analytical consideration on sentiment and opinion, although not generalizable to the entire corpus, was always related to the type of VA. In many cases users have tweeted only once about the vaccine, so the quota per capita of tweet users was in line with the average number of tweets produced.

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Chapter 38

Mining Flickr to Better Understand Tourist Behavior Maria Giovanna Brandano Gran Sasso Science Institute, Italy Ludovico Iovino Gran Sasso Science Institute, Italy Daniele Mantegazzi University of Groningen, The Netherlands

ABSTRACT The aim of this chapter is to present an automated instrument collecting the enormous amount of information available online allowing urban planners, public administrations, tourism services suppliers, and researchers to easily understand the spatial and temporal distribution of tourist behaviors towards tourist attractions in a specific area. Geo-located photos provided by Flickr are used to identify points of interest (POIs). The developed application has been tested with data automatically retrieved and collected in L’Aquila province (Italy) during the years 2005-2018. Given the richness of information, these data are able to show how POIs changed over time and how tourists reacted to the 2009 earthquake. Results demonstrate the importance of using analytics and big data in tourism research. Moreover, by using the province of L’Aquila as pilot study, it emerges that tourist behaviors change over time and space, varying among different typologies of tourists: residents, domestic, and international visitors.

INTRODUCTION The diffusion of social networks and the increasing number of users is generating a massive amount of data in every moment. This is particularly evident in tourism sector, where three different sources of data exist: data generated by 1) users (i.e., texts or photos), 2) devices (i.e., GPS or mobile) and 3) operations (i.e., telephone companies, online booking or web search). This volume of information is in

DOI: 10.4018/978-1-7998-8473-6.ch038

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 Mining Flickr to Better Understand Tourist Behavior

some cases freely available and is called “open data” or “big data”. In others, data can be sold by the owner with particular licenses of usage. Tourism is an important economic sector in many countries around the world, significantly contributing to their economic growth (Seghir et al. 2015; Brida et al. 2016; Ohlan 2017; Perles-Ribes et al. 2017). Recent estimates indicate that the total contribution of the travel and tourism sector to the global GDP in 2018 was 10.4% with a growth rate of 3.9%, outpacing the growth rate of the whole economy for the eighth consecutive year (World Travel and Tourism Council 2019). Moreover, according to the tourism-led growth hypothesis (Balaguer and Cantavella-Jordà 2002; Dritsakis 2004; Louca 2006; Nowak et al. 2007; Proença and Soukiazis 2008; Cortes-Jimenez and Pulina 2010; Seetanah 2011), tourism represents an important driver of economic growth (see Bimonte et al. 2012). More specifically, the tourism sector may stimulate economic activity in different ways. Tourism directly creates employment and tax revenues; it has direct, indirect and induced effects in other economic sectors; it boosts investments in infrastructure; and it improves the efficiency of local firms, thanks to economies of scale and increased competition (Schubert et al. 2011; Tugcu 2014; Shahzad et al. 2017). According to the World Tourism Organization (UNWTO), before the diffusion of the pandemic related to COVID-19, tourism demand has been growing overtime and it was forecast a positive trend also for next years. Latest data pre-COVID-19 described a sector including 1,461 million of international arrivals in 2019, with a growth rate around 4% with respect to the same period of 2018. More than half of worldwide arrivals refers to European countries, where arrivals continue to grow and record more than 742 million. Italy is the third European destination, after France and Spain (UNWTO 2020a) and is the first country for UNESCO World Heritage Sites (Bank of Italy 2018). However, an important feature of the tourism sector is that it is not evenly distributed, both from a spatial and temporal perspective (Butler 2001; Batista e Silva et al. 2018). Moreover, tourism can affect the destination by generating negative externalities (e.g., crime, congestion, overbuilding and degradation of nature, among others), which must be taken into consideration when tourism flows, and tourism behaviors are studied. Indeed, a recent strand of the literature analyses the case of overtourism as a phenomenon potentially affecting not only tourist cities, but also rural areas, island destinations or part of cities during certain events (Koens et al. 2018). Unfortunately, after the diffusion of the pandemic related to COVID-19 the tourism sector has been completely transformed. The UNWTO (2020b) declared tourism to be among the hardest hit sectors, emphasizing the high exposure of small and medium enterprises. International tourist arrivals fell by 74% in 2020 (UNWTO 2021), representing an unprecedented crisis. The magnitude of this figure is completely different compared to previous crisis. Indeed, the 2009 global economic crisis recorded a 4% drop of international tourist arrivals. Nevertheless, some positive signals are provided by domestic tourism that continues to grow in several large markets, such as China and Russia, where domestic air travel demand has mostly returned to pre-COVID-19 levels (UNWTO 2020b). In this scenario, in which UNWTO forecasts that it could take between two-and-a-half and four years for international tourism to return to 2019 levels, it is essential to better understand the economic impacts of tourism in a given area, and analyze tourist flows and tourist behaviors in that specific region, by studying detailed spatiotemporal data on tourism (Batista e Silva et al. 2018). Analyzing tourist behavior and movement patterns at destination is important to develop appropriate infrastructure, transport systems and tourist products, as well as implement better destination marketing strategies and improve the management of the social, environmental and cultural impacts of tourism (Lew and McKercher 2018; Shoval and Ahas 2016). This emerges to be crucial also with respect to the new trends of tourism. After the COVID-19 pandemic, the UNWTO Panel of Experts predicts a growing demand for open-air and 687

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nature-based tourism activities, with domestic tourism and “slow travel” experiences gaining increasing interest (UNWTO 2021). Yet, a large amount of information about tourist behaviors cannot be extracted from the official statistics, because these data are aggregated from both a spatial and temporal perspectives. In fact, in Italy data on tourism demand (arrivals and overnights) available from the official statistics are aggregated at the level of the entire province, at a monthly frequency. To fill this gap, a very recent strand of the economic literature started to analyze the large amount of information freely available thank to the increase in social network use. In particular, scholars are attempting to predict tourists’ attitudes and behaviors by analyzing the impact of comments, reviews, photos, and tweets shared on social networks (Pantano et al. 2018 and 2017; Giglio et al. 2019 and 2020). Indeed, the era of big data offers the possibility of managing, collecting, and analyzing massive amounts of data that can be used for improving services, automating processes, understanding human behaviors, and increasing revenues. Even though there is still no absolute definition of big data, there are seven important characteristics that can be helpful to identify what big data are (Kitchin 2013): Volume, Velocity, Variety, Exhaustive, High Resolution, Relational, and Flexible. Over the last few years, the volume of data has exploded (Barry 2012; Akerkar 2013; Bessis 2014): in 1999, 1 gigabyte was considered as big data; however, already in 2006 total data was estimated to be 160 exabytes, corresponding to a 1’000% increase in 7 years. Consequently, nowadays 1 gigabyte can no longer be considered as big data, and only data larger than 1 terabyte1 can be defined as big data. Although large amounts of data are produced by sensors and applications in multiple application domains, social networks are among the largest big data generators (Saleh et al. 2013). Moreover, the analysis of data retrieved from Location Based Social Networks has gained importance as a popular and promising method for interpreting geolocated data for the study of cities. Although these approaches present some limitations (Martì et al. 2019) - e.g., the lack of consistency in the provision of an acceptable amount of valid geocoded data they offer a wide number and variety of applications. More specifically, recent studies show how big data can be used for a better comprehension of tourism demand, tourist behavior and tourist satisfaction (Li et al. 2018). Hence, the aim of this chapter is to present a user-friendly instrument collecting this enormous amount of information allowing urban planners, public administrations, tourism services suppliers and researchers to easily understand the spatial and temporal distribution of tourist behaviors towards tourist attractions in a specific area. In this research, a pilot study is also proposed on how this free online open data repository can be useful to better analyze tourist behaviors towards tourist attractions in an Italian destination, i.e., the province of L’Aquila. Moreover, this analysis represents the first attempt to study tourist behaviors and seasonality in Italy with a monthly frequency using big data. Thanks to the proposed instrument, this approach can easily be replicated and scaled in other cities and regions. The chapter is structured as follows. Section 2 includes a critical analysis of the previous contributions to this topic. Section 3 provides a detailed description of the methodology. Section 4 describes the case study and data used while Section 5 examines results obtained. Finally, Section 6 concludes with some policy implications, limitations and further development of the analysis.

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THEORETICAL BACKGROUND Big Data in Tourism Research Previous studies have demonstrated that the information generated by other consumers is more trustworthy than the managers’ or vendors’ information (Tran et al. 2012; Li et al. 2013; Tsekouras 2017). This seems to be recognized in all sectors where the product quality can be expressed by a review, a ranking or a rating. In other words, the recommendation system, rather than other quality indicators, gives the reputation of a good or service. In tourism this process assumes a relevant importance because holiday as a whole is considered an experience good. Namely, a consumption good whose main features and quality cannot be observed in advance and may be assessed only after the purchase and the act of consumption (Candela and Figini 2012). Examples could be theater plays, restaurant meals or museum and city visits. In this context, websites like TripAdvisor, Yelp, Open Rice have transformed the way tourists take their decisions (Filieri 2016). Where to go, what to see and do on holiday, where to eat, and so on strictly depend on these online consumer reviews. Hence, as argued by Cvelbar et al. (2018), the choices of tourists are influenced, and to some extent managed, by other tourists, through the use of technology. Since in the tourism sector people usually leave digital traces in every moment of their travel, in the last years they have produced a large amount of data, which are often geo-located. As in other sectors, also in tourism the analysis of big data has been mainly used to study these digital traces (Aragona and Felaco 2018). In a recent literature review, Li et al. (2018) provided an extensive analysis of different types of big data related to tourism research. This first attempt to examine this emerging topic represents the signal that a new and growing interest is born in tourism research. From 2007, when 3 articles were published, a maximum peak is recorded in 2016, when 30 articles are found in the academic databases. It seems evident that the topic is in its early stage, but what is more interesting is the positive trend of the published articles number, which underestimates the total amount of academic research, since it excludes books, reports, research notes and other kinds of contributions. Among the most common advantages in using these data, two are the most relevant: 1) big data can overcome the limitations of sample size issues of surveys data (Yang et al. 2015) and 2) big data analytics can eliminate sample bias because they provide sufficient data (Li et al. 2017). In addition, according to Del Vecchio et al. (2018), big data have become the main driver for value creation of a tourist destination. Indeed, these can activate the following dimensions: 1) improvement of decision-making processes by using real time information; 2) enhancement and enrichment of tourists’ experiences by anticipating their needs; 3) development of new business models and new products and services by the connections with stakeholders; 4) interconnection with business ecosystem by the collaboration with the local community. Despite the copious advantages, the analysis of big data derived by social media in the tourism field is up to now unexplored and need a more in-depth investigation.

Online Photo Data Following the previous literature and the motivation behind it, this chapter focuses on the analysis of the contributions that used online photo data to better understand tourism behaviors. According to Giglio et al. (2020) the information voluntarily provided by users of social networks is named volunteered geographic information (VGI) and includes not only post and messages but also photos and short videos. Li et al. (2018) call this data user-generated content (UGC) and find that 47% of 689

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the published articles focused on this information: 26% on online textual data and 21% on online photo data, which is the focus of this study. As Li et al. (2018) stated, three main sources of online photos exist: Flickr, Instagram and Panoramio. Given the longer availability of data in terms of time span, Flickr still remains the most used source of big data in tourism research. In 2013, this online platform recorded more than eight billion photos from more than 87 million users and more than 3.5 million new images uploaded daily2. Flickr processes millions of photos and videos every day, with billions of photos already online. It serves billions of pageviews and more than 7 billion API requests monthly, for more than 100 million registered photographers (www.flickr.com). Users provide spatial and temporal information in the form of coordinates and interact with each other by commenting photos and becoming friends and followers. Thanks to the richness of the information included in this platform, in recent studies various authors exploited this information for different purposes and in different geographical areas. For instance, Girardin et al. (2008) analyze 4,280 photos collected from the popular photo-sharing web platform Flickr in the Italian Province of Florence over a period of two years (2005-2007). The authors discriminate between visitors and residents by considering the length of the presence of users in the studied area, with a threshold period of 30 days. The results allow the authors to identify the urban mobility within the city of Florence and the tourist POIs, however, the analysis does not consider the related temporal dimension. Kurashima et al. (2013) use Flickr geo-tagged photos to extract travel paths in the United States. Majid et al. (2013) explore photos from various cities in China to predict tourist behaviors in other cities and generate recommendations. Önder et al. (2014) use Flickr photos from 2007 to 2011 to assess the presence of tourists in Austria, showing that the method provides more reliable outcomes for cities than regions. In the same year, Lee et al. (2014) analyze POIs patterns derived by online photos in Queensland (Australia). Kádár (2014) correlates Flickr photos with statistical data in 16 European cities and, subsequently, three tourist-historic cities (Vienna, Prague and Budapest) are compared more deeply to find spatial patterns for the year 2011. Zhou et al. (2015) do the same, but in multiple cities in the United States from 2007 by using spatiotemporal information of images. Vu et al. (2015) describe tourist behaviors in Hong Kong using 29,443 photos collected from 2,100 visitors from 2011 to 2013. Sun et al. (2015) define the trajectories of tourists in Munich (Germany) by using the images taken during the year 2010 and 2011. Önder (2017) uses Flickr images to understand the behaviors of tourists who visited at least two different Austrian cities based on the geo-locations of their photos. The author finds different multi-destinations trips and different clusters including cities with specific characteristics. More recently, Vaziri et al. (2020) use Flickr to automatically identify the tourist attractions in three European cities (London, Paris and Rome) during the period 2015-2016. Subsequently, the authors compare the POIs founded in the first part of the analysis with the information provided by TripAdvisor. The main purpose, in this case, is to give an example of a generalizable technique that can be used in any other city. From this brief analysis of the previous works focusing on this topic, there exists a gap in the literature for the most famous European tourist destinations, with only few exceptions (Girardin et al. 2008, Kádár 2014, Vaziri et al. 2020). This is in part filled by the recent publication of two articles on the Italian case by Giglio et al. (2019, 2020). These works focus on photos collected on Flickr in six Italian tourist destinations: Rome, Milan, Venice, Florence, Naples and Palermo. The downloaded data include the following information: Location, Location ID, Latitude and Longitude, Username, User-Id, datatime, Link of image. User profiles are also downloaded, in order to investigate the nationality of users and if the place of origin could influence the choice of visited places. These data allow the authors to identify POIs in each city under analysis and both hotspots and less attractive areas. Indeed, thanks to the geographical coordinates of photos, the authors have identified the spatial distribution of pictures taken 690

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by users to investigate the attractiveness of each city. Unfortunately, the collected data are not able to really identify how POIs change over time, since they use a very limited time span from 2014 to 2016. Moreover, the analyzed cities are very famous cultural destinations in Italy, which do not represent the variety of tourism supply and tourist flows characterizing the whole Italy. For this reason, the aim of this chapter is to investigate POIs and tourist behaviors in a less touristic area, i.e., the province of L’Aquila, characterized by an increasing interest of tourists in the last years and by a transition process followed by the strong earthquake of 2009. This work contributes to the existing literature in many ways. First, it analyzes a very long-time span (2005-2018) rather than only few years or one year as in the majority of previous studies. Moreover, this analysis represents the first attempt to study tourist behaviors and seasonality in Italy with a monthly (rather than yearly) frequency using big data. Second, it investigates tourist behaviors at the provincial level rather than the urban level. In this respect, the present analysis represents an advantage, given that the official data in the peripheral areas are often unavailable. Finally, the origin of tourists is directly identified by using Flickr data and not by assuming a threshold based on the users’ length of stay as in Girardin et al. (2008) and in Kádár (2014), eliminating any bias or error in the empirical analysis.

METHODOLOGY Mining Flickr to Get More Detailed Data Following Giglio et al. (2019, 2020), the analysis focuses on data automatically collected from Flickr (photos) in a general architecture which can be exploited by urban planners, public administrations and tourism services suppliers to understand and guide tourism supply. Flickr is a popular photo sharing and hosting service with powerful features (https://www.flickr. com). It supports the creation of an active and engaged community where people share and explore each other’s photos. Compared to other social networks, such as Instagram, it allows any size or dimension of the uploaded photo. This is particularly relevant in the analysis of tourists’ behavior, since photography endorses good memories about travel destinations, as explained in the literature related to the Electronic Word of Mouth and the reputation of a tourist destination. More than 60% of users are men, and, on average, they are 35-39 years old. With respect to Instagram, for instance, Flickr users are less young, and there is a lower percentage of women. In such context, data obtained interacting with Flickr Application Programming Interfaces (API: https://www.flickr.com/services/api/) are significantly relevant. They include the following information: Location, Location ID, Latitude and Longitude, Username, User-Id, data-time, Link of image and then all exif data. User profiles are also part of the available information, in order to investigate whether the nationality of users and the place of origin could influence tourists’ behavior. These data allow one to identify: 1) POIs within the inspected geographical area, e.g., the province of L’Aquila that will be used as pilot study; 2) hotspots and less attractive areas within the analyzed region. Indeed, thanks to the geographical coordinates and the taken time of photos it is possible to identify the spatial distribution and the seasonality of pictures taken by users to investigate the attractiveness of the area and understand how to improve the tourism services in the most crowded areas. The general architecture proposed for this purpose is reported in Figure 1, where on the bottom the components are depicted and on top a mockup of the data visualization is hypothesized. The proposed platform stores the data obtained interacting 691

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with Flickr APIs with a cron-job in a storage layer. It is important to outline that retrieved data might be cached for improving performance of the system, but data analysis is completely dynamic since the inspected area is “zoomed” by the Visualization component, which requires data from the Data retrieval layer. This layer interacting with the API aggregates the obtained information in a standard format, which will be elaborated by the Data analysis layer and returned back to the Visualization. In the map visualization, for instance, the user can browse the geographical area available and then navigate to detailed view offering specific insights and data. For example, in the picture reported, a detailed view of the time-based distribution for two-selected POIs is visualized. This detailed view highlights how the Ovindoli municipality (in blue), which is a famous ski resort near the city of L’Aquila, is mostly photographed in winter. On the contrary, the National Park (in red) is mostly visited in summer and spring months. Figure 1. Proposed general architecture

CASE STUDY AND DATA Tourism in L’Aquila Province The context of the analysis is identified in the province of L’Aquila, in the central part of Italy. This tourist destination is not in the first places of the Italian ranking for number of tourists, however L’Aquila is recognized as a province characterized by a differentiated tourist supply. In particular, the presence of

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two national parks – Gran Sasso National Park and National Park of Abruzzo, Lazio and Molise – attracts every year a large number of natural and sport tourists. The province is also famous for its many medieval castles and fortresses located in unspoiled hill villages. The well-known villages of Rocca Calascio and Santo Stefano di Sessanio give an example of this kind of cultural and rural destinations. At the urban level, two main cities are the most attractive places: L’Aquila and Sulmona. According to the Italian national statistical institute, in 2018 the total number of accommodations in the province is 852, which includes 23,235 total beds. Tourist arrivals are 388,955 and overnight stays 919,851. This means that - on average - every tourist stays in the province about two days. The most visited cities are: Roccaraso, L’Aquila and Pescasseroli. From 2000 to 2018 the total number of arrivals seems to be stable around 350,000 (see Figure 2). The maximum peak is recorded in 2008, before the earthquake. On the contrary, overnight stays follow in the same period a negative trend, even more evident after 2009. Hence, the main issue of this tourist destination is not its attractiveness, but rather its capacity to increase the length of stay of tourists. However, it is also important to underline that holiday patterns in the entire world are changing: on average, tourists take more holidays throughout the year but each holiday is characterized by shorter lengths of stay (Candela and Figini, 2012). Figure 2. Time series of arrivals and overnight stays in the province of L’Aquila (Years: 2000–2018)

Data Collection Data were obtained by interacting with Flickr APIs, allowing collecting information of 66,875 images shared by 3,853 users between January 2005 and December 2018 in the Italian province of L’Aquila. More specifically, in a first step, the interaction with Flickr APIs allowed collecting the following metadata for each of these pictures: Picture ID, User ID, Date-time, Tags, Latitude, Longitude, and Link of image. In a second step, information about user profiles was also obtained from Flickr. In particular, the dataset considered in this study also contains the following personal information for those users who allowed making them publicly available: User ID, User Alias and User Location. Overall, 39,078 of the 66,875 pictures could be associated with information related to the location of the user. The availability of this information allowed distinguishing among users living in the province of L’Aquila, who can be

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referred to as residents; those living in Italy, but not in the province of L’Aquila, who can be assumed to be domestic tourists; and those living abroad, who can be referred to as international tourists. In the 14 years considered in this analysis, 57 users were living in the province of L’Aquila and shared 10,926 pictures, with an average of 192 pictures per user. In the same period, 1,280 users came from the rest of Italy and shared 23,315 images on Flickr, with an average of 18 pictures shared by each user. Finally, 251 users were living abroad and shared 4,837 pictures, with an average of 19 images shared by each user. Figure 3 shows the yearly evolution of the size of different groups of users who have taken pictures in the province of L’Aquila between 2005 and 2018. As clearly exposed in the graph with the orange line, between 2005 and 2011 there is a general increase in the number of users taking and publishing pictures in the province of L’Aquila every year. This growth is followed by a negative trend between 2011 and 2018. This general evolution is characterizing all the groups of users, in particular those living in the rest of Italy (red line) and those who have not provided any information about their location (green line). Figure 3. Yearly evolution of the number of users sharing on Flickr pictures taken in the province of L’Aquila, by user location from 2005 to 2018

As shown in Figure 4, also the overall number of pictures taken in the province of L’Aquila and posted on Flickr is characterized by a similar trend (orange line). In fact, there is a general increase in the number of pictures taken in the province of L’Aquila and published every year between 2005 and 2012, followed by a general decrease between 2012 and 2018. However, analyzing the evolution of the number of pictures published by the different groups of users, only those who have not provided any information about their location (green line) are characterized by a similar pattern. The yearly number of pictures published by users living in the province of L’Aquila (blue line) records a substantial increase

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between 2009 and 2012, i.e. in the three years following the earthquake, and then remained relatively stable. At the same time, the yearly number of pictures taken in the province of L’Aquila and posted on Flickr by users living in the rest of Italy (red line) and those living abroad (purple line) show fluctuating trends. Interestingly, the first peak (which is the most important for the users living abroad) is in the year of the earthquake (2009). Figure 4. Yearly evolution of the number of pictures taken in the province of L’Aquila and shared on Flickr, by user location from 2005 to 2018

This rich dataset is further analyzed in the next section, in order to explore the spatial and temporal distribution of the pictures shared on Flickr by the different types of users. In particular, the analysis investigates the monthly frequency and the geographical location of pictures taken in the province of L’Aquila and shared on Flickr, distinguishing by user location.

RESULTS This section exposes findings of the analysis carried out on the richness of information available in order to investigate the spatial and temporal distribution of the pictures shared on Flickr by the different types of users. Given the geographical and temporal uneven distribution nature of the tourism sector (Butler, 2001; Batista e Silva et al., 2018), the results of this analysis can be particularly interesting for urban planners, public administrations, tourism services suppliers and researchers to easily understand the particular characteristics of tourist behaviors towards tourist attractions in a specific area. Moreover, the results obtained can help both stakeholders and administrators to increase the supply of tourist services in an area that is not completely developed as tourist destination, but that has room for improvement.

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Temporal Distribution In this section the analysis specifically focuses its attention on the temporal distribution of the number of pictures taken in the province of L’Aquila and shared on Flickr between 2005 and 2018, distinguishing by group of users, according to their origin. Figure 5 shows the monthly number of pictures taken in the province of L’Aquila by residents and shared on Flickr. Figure 5. Monthly number of pictures taken in the province of L’Aquila and shared on Flickr by user living in the province of L’Aquila from 2005 to 2018

The first years (2005 – 2009) are characterized by low numbers of pictures shared every month on Flickr and there is little variation through time. After 2009 the number of images taken in the province of L’Aquila and shared on Flickr by users living in this province increases and develops a pattern, which tends to repeat itself on a yearly basis. In particular, there seem to be two main periods of the year in which residents of the province of L’Aquila become more active in taking and sharing pictures in this area. The first peak is between April and June, while the second intensively active period is between August and October, and it is usually characterized by higher numbers of pictures shared on Flickr than those shared during the first period. An exception to this general trend is represented by the year 2015, where the only intensive period is between June and August. Figure 6 reports the monthly evolution of the number of pictures taken in the province of L’Aquila and shared on Flickr by users living in Italy, but not in the province of L’Aquila.

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Figure 6. Monthly number of pictures taken in the province of L’Aquila and shared on Flickr by user living in the rest of Italy from 2005 to 2018

From 2005 to 2012 there is a similar trend repeating itself every year, characterized by a single outstanding peak of high number of pictures taken in the province of L’Aquila and shared on Flickr in August. This pattern is also clearly visible in the years 2016 and 2017. In 2013 and 2014 the evolution of the number of pictures taken in the province of L’Aquila and shared on Flickr by users living in the rest of Italy is very similar to the general pattern identified for the residents in the province of L’Aquila, with two peaks, the first in March and the second in August. Finally, in 2015 and 2018 are characterized by low variation in the number of images taken in the province of L’Aquila and shared on Flickr by users living in the rest of Italy. Figure 7 shows the monthly number of pictures taken in the province of L’Aquila and shared on Flickr by users not living in Italy. In general, there is a similar pattern repeating itself every year, characterized by higher number of pictures shared on Flickr between September and November. In some cases (e.g., in 2011 and 2016), there is another intensive period between April and June. Interestingly, in October 2009 (year of the earthquake) there has been an outstanding number of pictures taken in the province of L’Aquila and shared on Flickr by international users. To summarize, in the above analysis it is important to notice that domestic and international tourists have a different seasonality, with domestic tourists showing a double seasonality. This seasonal profile, where a destination adds to the peak season (summer) a shoulder season (Easter), is typical of beachoriented destinations in South Italy or South Spain (Candela and Figini, 2012). However, in the case under analysis, this bi-seasonal pattern is not due to the local climate, rather, it might be caused by the presence of “institutionalized” and/or religious holidays (such as Easter) and the proximity with famous

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Figure 7. Monthly number of pictures taken in the province of L’Aquila and shared on Flickr by user living in the rest of the world from 2005 to 2018

tourist destinations like Rome. The ability to distinguish and analyze the specific local seasonality of different types of tourists is becoming even more important due to the new trends in tourism recorded after the outbreak of the COVID-19 pandemic. Indeed, in the next months it will be more likely that tourists visit less famous destinations because these are perceived as more secure and, at the same time, less frequented.

Spatial Distribution Analogously to the previous section, the analysis proceeds focusing on the spatial distribution of the pictures about the province of L’Aquila shared on Flickr between 2005 and 2018, distinguishing by group of users, according to their origin. The boundaries of the province are marked with a solid black line, while the location of the pictures is marked in red. Additionally, locations with high density of pictures are marked with more intensity and, on the contrary, location with low density of pictures are marked with more transparency points. Figure 8 shows the yearly location of pictures taken in the province of L’Aquila and shared on Flickr by residents. As it clearly emerges from the maps, residents of the province of L’Aquila mainly share pictures of the western area of the province, in particular in the regions close to the city of L’Aquila (located in the north-west area of the province) and the city of Avezzano (located in the central-west part of the province). The spatial extension of the location of the pictures shared on Flickr by residents

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Figure 8. Yearly spatial distribution of pictures taken in the province of L’Aquila and shared on Flickr by user living in the province of L’Aquila from 2005 to 2018

is visibly larger in the area surrounding the city of Avezzano and relatively smaller in the region close to the city of L’Aquila, in particular after the year 2009. Figure 9 reports the yearly location of the pictures taken in the province of L’Aquila and shared on Flickr by users living in Italy, but not in the province of L’Aquila. As it emerges from the map, these pictures are spread across the entire province, particularly in natural areas and in relevant tourist destinations. In fact, the majority of the pictures shared on Flickr by users living in the rest of Italy have been predominantly taken in the Gran Sasso and Monti della Laga National Park (located in the north part of the Province), in the Abruzzo National Park (located in the south-east part of the province), in the Sirente-Velino Regional Park (located in the central area of the province), in the medieval village of Santo Stefano di Sessanio and in the mountaintop fortress Rocca Calascio (both located in the north-east part of the region). Moreover, it also emerges that there is some activity in the city of L’Aquila (located in the north-west area of the province), in the city of Sulmona (located in the central-east part of the province), and, to a lesser extent, in the city of Avezzano (located in the central-west part of the province). Finally, Figure 10 shows the yearly location of pictures taken in the province of L’Aquila and shared on Flickr by users not living in Italy. As it emerges from the maps, users living abroad mainly share pictures of the north-east area of the province. In particular, the pictures shared on Flickr by international users are predominantly located close to the city of L’Aquila (located in the north-west area of the province),

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Figure 9. Yearly spatial distribution of pictures taken in the province of L’Aquila and shared on Flickr by user living in the rest of Italy from 2005 to 2018

the medieval village of Santo Stefano di Sessanio and in the mountaintop fortress Rocca Calascio (both located in the north-east part of the region), and in the regions surrounding the city of Sulmona (located in the central-east part of the province). The results clearly show the importance of understanding the temporal and spatial characteristics of tourist behaviors towards tourist attractions in a specific area, and how using Big Data can help in achieving this strategic goal. In fact, the findings presented in this chapter indicate that residents, domestic and international tourists are characterized by different behaviors, with different geographical and spatial preferences. More specifically, residents tend to be more active in two periods of the year, i.e., in spring and in late summer. At the same time, domestic tourists record the peak of their presence in the province of L’Aquila during the month of August, while international tourists in autumn. Moreover, this analysis represents the first attempt to study tourist behaviors and seasonality in Italy with a monthly frequency using big data. Additionally, also the spatial distribution of pictures taken by the various groups of users clearly shows different patterns. Residents are mainly active in the west part of the province, in the areas surrounding the cities of L’Aquila and Avezzano. Domestic tourists are mainly interested in the national and regional parks of the province, while international tourists are mainly concentrated in the north-east part of the province, in the areas surrounding the cities of L’Aquila and Sulmona, in the medieval village of Santo

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Figure 10. Yearly spatial distribution of pictures taken in the province of L’Aquila and shared on Flickr by user living in the rest of the world from 2005 to 2018

Stefano di Sessanio and in the mountaintop fortress Rocca Calascio. These results seem to be in line with those highlighted by the panel of UNWTO experts, indicating that, in the near future, the demand for open-air and nature-based tourism activities will grow, and domestic tourism and slow travel experiences will gain large interest. As demonstrated by a recent study by Walden-Schreiner et al. (2018), the use of big data is particularly important when official statistics are limited, such as in mountain protected areas. By exploring geotagged photos on Flickr, they analyzed the Aconcagua Provincial Park in Argentina and the Kosciuszko National Park in Australia. As in our analysis, their results show the usefulness of these data by providing detailed spatial and temporal information for site-specific and park-level management of visitors and potential impacts in conservation areas.

CONCLUSION AND POLICY IMPLICATIONS An important feature of the tourism sector is that it is not evenly distributed, both from a spatial and temporal perspective (Butler 2001; Batista e Silva et al. 2018). Hence it is essential to understand and analyze local tourist flows and tourist behaviors by studying detailed spatiotemporal data on tourism (Batista e Silva et al. 2018). Yet, a large amount of information about tourist behaviors cannot be extracted from

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the official statistics, because these data are aggregated from both a spatial and temporal perspectives. To fill this gap, this chapter contributes to a very recent strand of the economic literature analyzing the large amount of information freely available online thank to the increase use of social networks. More specifically, this chapter focuses on the use of an instrument able to collect enormous amount of information related to photos and users of Flickr, to understand in real time, which are the tourist patterns in a specific area. The analysis focuses its attention on a case study – the province of L’Aquila – representing an example of tourist destination in development after a natural shock. The resilience of tourism, in particular of international tourism, is a well-recognized characteristic. However, after the earthquake of 2009, official data on tourist overnight stays in L’Aquila showed a negative trend that is not recorded into the big data obtained from Flickr. The use of this source of data permitted to better understand the tourist behaviors by differentiating between domestic and international demand. Domestic tourists seem to be more interested on natural tourism, while international ones are more attracted by cultural and rural destinations, but also by urban tourism. Moreover, the seasonality of these two typologies of tourist is different. Autumns is the favorite season for international tourists, while domestic tourists show a double seasonality, with a main peak in summer and a shoulder peak around Easter. All this information is not clear by only analyzing official and aggregated data. Hence, this approach allows better understanding and distinguishing different tourist behaviors. The proposed approach is particularly useful thanks to its user-friendly results given by the visualizer component, and the possibility to easily replicate and scale it in other cities and regions. Moreover, the entire architecture allows extensions with other social networks data sources, opening a possible scenario of a refined instrument for tourism behavior. Moreover, the results emerging from this approach can be particularly interesting for urban planners, public administrations, tourism services suppliers and researchers to easily understand the particular characteristics of tourist behaviors towards tourist attractions in a specific area and can help improving the supply of tourism services according to the real POIs, the seasonality, the origin or other characteristics of tourists. This is even more important in the current situation, where the outbreak of the COVID-19 pandemic is radically changing the trends and the behaviors in the tourism sector, and a better understanding of these is needed. On the one hand, the main strength of this chapter is the use of non-official statistics that are not available in real time. The importance of using these new data has been broadly recognized due to the potential to improve existing statistics (Heerschap et al. 2014; Walden-Schreiner et al. 2018; Monaco 2020). Secondly, this paper proposes a new approach by using monthly (rather than yearly) big data to study tourist behaviors and seasonality. On the other hand, however, it is important to explicitly recognize some limitations that the approach proposed in this chapter presents. The main limitation is related to the issue of the completeness of the digital data. In particular, the literature indicates that inequalities related to Internet access, skills, uses, and outcomes tend to mirror existing social inequalities in terms of socio-economic status, education, gender, age, geographic location, employment status and race (Robinson et al. 2015). Hence, the presence of a digital divide between those who use Flickr and those who do not use Flickr might reflect other inequalities and the data obtained from Flickr might miss part of the information. This is particularly relevant in Italy, where the digital divide is elevated. Indeed, recent data recorded a 68% coverage of the 30 Mbit/s in download broadband, and this percentage decreases to 30% if we consider the 100Mbit/s download broadband (AGCOM). Additionally, there is still a 5% of people with no personal connection at home. Hence, data obtained from Flickr could be affected by this issue and could describe only one face of the coin. For this reason, further steps of the present analysis

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could go into the direction of a comparison between data provided by other social platforms and cross some statistics with official indicators, when available.

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Zhou, X., Xu, C., & Kimmons, B. (2015). Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform. Computers, Environment and Urban Systems, 54, 144–153. doi:10.1016/j.compenvurbsys.2015.07.006

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Data extracted from the post “How Big is Big Data?” -- https://towardsdatascience.com “The man behind Flickr on making the service ‘awesome again’”. The Verge. March 20, 2013. Retrieved February 12, 2021.

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Chapter 39

I Correct or Canceling You: Political Correctness and Cancel Culture on Social Media – The Case of Twitter Communication in Italy Cristiano Felaco University of Naples Federico II, Italy Jacopo Nocerino University of Naples Federico II, Italy Jessica Parola University of Naples Federico II, Italy Roberta Tofani University of Naples Federico II, Italy

ABSTRACT This contribution studies the debated terms “politically correct” and “cancel culture” on Twitter and in particular investigates the meaning that people give when they label something or someone as politically correct or indicate a case of cancel culture in the Italian context, where they are not yet widespread as they are in the USA and Britain. A textual analysis of a corpus of tweets selected through a set of hashtags was carried out to identify thematic clusters to understand features and meanings given to these expressions, along with their ways of using in the various situations and contexts. The main results show different meanings of the term, in the negative sense as a limitation of freedom of speech, and in a positive sense as the exclusion of some terms that may offend some people or groups. In this case, the meaning of a word is relative and depends on the situation and context in which it is used. Furthermore, the recourse in the discourses of cancel culture is only rhetorical; there are no actions of cancellation or boycott of someone or something.

DOI: 10.4018/978-1-7998-8473-6.ch039

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 I Correct or Canceling You

INTRODUCTION Politically correct1 (PC) is a term discussed, criticized, and in some cases satirized by commentators from across the political spectrum. First appeared in Marxist-Leninist vocabulary following the Russian Revolution of 1917, it became fully part of the politics, before being used wittily by liberal politicians to refer to the extremism of some left-wing issues, then spread in the civil rights movements born in the American colleges in the second half of the twentieth century. Today, PC is used to refer to language that seems intended to avoid offense to people, especially concerning race, gender, culture, and sexual orientation (Roper, 2020). The act of ensuring social justice is not always limited to language but often leads to a form of activism to ostracize people or to remove them from prominent positions on account of an ideological breach or for violating social norms (Norris 2020). Cancel culture, as this phenomenon is better known, has a long tradition dating back to the 1950s as a strategy to boycott influential figures from black culture civil rights movements, and it takes place on social media. The lack of a shared vision about the PC and cancel culture, often also seen as overlapped and interchangeable terms (Ben-Porath, 2017), make them highly contested (Granath & Ullén, 2019; Bouvier & Machin, 2021). This condition calls for works that shed deeper light on their meanings and features, along with the definition of contexts where they occur. In this light, the present study contributes to enhancing the understanding of the meaning that people give when they label something or someone as politically correct or report/indicate a case of cancel culture through an analysis of their conversations on social media. To accomplish this goal, a dataset of Twitter conversations featuring the phrases “politically correct” and “cancel culture” was collected; after, a textual analysis of the tweets was performed in order to identify thematic clusters that allowed better understanding in which contexts these terms was used. Social media represent in fact the privileged space where these phenomena take place (Bouvier & Machin, 2021), in particular, Twitter plays a positive role in social justice campaigns (Bouvier, 2020). This is an exploratory purpose but became more relevant considering that the debate in Italy is recently ongoing: if, on the one hand, the use of political correctness and cancel culture is growing, especially in the most recent news events, there have not yet been cases of cultural cancellation as well as it happened in the United States. The debate about this matter in Italy, in fact, only concerned the online disapproval for the “black face” of the TV show “Your Face Sounds Familiar” (Tale e quale show) but did not affect it that much, and, during the #MeToo campaign, the only Italian case more similar to the American cancel culture examples is that of the director Fausto Brizzi, but there was no cancellation because he started working again in the show business. In the Italian case, the opposition to “political correctness” is configured as several isolated episodes, mostly produced by single users whenever the debate is revived, rather than as a real cultural or political movement (Capozzi, 2018). This paper is structured as follows: section two illustrates the origin and the framework of politically correct and cancel culture; section three describes the method adopted, while section four shows the results of the analysis. The last section concludes with some remarks about the undertaken work and future perspectives.

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POLITICAL CORRECTNESS AND CANCEL CULTURE ON SOCIAL MEDIA Political correctness is, in general, the rejection of language and behavior that could be offensive to others, especially relating to sex and race, suggesting for instance the use of “gay” instead of “sodomite” or “faggot”, “African Americans” or “Black people” instead of “nigger”. The history of political correctness is complex. This expression had neither a linear nor continuous development, it picked up steam around 1990, peaks between 1991 and 1995, and gradually its diffusion reduced over time (Lakoff, 2000). Nevertheless, PC never is over or come back, remains very much a contested issue (Hartman, 2015). Also from a semantic point of view (Gallie, 1956; Connolly, 1993) “an agreed, clear, literal meaning [of PC] in the way that grammatical correctness or political corruption do” is lacked (Hughes, 2010:17). The term “political correctness” is usually harked back to the second half of the 20th century in the Communist terminology used by the American New Left as a policy concept denoting the orthodox party line of Chinese Communism as enunciated by Mao Tse-Tung in the 1930s, and then used sporadically by the regime press to indicate opinions aligned with their respective dictates (Perry, 1992). Albeit its diffusion in the social and political associations, the politically correct was not born as an ideological campaign, but as a pragmatic remedy for unsustainable situations following episodes of racism and intolerance on American campuses through by writing “speech codes”, i.e. regulations aimed at sanctioning those who did not conform to the linguistic norm imposed by the academic world, a norm that it condemned racism, sexism, homophobia, and any discriminatory expressions. The debate on political correctness involves not only race, class, gender, sexual orientation issues, but also the themes like multiculturalism, disability, environment, culture, and animal rights (Johnson & Suhr, 2003; Hughes, 2010). At the same time, the “sensitivity training” programs run to these universities were also seen by some groups (for example, the National Association of Scholars) as threats to academic freedom, a form of orthodoxy disguised as tolerance. The notion of PC emerged as a contested issue regards the polarized political discourse in the LeftRight, it is especially evident in the discourses surrounding social media movements for social justice. The fact that the discourse around PC takes place mainly on social media implies a modification of the social practices that involve avoiding or policing behaviour – usually speech – that is seen as derogating people in subordinated social groups (Aly & Simpson, 2019). Acting on social media would produce a form of social desirability in part influenced by politically correct itself: indeed, politically correct speeches are also used to perform considerate and respectful personae, to project a more desirable state of discourse (Wikström, 2016). This creates a sort of expectation of people behaving according to the social norms of the online media and its participant. In this view, what is politically correct to say or do, might not necessarily be true, rather could function as a kind of self-censorship given that people could hide their thoughts or reveal their real orientation (Loury, 1994; Herzogenrath-Amelung 2016). Generally, different positions concerning the PC debate may be identified (Granath & Ullén, 2019). From the so-called “protesters” who charge PC with stifle public debate and limit free speech (D’Souza 1991; Johnson & Suhr, 2003), to the “deniers” who question the very existence of political correctness, just seen as a label systematically deployed by those on the Right-wing forces to discredits views challenging the status quo and the authority of the Left (Wilson 1995), and then a tool for concealing the truth (O’Neill, 2011). Conversely, other groups defend the assumptions underlying the PC (the “defenders”), seen as a set of positive values, or a consciously devised “way of talking about taboo topics” (Allan and Burridge 2006: 111), or a reasonable discursive practice (Cameron 1995; Lakoff 2000; Halmari 2011; 710

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Curzan 2014). Along with these positions, some insist that in addition to the study of the phenomenon in general (“analyst”), more attention should also be paid to the meaning given to it and which phrases are used in its discourse (Johnson and Suhr 2003; Toolan 2003). Despite PC being conceived as a myth or reality (Wilson, 1995), or a “spurious construct” (Fairclough, 2003), yet all these positions agree that it particularly affects public debate (Granath & Ullén, 2019). The recognition of minorities in general and greater social justice is also the purpose of some movements that, instead of mainly claim a more respectful use of language, support radical actions, such as the boycott of someone or something, or the threat of “cancel” positions and behaviors considered not in line with the dominant culture. Better known as “cancel culture”, it properly refers to public shaming initiated on social media to deprive someone of their usual clout or attention to make public discourse more diffused and less monopolized by those in positions of privilege (Clark, 2020). Specifically, this action born in queer communities of color, in particular on the Black Twitter - the meta-network of culturally connected communities on the microblogging site (Clark, 2015) - made the language of being “canceled” into an internet meme (Shifman, 2013). Cancel culture is not new while related to call-out culture, doxing, and parallel to deplatforming2 (Saint-Louis, 2021). The practice of cancellation can be more properly thought of as a form of political activism analogous to the tactic of using online consumer review platforms or of consumer-boycotts withdrawing support for perceived unethical brands and corporations (Lightfoot, 2019): in this sense the action of canceling aims to shame people to exert penalties, such as limiting their access to public platforms or damaging reputations (Norris, 2020). While online consumer review platforms can be used to propagate false information (Luca & Zervas, 2016), or boycott an organisation or an individual, however they seek to inform other consumers and not push sanctioning organisations to cancel other organisations and individuals (Saint-Louis, 2021). In literature, a shared agreement on what cancel culture is, instead more debated are its purposes. Some agree that cancel culture is a way of achieving social justice for those victims unable to get legal redress or public apology, as in the famous case of MeToo movement (Norris, 2020). Conversely, cancel culture, like political correctness, has also been framed as a form of intolerance against opposing views. Being designed for the digital age in the midst of hypersociality, the pervasiveness of the culture of cancellation does not enable debate openly, it is even seen as form of critique that is destructive (Velasco, 2020). Others show that in some cases the brutality of the campaigns cannot be in proportion to the original transgression (Kirk, 2018), or note that these campaigns could mainly be driven by the pleasure brought to those tweeting to work together, fight for justice (Bérubé, 2018). Dealing with social injustice may not be the real purpose of a cancel campaign, but just representing an opportunity to show to be morally good (Ditum, 2014). Or even, for many critics of cancel culture who sit to the political right, cancel culture would overlap to PC describing a kind of political correctness seen as a dangerous ‘Cultural Marxism’ (Furedi, 2020). Beyond the differences of what cancel culture is or it involved, the common element required for cancel culture to be enacted is for there to be something offensive and therefore cancellable: i.e. typified by racist, sexist, or homophobic remarks. Fundamentally the subject of cancel-culture must be accessible in some way through online media or not and even if one is not active on social media, their actions could be captured, uploaded, and thus archived, at any moment. Then it must be perceived as offensive by a large enough social group and garner enough support (in terms of more shares, reshares, angry faces, dislikes/ likes) from their intended audience to lead to the loss of one’s positions in society, a role, a job, etc (Hooks, 2020). 711

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In this light, it is inevitably necessary to take into account the resonance that social media give to the matter of political correctness and cancel culture because of the easiness with which content and in particular opinions towards problematic acts circulate rapidly on a large scale. The act of canceling often involves scouring through feeds, articles, interviews, videos, just think of BlackLivesMatters, MeToo, and the LGBTQ+ movements which use different tactics on social media to carry out a cancel culture, such as dislike, unfollow, up to the removal of the profile. Probably the most glaring examples of it are the TV show “The 100” where, following the death of an openly homosexual character, the showrunner of the series Jason Rothenberg, lost 14,000 Twitter followers within 24 hours; and the #MeToo movement has brought to public attention several testimonies of sexual and gender-based violence, which has led in some cases to the removal of the profile of some important characters. Specifically, Twitter is the most used platform for denouncing the use of politically incorrect expressions and enacting cancel culture (Bouvier & Machin, 2021). Thanks to its feeds which can take a highly insular and nodal form, the users tend to use it to bring attention to various issues, ideologies, or acts (KhosraviNik, 2017). This, on the one hand, allows users to gather in affective communities driven by a common ideology, and, on the other hand, leads to the creation of tweets driven by the need to capture attention, likes, and retweets, which can create a push for those that provoke high emotion, polarity, sarcasm and humor (Breazu & Machin, 2019). Moreover, many studies highlight the ability of Twitter to give voices to marginalized parts of civil society, allowing them to unite, share ideas, and mobilize for a common cause (Castells, 2015). Twitter, therefore, becomes a place in which to expose social injustices and bring them into the wider public and hold accountable those who discriminate or exclude, leading to challenges of power relations (Bouvier & Cheng, 2019). Such wide exposure is enhanced by the fact that people enjoy coming together against a perpetrator because mobilizing can bring group members closer together; and, also, searching for the misdeeds of others can increase social status (Henderson, 2019). This study contributes to make clearer the meanings of the contested expressions political correctness and cancel culture, identifying their borders and features in a context where they have not yet fully spread as they did in the USA or North Europe. To this end, the analysis of the Twitter communication can help to figure out how the politically correct and cancel culture in the Italian context is conceived, and then understand which events, speeches, actions, and so on, users refer to when they talk about these phenomena.

METHOD AND PROCEDURES The research was conducted by using Twitter conversations. First of all, only tweets were used, this is because the interest was not so much to estimate the incidence of the PC and cancel culture on Twitter conversations but to identify how users define these phenomena. Moreover, given the purpose of this study, only tweets in the Italian language were selected. Tweets were collected in the period between 3rd and 13th May 2021. The particular period was chosen closely with specific events that have given an even greater resonance to PC and cancel culture in the public debate. The debate already present has intensified, with the approval of the Zan law proposal, which involves measures to prevent and tackle discrimination and violence concerning sex, gender, sexual orientation, gender identity, and disability. Politically correct has placed itself at the center of a political dispute over the law, but has met with great interest from public opinion. This fundamental case was the trigger for three other events, which made the debate even more heated: the speech of the singer 712

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Fedez at the Labor Day of May 1st in Rome, where he denounced an attempt to censor his speech, which openly sided against some exponents of the Italian right who were against the aforementioned law; the news concerning the non-consensuality of the prince’s kiss to Snow White in the homonymous fairy tale; and the monologue of the comedians Pio and Amedeo on the use of some racist or homophobic terms in different situations of the everyday life. Regarding the data collection, tweets were gathered by using the TwitteR package of Rstudio and the Twitter API to scrape all tweets containing a selection of specific hashtags. The use of hashtags allows understanding how users describe their discourses on Twitter. Indeed, a hashtag indicates a tag that sets up an attributive relationship between the tweet as a tagged token and the tag as its type (Zappavigna, 2011). This kind of relationship assumes that other users will use this tag as a keyword for a tweet on this topic. The selection of hashtags involved two stages. In the first one, hashtags that more directly recall the phenomena studied were included: “politically correct”, “political correctness” (both in the English and in the Italian language), “cancel culture”. In a second step, an analysis of hashtags occurrences and co-occurrences was carried out, and those that more frequently occurred together were used in order to better detect main topics and events that affected the debate (cancel culture and Snow White; politically correct and pioeamedeo; dictatorship and words; dictatorship and politically correct; censorship and words; politically correct, freedom and expression; left and politically correct; ddlzan and words; ddlzan and politically correct; ddlzan and polically correct)3. The final corpus contained 4860 tweets. The aim was not to construct a representative corpus of the linguistic activity on Twitter on PC, but instead to conduct a case study to afford a study on meaning-making in a specific context and time on Twitter. A textual analysis of the tweets was performed by using Rstudio. We have previously handled messages by customizing the dictionary through two phases: the lemmatization and disambiguation of words with the same graphic form but different meanings, and the creation of uniform strings. Then, a lexical correspondence analysis was performed on the corpus first, in order to highlight the latent structure underlying the various Tweets examined by reducing the dimensionality of the space of representation of the linguistic variables; successively, a hierarchical cluster analysis was carried out to identify positions and opinions of Twitter users regarding the politically correct and cancel culture, along with the understanding of relationships between the meanings and the situations in which these terms are used.

EMERGING DIMENSIONS OF MEANING The WordCloud4 gives preliminary information by showing the most occurring words that most characterize the conversations about the politically correct and cancel culture on Twitter5 (fig. 1). The largest words placed in the middle of the WordCloud, “freedom” and “expression”, are pinned as the most occurring words, with frequencies of 2056 and 1865 respectively, that form about 38% of the corpus. Politically correct is linked to the freedom of expression, the term “free-speech6” is used 37 times. Also “opinion” is one of the most recurrent words, right after “freedom” and “expression” with a frequency of 538. The issue of the freedom of expression is enhanced by the presence of words such as “limit7” (192), “censorship” (189), as well as “delete” (56), which underline the limitation in usage of certain words and the act of canceling people or events (3% of the tweets). Some words identify the phenomenon from a legal and political point of view in the top frequencies, among these, “laws” and “rights” present the highest frequency (559 and 556, equal to 4% of the corpus); moreover, “democracy”, “art”, “constitution”, “pluralism” and “civil” highlight a demand for civil rights 713

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Figure 1. Word Cloud

that also brings with it the words “offence” (84) and “deny” (31). Along with this, other words are more strictly connected to the political sphere: there are words like “left8” (160), “right9” (153), “politics” (97), which can be considered the main agent of the discussion around political correctness in “Italy” (254). While some words (1% tweets) refer more directly the right and discrimination matter: “offence” (118), “discrim10” (76), “violence” (71), “hate” (56), “insult” (56). The words “homophobia” (66), “homosexuals” (35), “LGBT” (35), and far below ‘minorities’ (15), could be identified as the subjects of discrimination and demand for civil rights. In the figure below, words linked to current news are presented. One of these is the story of Snow White: one could notice the words “kiss” (229) and “consensual” (158), and again the words “Disney” and “tales”. The other topical news item is the one referring to the Pio and Amedeo affair: we can notice from the WordCloud the words “laugh” (50) and “satire” (38). And the last one refers to the case of Fedez, with “fedez” (143) visible in the WordCloud and “rai” (25) much less frequent. Ultimately, the news concerning Gervasoni regarding the insults made to President Mattarella is also discussed. In addition to the highlighted topics, some words account for the mood of the discussion: ‘fuck’ (99), ‘bothered’ (76), ‘idiots’ (40), ‘fear’ (41), ‘absurd’ (35), ‘ridiculous’ (33).

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Then, a lexical correspondence analysis was carried out on the tweets corpus in order to get a representation of the relationships between the words and to identify the main dimensions of meaning. Figure 2. Correspondence analysis

Looking at the axes, it is possible to identify many areas that reflect the debate upon politically correct that emerged in the Twitter conversations. On the left-hand side, opinions on more strictly political issues, especially on the Zan bill, are placed; while, on the right-hand side, opinions arising from news events that have sparked off the debate are presented. The first axis shows two different visions of freedom of expression. On the left side of it emerge the theme of freedom of expression as respect for others (expression, freedom, right, law, etc.): the discussion is around the defense and importance of the Zan bill for civil rights, showing themselves “in favor” of politically correct as a tool to restore equal dignity to all components of civil society. In particular, some words underline the consequences of this bill in limiting life (limit, crime, legitimate), while others emphasize the tendency to defend the law as a guarantee against violent behavior and gestures (behaviors, discrim). Instead, the right side illustrates the matter of the alleged violation of rights seen as a radicalization of the politically correct (snow white, get out of hand, madness). The second axis is characterized by the discussion around the cancel culture. The conversations mainly refer to the discourse stemmed from the comedic duo of Pio and Amedeo and the Snow White fairy tale - with its respective querelle on the alleged non-consensual kiss. The terms around these themes show criticism of politically correct and its derivation of cancel-culture. Above all, we find terms denoting intolerance (bothered, fuck) towards politically correct, an intolerance that can be exemplified by the

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Figure 3. Cluster analysis

usage of the word ‘dictatorship’: a word that often returns in the discourse against politically correct. Here, cancel culture is only used in the conversations in a rhetorical sense there are no actions of cancellation or boycott of someone or something.

Distinctive Orientation Groups Starting from the analysis of the lexical correspondences, a hierarchical cluster analysis was carried out to detect groups of words and make the different positions and opinions of Twitter users regarding politically correct in general emerge. Keywords that fall out in each cluster allow to identify the specific theme and to label the cluster. Four clusters were found, so named: “the Public Debate”, “the Anti-revisionist”, “the Satirical”, and “the Polemic”. The first cluster describes the wide discussion about the phenomenon, condensing public opinion and political debates. The second and third clusters are formed around topical issues and respectively concern the discussion on the Snow White cancellation and Pio and Amedeo’s monologue on politically correct. The last cluster expresses a clear position against politically correct. “The Public Debate” cluster holds together a large number of words and a semantic variety that reflect the political and ideological contested debate upon the issue of politically correct. The cluster includes, at the same time, the issues of politics, the need for laws that protect and defend people who

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Figure 4. ­

are verbally discriminated against or victims of physical violence, human rights, and, of course, freedom of expression (solidarity, violence, protect, fascism). However, the issue regarding the right to express oneself freely brings out two different ideological positions on that. Some people claim that the “freedom” of “expression” they feel has been taken away because of politically correct, which is experienced as “censorship” and not as a “protection”. Conversely, for others being free does not mean using whatever expressions, including those that may be offensive towards gender, ethnicity, and sexual orientation. In this sense, the politically correct do not eliminate freedom of expression, but, on the contrary, strengthen it, defending the freedom of “minorities”. The word “condemnation” refers both to the condemnation of politically correct and of those who reject it. In the first case, users claim the liberty to express themselves with certain words on behalf of them not being directly offensive; in fact, the word “sense” is present inside this cluster, together with “faggot” and “context”, showing how it is the sense entrusted to each word is important and the context where they are used. Moreover, they condemn political correctness because the recurring use of certain words results in ‘emptying’ them of their discriminatory meaning. According to this logic, using words like faggot or nigger means making them enter the common language as simple adjectives and thus destroying discrimination; to some, remarking offensive words is to make them customary and no longer offensive, implementing them in a context of normality and habit. Figure 5. ­

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Figure 6. ­

On the other hand, those who reject the assumptions of politically correct do not understand that this is the only way to condemn those who discriminate and, at the same time, to “defend” “minorities”. The cluster also includes those words that refer to the political discussions surrounding civil rights, in particular about the proposed ‘Zan’ bill. Here, Mancino law is discussed, now in force in Italy, which is not considered enough to defend the ‘freedom’ of individuals regarding their sexual orientation. Therefore, new and greater restrictions and sanctions against discrimination would be needed. In this case, words like “jail”, “wrong” and “absurd” are used. Furthermore, this group also contains those words about the political issue: “dictatorship”, “democracy”, “regime”, associated with words such as “right” and “left”. Those opposed to politically correct speak of it as a dictatorship created by the imagination of the left-wing political parties - more historically oriented towards openness and to citizens of other nationalities. This ‘dictatorship’ is a regime of ‘imposition’ of language and actions. On the other hand, the proximity of words such as “fascism”, “sexual” and “LGBT”, shows how right-wing parties disagree with other various forms of sexual orientation, supporting alleged normality in love affairs/relationships as well as in identity. The following three clusters are smaller in size than the first cluster but are focused on more specific aspects of PC. The second cluster identifies the Anti-revisionist, those who are against the review of cultural products of the past according to the values of present-day society. It revolves around the discussion upon the “deleting” of the kiss received by Snow White in the Disney fairy tale as it could be considered nonconsensual. The act of canceling may be seen as a form of extremization of politically correct. Defining the case of Snow White’s kiss as a “rape” is seen as a “madness” and a “controversy”. The Satirical cluster concerning Pio and Amedeo’s monologue also brought to light the theme concerning comedy. The tweets containing the words “laugh” in this cluster support the importance of laughter as a way to respond to the use of certain language - which, according to the two comedians, does not necessarily imply discrimination against the people towards whom the message is directed. This posture refers to what Granath and Ullén (2019) named “protesters”. The last cluster identifies the “polemic” behavior against PC and cancel culture. Here, the main interest is participating in the discussion only to express their contrary opinion, without providing arguments that can somehow enrich the debate. The cluster presents opposition to the deletion of words from everyday language, as well as to the censorship of films, books, or single extracts; in this regard, the most

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Figure 7. ­

Figure 8. ­

Figure 9. ­

relevant terms are “bothered”, “assholes”, “overreacting” that express a sort of intolerance towards the acting of canceling the culture. A further important word is “weight”: those who are against the cancel culture affirm that the most important thing is the weight given to the words and the way of saying them.

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Figure 10. ­

Figure 11. ­

The weight of the word and the meaning given to it depends on the ‘discourse’ around it, not the word itself. In this view, censorship is not the right way to solve discrimination.

CONCLUSION The paper illustrated the features and the meanings given to politically correct and cancel culture by Twitter users in the Italian scenario. The study confirms the contested and discussed nature of political correctness: there is no common vision among the users, but they tend to give different meanings and/or associate various political and social issues with it. In particular, PC includes two contrasting visions of freedom of expression: the one as a way of granting respect and rights to everyone, the other as a tool of limiting or, in extreme cases, of censuring people. The alleged violation of rights represents a kind of radicalization of the politically correct which translates into cancel culture. This result shows PC and cancel culture are two intertwined concepts. However, this kind of radicalization does not lead to a real cancellation of people or events, but it is mostly rhetorical. What emerged is the relativity of the speech around politically correct: any speech is not necessarily discriminatory per se but depends on the speaker’s intentions. In this sense, the politically correct is seen as an unnecessary limitation. The study presented here can contribute to the enhancing of that research strand aimed to improve the understanding of political correctness and cancel culture on social media through the detection of meanings and situations that characterize them. Along with this, this work makes evident one of the strengths of digital methods, namely the possibility of capturing data in real-time about a phenomenon

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that occurs almost completely in the digital context, providing a precise snapshot of public opinion in the days when the debate on politically correct in Italy peaked. The analysis on Twitter shows that many tweets that debated politically correct have been stimulated by specific and often isolated events. If the politically correct is still a widely debated phenomenon, cancel culture has not yet seen its real/complete application in the Italian context, for these reasons it is still harder to give an unambiguous definition. In this regard, future research should investigate these controversial issues from a diachronic perspective to discover how they develop over time and from what further events they may arise, as well as to be more complete framed and understood. The last issue regards the role played by social media in defining and affecting the meaning of PC and cancel culture. In this view, the study could be improved with new points of view by analyzing the same phenomena in different countries and on different platforms and social media. It could be interesting to investigate whether and how the various digital platforms affect ways of conceiving of politically correct, and of the perceived range of possible actions. In other words, it should take into account in deeper the role of affordances which can both facilitate and constrain the action (Bucher & Helmond, 2017). The term “social media” is in itself deeply ideological in that it claims certain inherently positive qualities – user-centred community-building (Van Dijk, 2013: 11); therefore, any change brought about by these media seems to be necessarily social and hence positive. In the case of Twitter, some of its intrinsic features, such as the textual brevity of any individual post (140/280 words), the speed with which posts are disseminated, and the rapidity of online exchanges, would reveal a new form of instantaneity (Herzogenrath-Amelung, 2016), foster a kind of ideological rigidity and lack of nuance (Ng, 2020), usually enhanced by a reductive narrative based on opposing forces of good and evil (Papacharissi, 2015). These features, indeed, would encourage brief, deeply emotive outbursts (Papacharissi, 2015; Sampson et al., 2028), while leaving no space for the reflexivity that is crucial in deal with sensitive issues such as racism, sexism, homophobia, disability, and so on, that are the target of politically correct campaigns (Bouvier, Machin, 2021). Indeed, engagement, such as likes and sharing, is often driven by sarcasm and cruel humor, along with memes and parody (Henefeld, 2016), which in fact can work as a form of self-promotion and attention grabbing (Udupa & Pohjonen, 2019). This is why the study of the meanings given to political correctness and cancel culture simply cannot overlook to take into account the features of the social media in which the discourses are placed, and question to what extent the features of the digital platforms may affect ways these phenomena are spread and are perceived by users. In this angle, the context would assume a dual nature: as a physical, social, and cultural environment where a digital phenomenon is located, but also as digital space, and here it is referred to the kind of social media platform, where people come into contact with it, exchange information and ideas, make groups or movements, and more, in general, produce different actions and reactions. Better framing of the context therefore would aid to disentangle the elements, values, and ideals that underlie political correctness and cancel culture from consequences of the intrinsic features of digital platforms in which the discourses are nowadays expressed.

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Lightfoot, E. B. (2019). Consumer Activism for Social Change. Social Work, 64(4), 301–309. doi:10.1093wwz035 PMID:31560773 Loury, G. C. (1994). Self-censorship in public discourse: A theory of “political correctness” and related phenomena. Rationality and Society, 6(4), 428–461. doi:10.1177/1043463194006004002 Luca, M., & Zervas, G. (2016). Fake it till you make it: Reputation, competition, and Yelp review fraud. Management Science, 62(12), 3, 412–413, 427. doi:10.1287/mnsc.2015.2304 Maddison, S., Sampson, T., & Ellis, D. (2018). Introduction: On Affect, Social Media and Criticality. In T. Sampson, S. Maddison, & D. Ellis (Eds.), Affect and Social Media: Emotion, Mediation, Anxiety and Contagion (Radical Cultural Studies) (pp. 1–9). Rowman & Littlefield International. Ng, E. (2020). No Grand Pronouncements Here...: Reflections on Cancel Culture and Digital Media Participation. Television & New Media, 21(6), 621–627. doi:10.1177/1527476420918828 NorrisP. (2020). Closed Minds? Is a ‘Cancel Culture’ Stifling Academic Freedom and Intellectual Debate in Political Science? SSRN. doi:10.2139/ssrn.3671026 O’Neill, B. (2011). A Critique of Politically Correct Language. Independent Institute Stable. The Independent Review, 16(2), 279-291. https://www.jstor.org/stable/24563157 Papacharissi, Z. (2015). Affective publics and structure of storytelling: Sentiment, events and mediality. Information Communication and Society, 19(3), 307–324. doi:10.1080/1369118X.2015.1109697 Perry, R. (1992). A Short History of the Term Politically Correct. In P. Aufderheide (Ed.), Beyond PC: Towards a Politics of Understanding (pp. 71–79). Graywolf Press. Rogers, R. (2020). Deplatforming: Following extreme Internet celebrities to Telegram and alternative social media. European Journal of Communication, 35(3), 213–229. doi:10.1177/0267323120922066 Roper, C. (2020, January 31). Political correctness. Encyclopedia Britannica. https://www.britannica. com/topic/political-correctness Saint-Louis, H. (2021). Understanding cancel culture: Normative and unequal sanctioning. First Monday. Advance online publication. doi:10.5210/fm.v26i7.10891 Shifman, L. (2013). Memes in a digital world: Reconciling with a conceptual troublemaker. Journal of Computer-Mediated Communication, 18(3), 362–377. doi:10.1111/jcc4.12013 Sills, S., Pickens, C., Beach, K., Jones, L., Calder-Dawe, O., Benton-Greig, P., & Gavey, N. (2016). Rape culture and social media: Young critics and a feminist counterpublic. Feminist Media Studies, 16(6), 935–951. doi:10.1080/14680777.2015.1137962 Spratt, V. (2018, April 13). #Cancelled: Has Call Out Culture Gone Too Far? Grazia. https://graziadaily. co.uk/life/opinion/call-out-culture-meaning/ Sucharov, M. (2021). Borders and Belonging: A Memoir. Palgrave Macmillan. doi:10.1007/978-3-03053732-6

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Thiele, M., Verlag, H., & Von, H. (2021). Political correctness and Cancel Culture - a question of power! Journalistik. doi:10.1453/2569-152X-12021-11282Toolan, M. (2003). Le Politiquement Correct Dans le Monde Francais. Discourse & Society, 14(1), 69–86. doi:10.1177/0957926503014001930 Udupa, S., & Pohjonen, M. (2019). Extreme Speech and Global Digital Cultures—Introduction. International Journal of Communication, 19. Van Dijk, J. (2013). The Culture of Connectivity: A Critical History of Social Media. Oxford University Press. Velasco, J. C. (2020). You are Cancelled: Virtual Collective Consciousness and the Emergence of Cancel Culture as Ideological Purging. Rupkatha Journal on Interdisciplinary Studies in Humanities, 12(5), 1–7. doi:10.21659/rupkatha.v12n5.rioc1s21n2 Wikström, P. (2016). No one is “pro-politically correct”: Positive construals of political correctness in Twitter conversations. Nordic Journal of English Studies. Wilson, J. K. (1995). The myth of political correctness: The conservative attack on higher education. Duke University Press. Zappavigna, M. (2011). Ambient affiliation: A linguistic perspective on Twitter. New Media & Society, 13(5), 788–806. doi:10.1177/1461444810385097

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The terms “political correctness” and “politically correct” are often used interchangeably in this paper. Here it is used the more general concept “Cancel culture”. Call-out culture refers to the mediation and exchanges between parties (Sills et al., 2016) where the contents and behaviour published by someone are denounced or are called attention. Doxing instead occurs when personal information about a person is released on the Web with the intent of causing harm (Douglas, 2016). Deplatforming is a platform’s removal of an individual or a group’s social media account following an infraction to the former’s terms of services (Rogers, 2020). The keywords were translated from Italian to English here. For better visualisation, all words searched for in the screaping phase have been removed. A threshold of frequency equal to 15 was fixed. The expression ‘free-speech’ derives from a replacement, i.e. it is a recoding made by aggregating the words ‘express’ and ‘freely’ into a single pattern. The expression ‘limit’ is derived from a replacement, of the words ‘limitation’, ‘restrict’, ‘limits’ and ‘limit’ in a single pattern. ‘Left’ has also been recoded as ‘pd’. ‘Salvini’, ‘Meloni’ and ‘Lega’ have also been recoded into ‘Right’. The expression ‘discrim’ is derived from a replacement, of the words ‘discrimination’, ‘discrimination’, ‘discriminator’ in one pattern.

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Section 6

Digital Research Practices The practices of social research are extremely diverse. Disciplines, topics, and focus can change radically between one scholar and another although the field of inquiry remains digitally bound. In this section, the authors present approaches, case studies, and applications aimed at exploring the vastness of a field of study that is certainly not circumscribable.

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Chapter 40

Between Moral and Climate Crisis:

Interpreting Climate Change Through the Lens of Moral Panic Maria Laura Ruiu Northumbria University, UK Massimo Ragnedda Northumbria University, UK

ABSTRACT This chapter identifies four main themes in the literature on media communication of climate change, which represent an interesting object of analysis for scholars who focus on moral panics’ application. The combination of both the processual model and the attributional model to interpret the results of this literature review shows that during its emergence, climate change was polarised between “advocates” and “deniers” of both its existence and anthropogenic causes. This division has progressively shifted towards the consequences of climate change and need for action against it. Two distinct moral panics are identified. One is rooted in sceptical arguments and seems to work “in reverse” by emphasising the “uncertainty” around the phenomenon and its impacts. A second one is triggered by climate change supporters, who emphasise that climate change threatens life on the planet and that the current social practices need regulation and control.

INTRODUCTION This chapter explores if and how the moral panic framework can be applied to the analysis of some contemporary social issues, such as the construction of the meaning of climate change. The originality of this work relies on applying the moral panic framework to the interpretation of a polarisation in the climate change debate. The literature shows that both political advances and the public understanding of climate change are “paralysed” due to the “confusion” promoted by media narratives. This mainly DOI: 10.4018/978-1-7998-8473-6.ch040

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 Between Moral and Climate Crisis

depends on the representation of opposite interpretations of the phenomenon. On the one hand, sceptical narratives support a “status quo”, meaning “no-intervention” in terms of implementing restrictive measures that might cause damage to both global and local economies. On the other hand, advocate narratives represent climate change as real and “catastrophic” in its consequences if current social practices are not “corrected”. This work contributes towards providing an analytical tool that can help identify the elements that encourage climate change scepticism by trying to direct policy-making and public opinion on climate change towards a “no-intervention” approach. This review expands the debate around the adoption of the moral panic framework to investigate climate change discourse. In fact, only a limited and controversial discussion exists on both the theoretical and empirical application of moral panics to the phenomenon. This chapter will show that the apparent conflict between those scholars who claim that climate scientists are the folk devils, and those who identify the villains as the deniers, can be solved if two different moral panics are explored separately. The chapter is structured as follows: the first section critically revises the existing debate around the use of moral panics to explore climate change; the second describes the method adopted to revise the literature on media communication of climate change. The third section and the related sub-sections explore the main contents/themes identified in the literature. The fourth explores the points of overlap of these themes with moral panics. Finally, some conclusions will be drawn on the application of the moral panic theoretical framework to climate change narratives.

MORAL PANICS TO INTERPRET CLIMATE CHANGE The current debate around the application of moral panics to climate change narratives is limited and controversial. Two main criticisms result from this discussion related to the theoretical applicability of moral panics to climate change and contrasting empirical findings. At a theoretical level, some studies describe climate change as a new “uncertain” risk (Lorenzoni and Pidgeon, 2006; O’Neill and Nicholson-Cole, 2009; Retchless, 2014; Weingart, Engels and Pansegrau, 2000) suggesting that, given its incalculability and invisibility, climate change cannot generate a moral panic due to the lack of both tangible effects and “folk devils” (Ungar, 1995, 2001). By contrast, Critcher (2003) argues that “folk devils”, “volatility” and the “public support” are no longer essential to generate moral panics. Ungar (1992, 1995) suggests the adoption of “social scare” rather than “moral panic” to interpret this “unpredictable” threat. According to this approach the impossibility of connecting real events to climate change (abstract and distant in time), makes it difficult to generate panic (see also Goode and Ben-Yehuda, 2009; Marsh and Melville, 2011). However, these considerations could be valid during the emergence of climate change in the media landscape, as that is when it was presented as an uncertain possibility and unpredictable in terms of its severity, location and period. Nowadays, the representation of scientific consensus around the existence of climate change has increased, and sceptical arguments have mainly shifted towards questioning the type of actions to be implemented (Boykoff, 2007). This is particularly evident in Europe, in which, since 2010, the public awareness of climate change has progressively increased (Stokes, Wike and Poushter, 2016). Moreover, the media are generally recognised to contribute towards “amplifying” or “de-amplifying” the public perception of a phenomenon (Murphy, Dunning and Williams, 1988), in particular when there is not a direct experience of it (Hall et al., 1978). At the same time, Ungar (1992, 1995, 1998, 1999, 2000, 2003) has repeatedly 728

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questioned the role of agenda setting in determining “What to think about” climate change. In contrast, some approaches interpret new risks as a trigger for generating moral panics (Hier, 2003). In fact, the uncertainty surrounding these new risks may encourage people to adopt “precautionary” approaches (Hier, 2015). Moreover, the simultaneous representation of risks as “negative”, due to their “unpredictability”, “uncertainty” and “inescapability” (Beck, 1992), but also potentially “positive”, thanks to efficient riskmanagement (Bernstein, 1996; Garland, 2003), echoes the dialectic between “bad” and “good” moral panics. Goode and Ben-Yehuda (2009) describe global warming as an “amoral panic” (Waiton, 2008) because even though it represents a “social concern”, it does not affect collective morality. By contrast, for Rohloff (2011a) risks are an integral part of moral regulation and there is a moral imperative for society to prevent potential harm (Zinn, 2007). The empirical application of moral panics to climate change has produced contradictory findings. Rohloff (2013b) and Cohen (2011) identify climate sceptics/deniers, and all those actors who directly or indirectly exploit the environment as responsible for the problem. Rohloff (2011a, 2011b, 2013a, 2013b), and Rohloff and Wright (2010) apply the moral panic lens to explore documentaries and other media products, showing that climate change narratives might activate positive moralisation processes. This suggests that people can be “educated” through the activation of “good moral panics”. By contrast, Brisman and South (2015) argue that the prevalence of sceptical narratives in the media landscape attributes the role of folk devils to mainstream scientists. They found that the moral panic concept works “in reverse” in the context of climate change. Following the attributional model proposed by Goode and Ben-Yehuda (2009), they identify a de-escalation in terms of both public reaction and action against the “problem”. However, when Brisman (2012, 2013) argues that corporate and political interests are responsible for the current situation, he seems to support that moral panics serve powerful structures in time of crisis to reaffirm the pre-established order. In this case, climate change seems to be an example of a “bad moral panic”, which is instrumentally used by those who generate the problem to defend their own interests The dialectic between “good” and “bad” moral panics suggests that more than one moral panic coexists in the context of climate change, which still needs to be investigated, especially in relation to the role played by the media. In fact, the media institutionalise different claim-makers who in turn identify certain folk devils, and this can generate multiple contrasting interpretations and confusion around the problem.

LITERATURE REVIEW, METHOD AND MAIN COMPONENTS In order to investigate the applicability of the theoretical framework, a literature review was conducted on a core set of 273 academic articles on media communication of climate change. The keywords “communication of climate change”, “weather extreme” “climate perception” were used to search academic articles in Journal Search engines such as Directory of Open Access Journals - DOAJ (www.doaj.org), Elsevier – Science Direct (www.sciencedirect.com), Jurn (www.jurn.org), Open Access Journals Search Engine - OAJSE (www.oajse.com), Google Scholar, and Web of Science Thomson Reuters (http:// thomsonreuters.com). The period (2010 and 2016) was established in relation to two factors: the most recent literature review on media communication of climate change includes a period from 2000 to 2011 (Wibeck, 2014); and since 2010 the public awareness of climate-related risks has increased in Europe (Stokes et al., 2016). Papers were retained for inclusion if they directly pertained to media communication of climate change; and were academic articles/editorials (excluding review articles, books and PhD 729

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theses). Papers that did not focus primarily on communication of climate change were not included in this core set of articles. The literature review was carried out by reading the papers in full and identifying the main contents. Then, these contents were classified as themes and interpreted by combining the three phases included in the processual model by Cohen (“Inventory Phase”, “Opinion and Attitude Themes Phase”, and “Rescue and Remedy Phase”) (1973) with the characteristics of the attributional model identified by Goode and Ben-Yehuda (“concern”, “hostility”, “consensus”, “disproportionality”, and “volatility”) (2009). Accordingly, the Media Inventory Phase is characterised by the “attributions” of “consensus”, “concern” and “disproportionality”. In fact, this is the phase in which the media i) exaggerate and distort the problem (“disproportionality”), ii) represent expert consensus around the understanding of the phenomenon, and iii) generate “concern” around a “common threat” by using symbols and predicting future events related to the phenomenon. During the Opinion and Attitude Themes Phase specific interpretations are encouraged around the causes and consequences of the problem, and specific folk devils are identified (“hostility” trait). Finally, in the Rescue and Remedy Phase both “concern” and “volatility” are used to support the implementation of specific control measures. The main differences between the two approaches relate to both the temporal perspective and the interpretation of the role of the media. The processual model interprets the media as “initiators” of a process (from a diachronic perspective); whereas the attributional model interprets the media as “amplifiers” of other views by adopting a synchronic perspective (Critcher, 2003). However, both models attribute a role (primary or secondary) to the media in activating moral panics. Two hundred and twenty-three out of 273 articles considered in this review are represented by empirical studies that refer to both global and local dimensions of the media communication of climate change. The majority of the studies are published in social sciences journals, however, 42 papers are published in climate science journals. This suggests that climate scientists have been increasingly reflecting on the effectiveness of media communication of scientific findings (see Figure 1). The main contents identified fall into four categories that can be labelled as i) “media polarisation between sceptics and advocates”; ii) “between underestimation and apocalyptic scenarios”; iii) “climate as political/economic problem”; and iv) “public engagement”. The first theme related to the polarisation between sceptics and advocates is connected to the identification of two different groups of folk devils. This polarisation is in turn connected to the second topic related to a polarisation between over-estimation and underestimation of the problem. Moreover, the topic related to the political/economic valence of the phenomenon raises the same questions that characterise the moral panic debate in terms of the relationship between media reporting and the structures of power (Hall et al., 1978). The relationship between climate communication and public engagement will only be transversally investigated since this paper mainly focuses on media representations of climate change.

MEDIA POLARISATION: “SCEPTICS VERSUS ADVOCATES” One of the most analysed topics in the literature related to the conflict between “contrarians” and “advocates” of climate change (McKnight, 2010; Painter and Ashe, 2012; Painter and Gavin, 2015). The literature highlights that a “disproportionate space” is given to contrarians, even though they represent a minority in the scientific debate (Boykoff, 2013; Boykoff and Boykoff, 2004; Freudenburg and Muselli, 2010; Moser and Dilling, 2004; Rahmstorf, 2012; Tosse, 2013). These actors can be empowered by the 730

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Figure 1. Number of articles per journal’s discipline

media as a legitimate counter-movement to mainstream science (Jaspal, Nerlich and van Vuuren, 2016) during specific events (e.g. scandals or errors in IPCC reports, see Anderegg and Goldsmith, 2014; Somerville and Hassol, 2011). A multiplicity of causes explain this “disproportionality” (Aram, 2012; Boykoff and Yulsman, 2013; Farnsworth and Lichter, 2012), however, the representation of consensus around climate change within the scientific community has been increasingly recognised by the media, even in those contexts historically characterised by scepticism (such as the USA) (Grundmann and Scott, 2014; Jang and Hart, 2015). This shift has resulted in the representation of climate change as a real phenomenon, which is mainly caused by human activities (Gibson, Craig and Harper, 2015). However, the polarisation between “sceptics” and “advocates” has evolved into new forms by shifting the focus from the causes to the consequences and actions needed to tackle the phenomenon. The literature shows a complex picture in which multiple patterns of interpretation are provided by the media, and this makes the identification of specific “folk devils” difficult. In fact, assuming that climate scientists represent the primary definers of climate change (Cohen, 2011), the folk devils should be identified in those who deny the problem or contribute towards increasing it. In contrast, climate change contrarians become authoritative voices in the debate (often by playing the role of claim-makers) and publicly accuse climate change scientists of orchestrating a “conspiracy”. This conspiracy is initially linked to the mystification of the causes, then to the exaggeration of the consequences of climate change (Boykoff, 2013). The mainstream scientists often become the “folk devils” who try to “scare” the public and impose restrictions on people’s lifestyles. The resulting image contributes towards creating a “nebulous aura” around climate science.

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BETWEEN UNDERESTIMATION AND APOCALYPTIC SCENARIOS A conflictual character of the scientific debate can be also identified in a division between “underestimation” and “exaggeration” of the consequences of climate change. The resulting image contributes towards increasing confusion (Speck, 2010), uncertainty (Rhomberg, 2010; Ward, 2010) and feelings of fear (Manzo, 2010a, 2010b), in particular if climate change is represented as a “vindication” of nature that is out of human control due to its unpredictability (Markowitz and Shariff, 2012; Salvador and Norton, 2011). The communication of uncertainty is generally interpreted as a double-edged sword that produces different effects depending on the type of audience and their personal characteristics (Feldman et al., 2014; Retchless, 2014). On the one hand, it might increase people’s engagement and their confidence in science transparency (Pearce et al., 2015), and offer several patterns of solutions to policy-makers (Patt and Weber, 2014). On the other hand, it might increase underestimation of the problem and provide a justification for delayed action (Retchless, 2014). In this direction, when the problem is underestimated the uncertainty becomes a dominant topic (Lockwood, 2009). At the same time, the representation of catastrophic consequences (but also the connection of climate change to extreme weather events, see e.g. Brulle, Carmichael and Jenkins, 2012) might increase awareness and fear of climate change as a temporally and spatially distant phenomenon (Manzo 2010a). This indicates that both uncertainty and exaggeration negatively affect public engagement (Greitemeyer, 2013; Nerlich and Jaspal, 2014; Wibeck, 2014). Some studies highlight that although the “hollywoodian” effects” (such as e.g. in the case of the movie “The Day After Tomorrow”) often represent an attempt to activate processes of “civilisation” (Rohloff, 2012) and forms of “good moral panics” (Cogen, 1973), they can only reinforce existing environmental orientations (Howell, 2011, 2014; Morrison and Hatfield-Dodds, 2011). The representation of catastrophic consequences was also found to increase both awareness and engagement but only in the short term (Howell, 2011; Jacobsen, 2011; Nolan, 2010; Sakellari, 2014). The other side of the adoption of “drama” in climate narratives, is that it can be instrumentally used by sceptics as evidence of inaccuracies in climate science (Von Burg, 2012) and to support the idea that mainstream scientists try to “scare” the public by exaggerating the severity of climate change. This suggests an attempt to activate “bad moral panics” by promoting underestimation of the problem and the idea that there is no need to tackle climate change. This polarisation between “under” and “over” estimation of the problem suggests that, on the one hand, the “underestimation” favours the contrarian crusade by inflaming hostility against those scientists/environmentalists who willingly exaggerate the problem to generate panic. On the other hand, the use of “sensationalism” can produce diverse results by either generating “panic” (due to the human incapacity to cope with the consequences of climate change) or promoting the idea of an “overestimation” of the severity of climate change (due to the lack of tangible evidence).

CLIMATE AS POLITICAL/ECONOMIC PROBLEM The literature shows a multiplicity of connections between climate change and politics in relation to the international political arena (Kunelius and Eide, 2012; Malhotra, 2015), the political standpoints of the media, the political polarisation of the audience (Feldman et al., 2014; Hart et al., 2015; Kim, 2011),

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and the (direct or indirect) influence exercised by governments (and powerful businesses) on the media (Boussalis, Coan, and Poberezhskaya, 2016; Poberezhskaya, 2015; Speck, 2010). It appears that mixed political and economic interests are implicitly defended by the media (Batta, Ashong and Bashir, 2013; Kirilenko and Stepchenkova, 2012; Murphy, 2015; Stoddart, Haluza-Delay and Tindall, 2015). Climate change policies (Gkiouzepas and Botetzagias, 2015), policy and politicians’ failures in reducing emissions are also a target of the media (Blanco Castilla, Quesada and Teruel Rodríguez 2013; Feldman, 2013; Gunster, 2011). The political valence of climate change is testified for by the frequent adoption of specific political voices/sources as claim-makers in comparison to scientific figures (Dotson et al., 2012; Grundmann and Scott, 2014; O’Neill, 2013; Rebich-Hespanha et al., 2015; Takahashi et al., 2016; Takahashi and Meisner, 2013; Young and Dugas, 2012). Furthermore, global political events tend to attract more media attention (DiFrancesco and Young, 2011; Liang et al., 2014) than the publication of scientific literature (Rick, Boykoff and Pielke, 2011; Schäfer et al., 2013). Climate change is often defined as filtered by the political interpretation that dominates climate change narratives (Dirikx and Gelders, 2010; Schmidt and Schäfer, 2015). In this direction, a number of studies showed that the political polarisation of the media is often reflected in a political polarisation of the public (Feldman et al., 2012, 2014; Hart and Nisbet, 2012; Jamieson and Hardy, 2014). In addition to a political dimension, the media were found to serve economic interests (Zamith, Pinto and Villar, 2012) and elite statements (Shehata and Hopmann, 2012). The economic and political valences of climate change are often intertwined, e.g. when defending the production of nuclear energy (Doyle, 2011) or fracking (DiFrancesco and Young, 2011) by insisting on the need for energy supply/ security. In these cases, climate change is discussed in the light of technical and economic dimensions (Cherry et al., 2013; Uldam, 2013). Sometimes these narratives shift the focus from a scientific angle towards a “political/business angle”, also in relation to the political orientation of the media (Yun et al., 2012). This political/economic “polarisation” connects the climate change issue to some criticalities that characterise the moral panic debate, in particular in relation to the role of the media in terms of primary or secondary definers of social problems (Hall et al., 1978). Moreover, the existence of two opposite groups that use several different media formats and channels (Cooper, 2011), makes it difficult to identify folk devils and their counterparts (McRobbie and Thornton, 1995; Thornton, 1994). At the same time, this polarisation seems to support the idea that the media reflects the “existing power-play” between political elites (Bennett, 1990; Hallin, 1986).

DISCUSSION: TRACES OF MORAL PANIC IN MEDIA NARRATIVES OF CLIMATE CHANGE Media narratives of climate change can be influenced by a number of elements such as e.g., the occurrence of climatic extremes, scientific findings, climate policies, and contextual factors (political, cultural and politically related events) (Moser, 2016). The combination of all these elements also contributes to shaping different narratives around climate change in relation to its evolution over time (in scientific, political and economic terms), and its spatial dimension (global/national/local). The adoption of both the processual model (Cohen, 1973) and the attributional model (Goode and Ben-Yehuda, 2009) can help interpret the rival narratives emerging from the literature review.

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From an attributional model perspective “concern”, “hostility”, “consensus”, “disproportionality”, and “volatility” imply different meanings in relation to the perspective adopted. The literature suggests an evolution of climate change and the related “concern” from a starting phase (“emergence phase”), in which the polarisation between “deniers” and “supporters” related to both the reality of the phenomenon and its anthropogenic causes, towards a polarisation around the consequences of climate change and the related intervention needed. A fracture between two opposite forces emerges, which is also reflected in the polarisation of public opinion (Stokes et al., 2016). These reflections are connected to the attribution of “hostility”. Two different tendencies can be identified in relation to the perspective adopted. According to the “sceptics”, the folk devils are represented by mainstream climate scientists and those environmentalists (such as activists and NGOs) who cause a “societal crisis”. By contrast, following the “advocates”, the “villains” are those who deny or cause environmental damage (Rohloff, 2013b). Goode and Ben-Yehuda (2009) argue that moral panics do not need universal consensus, but they can be limited to specific groups or areas. This can explain the existence of different “versions” provided by each group. In fact, two different types of “generalised consensus” can be identified, one related to the anthropogenic causes and real consequences of climate change; and another to the “absence of agreement” within the scientific community. In the latter case, the consensus also concerns the awareness that climate scientists (folk devils) exaggerate their findings because they want to scare people. This is directly connected to another controversial “attribution” of the moral panic framework related to “disproportionality”. “Over-exaggerated” reactions in terms of both public reaction and governments’ countermeasures cannot be identified in the context of climate change. However, some revised approaches to moral panic (see Critcher, 2003) do not consider the public reaction fundamental to generating moral panics, but they focus on the media representation of such reactions. The disproportionality needs to be explored in relation to both the “over-representation” (and under-representation) of specific opinions and the “over-dramatization” (or “underestimation”) of climate change consequences by the media. This means that, on the one hand, sceptics accuse media reporting (and advocate narratives) of being deliberately “apocalyptic”. On the other hand, climate change supporters claim that the media overrepresent contrarians and underestimate the consequences of climate change. Finally, considering the attribution of volatility, media attention is captured by a multiplicity of economic and political events. However, the literature highlights that, in correspondence to peaks, a prevalence of specific voices can be identified, such as e.g. sceptical positions in reporting of climaterelated scandals, which undermine scientific credibility (Leiserowitz et al., 2012). From these considerations two different moral panics seem to emerge related to two specific groups. Adopting a processual point of view, the evolution of climate narratives has involved different voices in the media debate over time, from scientists and environmentalists (since 1980), to politicians, economic actors (but also celebrities and testimonials) as authoritative claim-makers (Anderson, 2011). This evolution has been followed by a shift of focus from the scientific debate towards the political and economic one (Moser, 2010). This complicates the identification of the primary definers of the problem, because each group has space in the media debate. However, at the beginning of this process (the inventory phase for Cohen), climate change was mostly debated in scientific terms. From 1990 onward (“opinion and attitude phase”) climate scientists have progressively lost their authority as primary definers, whilst political and economic actors have increased their influence, becoming legitimate voices who speak for the climate (Boykoff and Boykoff, 2004; Carvalho, 2007). This has caused a fracture between two factions, each of them promoting opposite “panics” in relation to different folk devils. 734

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The traits of sceptics/contrarians’ climate change narratives support the hypothesis of a moral panic working “in reverse” (Brisman, 2012). This means that this group promotes a lack of consensus in the scientific community (at the beginning in terms of causes, and then in terms of consequences). A generalised consensus can be found around the identification of mainstream climate scientists as the cause of the societal crisis. In fact, these scientists blame human activities for causing climate change and attribute the responsibility for solving the problem to society. In this direction, the concern does not trigger overreaction in the public. By contrast, the disproportionality concerns an underestimation of the problem (and over-estimation of consequences attributed to their counterpart). The panic is connected to the potential implementation of top-down restrictive measures that will affect people’s well-being. Moreover, the frequent reference to climate change as an “abstract” phenomenon (Kleinschmit and Sjöstedt, 2014) does not activate “moral alarmism”, it being described either as a “vindication” of the nature, or as an “unknown” risk that does not need to be “righted” (Markowitz and Shariff, 2012). The combination of all these elements supports the necessity of reaffirming the “status quo” by contrasting the real “threat” (mainstream scientists, but also activists and NGOs and environmentalists in general). By contrast, in the case of the “advocates” scientific consensus and concern around climate change and the necessity of changing current lifestyles are emphasised. Moreover, “top-down” measures to mitigate the causes of the problem are invoked. The disproportionality relates to the use of “drama” in climate narratives and an over-estimated authority given by certain media (mainly conservative) to sceptics, who represent the minority within the scientific community. The hostility is inflamed against a multiplicity of “villains”, identified in the phenomenon itself, and in all those actors (including climate change deniers) who damage the environment and contribute to the problem (Rohloff, 2013b). The “climate crisis” becomes a “moral crisis” in which both individuals and societies are encouraged to reflect on their own values and behaviours to limit their contribution to the problem. Climate scientists play a “moral role” in communicating their scientific findings to the public audience (Tosse, 2013). The morality of the problem is also evident in encouraging the media to play the role of “moral entrepreneurs” who can facilitate the translation of scientific recommendations into practice (Corner and Groves, 2014). In this direction, some media products try to activate “processes of civilisation” (Rohloff, 2012) through the use of “dramatic frames” to describe the “catastrophic” consequences of climate change. The panic results from the fear of “catastrophic consequences” for both the planet and humans. However, the dramatization that often characterises these warning messages also represents an opportunity for sceptics/deniers to discredit the science given the “sensational effects” adopted by these narratives.

CONCLUSION Climate change narratives can be interpreted by adopting both an attributional approach, more focused on the specific characteristics of the phenomenon, and a processual perspective, more concerned about its evolution. Both approaches capture a polarisation constantly present in the debate. However, in the emergence phase this division involves “deniers” and “supporters” of the existence of climate change and its anthropogenic causes. During the “attitude and opinion development” the polarisation shifts towards the effects of climate change. Finally, the “rescue and remedy phase” focuses on the (in)action needed. Given the lack of a “unique and universal” narrative, there emerges the hypothesis of two different moral panics related to two groups. This is supported by the identification of four themes in the literature that are characterised by two storylines that follow opposite directions. 735

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In the case of contrarians, the adoption of an “attributional perspective” demonstrates a moral panic working “in reverse”, which is characterised by a lack of scientific consensus around climate change and, by contrast, a generalised consensus about the identification of mainstream scientists as folk devils. In fact, they threaten the societal order because they arbitrarily attribute both the causes of climate change and the responsibility to solve the problem to the society. The concern is around the implementation of unreasonable restrictive measures. The disproportionality relates to an underestimation of the consequences of climate change (and over-estimation of them attributed to advocate narratives). Climate change is “amoral” because it is neither caused by nor can be tackled by humans. By contrast, it might produce positive consequences. This approach emphasises the need to re-establish the “status quo” by contrasting the common “threat” represented by the mainstream scientists (and environmentalists in general). In the case of advocates, consensus and concern relate to the recognition (and fear) of severe consequences that derive from perpetuating “harmful” individual and collective behaviours. The disproportionality is expressed in terms of an accusation of over-representation of sceptics by the media (and the use of dramatic tones to describe the consequences of climate change). Here, the folk devils are represented by climate/sceptics/deniers and those powerful actors who exploit the environment. The “climate crisis” reflects a “moral crisis” in which everyone has the “moral imperative” to “correct” those “wrong” values and behaviours that cause climate change. The panic derives from potential “catastrophic” consequences on society. As a consequence, a necessity to implement state-controlled measures to limit and mitigate the problem is highlighted. The literature highlights an increasing consensus around the existence of climate change and its anthropogenic nature over time. However, the scepticism has shifted towards questioning the consequences of climate change. Directly related to this point is the uncertainty around the kind of intervention needed. The simultaneous presence of two contrasting forces creates confusion and promotes delay in action. In turn, this favours the perpetuation of activities that are harmful to the environment. This suggests that the “sceptical crusade” benefits from such confusion, supporting the idea of an elite-engineered model in which the dominant economic actors reaffirm their power. However, empirical investigation is needed to further explore the role of the media as either primary or secondary definers of the meaning of climate change. The empirical investigation of media products might help to clarify if the hypothesis regarding the prevalence of the sceptical crusade in the climate debate is confirmed.

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Tosse, S. E. (2013). Aiming for Social or Political Robustness? Media Strategies among Climate Scientists. Science Communication, 35(1), 32–55. doi:10.1177/1075547012438465 Uldam, J. (2013). Activism and the online mediation opportunity structure: Attempts to impact global climate change policies? Policy and Internet, 5(1), 56–75. doi:10.1002/poi3.22 Ungar, S. (1992). The Rise and (Relative) Decline of Global Warming as a Social Problem. The Sociological Quarterly, 33(4), 483–501. doi:10.1111/j.1533-8525.1992.tb00139.x Ungar, S. (1995). Social scares and global warming: Beyond the Rio convention. Society & Natural Resources, 8(5), 443–456. doi:10.1080/08941929509380935 Ungar, S. (1998). Bringing the issue back in: Comparing the marketability of the ozone hole and global warming. Social Problems, 45(4), 510–527. doi:10.2307/3097210 Ungar, S. (1999). Is strange weather in the air? A study of U.S. national network news coverage of extreme weather events. Climatic Change, 41(2), 133–150. doi:10.1023/A:1005417410867 Ungar, S. (2000). Knowledge, ignorance and the popular culture: Climate change versus the ozone hole. Public Understanding of Science (Bristol, England), 9(3), 297–312. doi:10.1088/0963-6625/9/3/306 Ungar, S. (2001). Moral panic versus the risk society: The implications of the changing sites of social anxiety. The British Journal of Sociology, 52(2), 271–291. doi:10.1080/00071310120044980 PMID:11440057 Ungar, S. (2003). Global warming versus ozone depletion: Failure and success in North America. Climate Research, 23(3), 263–274. doi:10.3354/cr023263 Von Burg, R. (2012). Decades Away or The Day After Tomorrow? Rhetoric, Film, and the Global Warming Debate. Critical Studies in Media Communication, 29(1), 7–26. doi:10.1080/15295036.2011.637221 Waiton, S. (2008). The Politics of Antisocial Behaviour: Amoral Panics. Routledge. Ward, R. E. T. (2010). Adapting our cities for future climates: 17 February 2010. Weather, 65(11), 307–309. doi:10.1002/wea.602 Wibeck, V. (2014). Enhancing learning, communication and public engagement about climate change – some lessons from recent literature. Environmental Education Research, 20(3), 387–411. doi:10.108 0/13504622.2013.812720 Young, N., & Dugas, E. (2012). Comparing Climate Change Coverage in Canadian English-and FrenchLanguage Print Media: Environmental Values, Media Cultures, and the Narration of Global Warming. Canadian Journal of Sociology, 37(1), 25–54. doi:10.29173/cjs9733 Yun, S. J., Ku, D., Park, N. B., & Han, J. (2012). A Comparative Analysis of South Korean Newspaper Coverage on Climate Change : Focusing on Conservative, Progressive, and Economic Newspapers. Development and Society, 41(2), 201–228. doi:10.21588/dns.2012.41.2.002 Zamith, R., Pinto, J., & Villar, M. E. (2012). Constructing Climate Change in the Americas. An Analysis of News Coverage in U.S. and South American Newspapers. Science Communication, 35(3), 334–357. doi:10.1177/1075547012457470

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Zinn, J. (2007). Risk, Social Change and Morals. Conceptual Approaches of Sociological Risk Theories. Working paper 2007/2017. Social Contexts and Responses to Risk Network. Available at: https://www. kent.ac.uk/scarr/

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Chapter 41

Digital Methodologies for the Historiography of the History of Europe:

Testing Omeka Software on the “AsE” Alessandro Laruffa Sapienza University of Rome, Italy

ABSTRACT Within the historiography of history of Europe in the 20th century, it can be observed that the methodologies are mostly structured on archival research and comparative methods. Currently, the digital revolution has enabled the management of large amounts of data, information, and statistics. The history of historiography could consider the innovative methodologies for historical research like the digital humanities. This chapter reports the test of Omeka-S, an open-source content management system (CMS) specifically designed for humanities studies, on the history of European historiography. Omeka has been applied for the functions of digitisation, metadatation, and geolocation in accordance with international standards. The case study is the Association of European Historians (AsE), a network of historians from several European and non-European countries founded in 1983. The use of Omeka-S, in combination with traditional methodologies and network analysis, allows a more in-depth examination of the AsE’s network and its historiographical paradigm.

INTRODUCTION The paper analyses a methodological approach of the author’s PhD project in history of Europe entitled “From history of Europe to Digital Humanities: the Association of European Historians through digital history”. Starting from the description of the heuristic transition from digital humanities to digital history, the present study examines the application of Omeka-S, an open-source Content Management System (CMS) specifically designed for humanities studies, to the historical research. After the illustration of a significant example with regard to the employment of Omeka-S, the paper focuses on the author’s case DOI: 10.4018/978-1-7998-8473-6.ch041

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 Digital Methodologies for the Historiography of the History of Europe

study: the Association of European Historians (AsE), a network of historians from several European and non-European countries founded in 1983. Ultimately, the author discusses the added value of the application of Omeka-S to his research project.

From Digital Humanities to Digital History: A Heuristic Approach The digital revolution has a profound impact on the methodologies for studying, sharing, and teaching history, as well as on the analysis, conservation and production of sources. According to a widespread cortainty, the introduction of computing to the humanities has not been met favourably by researchers and scholars. For instance, Boonstra et al. (2006) highlight how the debates on the use of computers in the humanities are frequently characterised by a deep resistance against computing. They affirm that “...at the same time, we can see that, although basic computing skills of word processing, e-mailing and web browsing are nowadays omnipresent among humanities scholars, their methodical and technical skills for computerised research are fairly limited”. Nevertheless, few dispute that digital technology is fundamentally changing the research processes. Indeed, it can be observed that research is increasingly being mediated through digital technology. According to a variety of scholars, the latter mediation is progressively modifying the research, affecting both the epistemologies and the ontologies that underlie a research programme (Ayers, 2001; Schreibman et al., 2004; Berry, 2011; Gold, 2012; Zaagsma, 2013; Sula, Hill, 2017; Fridlund et al., 2020). Arguably, the development of digital technologies depends on disciplines and research methodologies; notwithstanding, it is currently rare to find a humanist who has not access to digital technology as part of its research activity. Whilst some scholars deny the “newness” of digital technologies and decry the loss of skills and techniques of older research traditions (Frisch, 2008; Fish, 2011; Marche, 2012; Liu, 2013; Kim, Stommel, 2018), others have warmly embraced what has come to be called the (DH) digital humanities (Svensson, 2010; Drucker, 2011; Berry, 2012; Hayles, 2012; Ramsay, Rockwell, 2012; Schreibman et al., 2015). The latter distinction suggests a debate in progress. Thus, it is tempting to consider the focus on the intersection between humanities and digital tools as a recent evolution. However, albeit the discursive transition from “humanities computing” to “digital humanities” in roughly the past decade, narratives on its origins homogeneously date back to 1950s. Most of scholars ground DH in mid-twentieth-century humanities computing (Hockey, 2004; Svensson, 2009; Kirschenbaum, 2010; Dalbello, 2011). The pioneering work of Roberto Busa, Italian Jesuit, linguist and computer scientist, is commonly considered the starting point of computer-aided research in the humanities. In 1949 Busa started his cutting-edge project on a lemmatised concordance of the works of Thomas of Aquino, the so-called Index Thomisticus, with the assistance of IBM. Thereafter, in 1962 an international conference entitled “The Use of Computers in Anthropology” took place in Burg Wartenstein, Austria (Hymes, 1965). Two years later, IBM organised a “Literary Data Processing Conference” (Bessinger, 1964), forerunning the dominance of text-based literary and linguistic analysis in the so-called “first wave” of digital humanities (Schnapp et al., 2009; Presner, 2010). The connections between the human-generated work and the move to automated systems for collecting and collating humanities materials is useful to analyse the shift from a predominantly print culture to a digital one. The need for a new conceptual language of tools and archives led to the creation of support systems and semi-standardised software for the archiving and maintaining of textual repositories (Hockey, 2004). The 1970s and 1980s are largely considered as periods of “consolidation” of text analysis methods. As storage and processing capabilities increased from the late 1970s onward, structured electronic text and multimedia archives dominated the field, followed in the 1990s by 747

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Internet-enabled hypertexts, digital libraries, and collaborative editing (Sula, Hill, 2019). Presner et al. (2009) argue that “the first wave of digital humanities work was quantitative, mobilizing the search and retrieval powers of the database, automating corpus linguistics, stacking hypercards into critical arrays. The second wave is qualitative, interpretive, experiential, emotive, generative in character. It harnesses digital toolkits in the service of the Humanities’ core methodological strengths: attention to complexity, medium specificity, historical context, analytical depth, critique and interpretation”. Notwithstanding, the items above described are not sufficient to explain comprehensively digital humanities as a field of study. We can find the first attempts to define DH within the Anglo-Saxon context, but over time the debate has spread to the main international scientific communities. At the end of the last decade, some scholars attempted to define more specifically this field of research, trying to circumscribe the heterogeneous set of tools and practices labelled as Digital Humanities. Manifesto 2.0 (2009) considers DH as a trans-discipline which incorporates “all the methods, systems and heuristic perspectives linked to the digital within the fields of humanities and social sciences”. However, several academic contributions on the topic reflect an identity crisis that digital humanists seem to suffer. They are divided into, on the one hand, those who consider the DH as a revolution which can profoundly transform the work of all humanists; and, on the other hand, those who strongly doubt that DH could have a significant impact on humanistic research. Within the debate, deserve a mention the conceptualisation of Stephen Ramsay (2013) who considers DH every activity that produces digital objects in the humanities. For instance, humanists should be able to interpret a map, a network, a text or an image; whereas a digital humanist is even able to produce these materials. Thus, a digital humanist is not only a scholar who disseminates the results of research using a blog or a portal: he is rather a researcher who designs, builds, and supervises the construction of digital objects for the pursuit of scientific objectives (Salice, 2017). Nonetheless, DH cannot operate in place of traditional methodologies, but rather it can be affirmed that digital technologies contribute to increase the management and analytical potential of research. Through DH, the researcher can organise and visualise elements that are invisible using traditional methodologies. Therefore, DH allow a more in-depth analysis of research materials even through the production of sources (Péoux & Houllier, 2017). Thus,referring to a general definition, currently the expression digital humanities refers to a discipline of study, research and teaching born from the intersection between electronic computation and human sciences (Drucker, 2014). Yet, according to the opinion of many scholars, the effectiveness of DH in relation to historical research, as well as the relationship with traditional methodologies, represent a field of study which has just opened up (Ansani & Ghignoli, 2008; Clavert & Noiret, 2012; Robertson, 2016; Salice, 2017; Salvatori, 2017a). History and the profession of historian are evolving and modifying due both to changes in society and to the development of new methods of analysis (Ansani & Ghignoli, 2008). At the same time, Stephen Robertson (2016) illustrates how digital history should be distinguished from Digital Humanities. He argues that: “Moving forward, we would be better served by reimagining digital humanities not as single all-encompassing tent but as a house with many rooms, different spaces for disciplines that are not silos but entry points and conduits to central spaces where those from different disciplines working with particular tools and media can gather. Each of the many disciplinary rooms would have a distinctive character, reflecting a particular contribution and orientation to the field” (Robertson, 2016).

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As for every neologism and new discipline born from the introduction of digital technologies in the humanities, it is not simple to give a precise definition of “digital history”. It can be assumed that digital history refers to the use, study and processing of digital tools applied to historical research, from the analysis of the sources to the dissemination of the results (Salvatori, 2017a). According to the Center for History and New Media (CHNM) of George Mason University: “Digital history is an approach to examining and representing the past that takes advantage of new communication technologies such as computers and the Web. It draws on essential features of the digital realm, such as databases, hypertextualization and networks, to create and share historical knowledge. Digital history complements other forms of history ‐ indeed, it draws its strength and methodological rigor from this age‐old form of human understanding while using the latest technology1”. Considering most of the worldwide historical research production, it can be observeda partial digitalisation of the historical field of study (Salvatori, 2017b). To summarise, the reasons for this “false innovation” are the distrust of many historians towards computers and networks, the low value given to digital products for career progression, and the widespread ignorance about main digital methodologies (Salvatori, 2017a). Historians should be aware that new research tools do not only generate superficial modifications on working methods. A scientific approach to DH should imply the comprehension of functions and limits with regard to this innovative methodology. For instance, the researchers could implement strategies to manage digital change, like working in interdisciplinary teams (Salvatori, 2017a). Withindigital humanities, as well as in digital history, historians often collaborate with humanists, graphic designers, philologists, computational linguists, and other scholars with a deeper digital education. The technologies implemented in the last twenty years could profoundly modify the work of the historian through new patterns of publishing and reading sources, innovative methodologies for unpublished historiographical productions, and significant advances in didactics of history (Ansani & Ghignoli, 2008). Most infrastructures of scientific knowledge have become digital: this factor has a decisive influence in conceiving, writing and dialoguing history (Clavert & Noiret, 2012). Consequently, the issue is how the digital revolution transforms historical research, dissemination of results and didactics of history, as well as the opportunities and problems it presents (Salvatori, 2017a). Some scholars suggests the need for a longue durée approach, associated at first with Fernand Braudel and the Annales school. The longue durée is feasible because the unprecedented availability of materials, as well as the tools to interpret them, makes contemporary historians able to analyse a larger amount of documents compared to the past (Guldi, Harmitage, 2015). At the same time, longue durée approach allows to step outside the confines of national history and to inquire the rise of long-term structures over decades, centuries, or even millennia, a necessary scaling to understand the current global issues (Guldi, Harmitage, 2014). Thus, longue durée perspective has renewed importance considering that the work of the cultural and linguistic turns, to date developed through the “close reading”, could be currently done both through “close” and “distant reading” with digital methods (Moretti, 2013; Fridlund et al., 2020). Following the latter approach, the present study aims to connect close and distant reading through digital technologies.

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METHODOLOGY Omeka Software Normally, a DH project consists of a store of digital objects described through metadata, which are accompanied by a variable set of services: the latter is used to organise and visualise the contents. To accomplish these functions, most DH projects use a similar logical structure. Regardless of the topic of research and the disciplinary objectives pursued, the projects are managed through a Content Management System (CMS), a computer software used to manage the creation and modification of digital content. The most popular CMSs have an open-source code and offer the possibility to install additional components, namely the plugins. These two features allow to customise the CMS, adapting the structure to the requirements of the research. Although the choice of different CMSs, the presentation of research products tends to a standardised model in order to simplifying the fruition and dissemination of results. As previously mentioned, the CMS selected for the present study is Omeka. The latter is a free, opensource digital publishing platform designed by the Roy Rosenzweig Center for History and New Media at George Mason University2, with specific reference to the requirements of museums, historians, and educators. Through Omeka, digital collections, archives, oral histories and essays can be organized and shared as online exhibits and tagged with international standard archival metadata. Archivists, librarians, educators, historians, curators, and other scholars can apply this user-friendly, flexible, and collaborative platform to the research, while teachers can use Omeka to create class projects of source analysis and web design (Kucsma et al., 2010). Users can install the software for free on their own servers or use a hosted version on Omeka website. Hosted options allow users without technical expertise to use Omeka. The software requires a server running LAMP: the Linux operating system, Apache web server, MySQL database system, and the PHP programming language. A small server hardware is required: 1 GB of RAM and a 1 GHz processor is recommended, with necessary hard drive space depending on the size of digital exhibits. Despite the installation is not difficult, and a step-by-step guide is available on the website, Omeka needs some IT skills to be installed on a web server. The user can choose both premade design themes and customised ones. Items can be organised into collections with narrative sidebars. The basic functions of Omeka are uploading digital objects, adding metadata, and organizing objects into collections or exhibits for public display (Puckett & Leslie, 2016). Any Omeka item could include multiple files described by Dublin Core metadata with a large number of customizable elements. In spite of the default item types cover most use cases, new types can be added. The visual elements can be customised by colour, design, font, and header image. As with other CMSs, Omeka functionality can be extended with plugins. For instance, some plugins connect Omeka to other sites to upload audio files or images; other plugins provide additional features like vocabulary enhancements, metadata harvesters, bibliographical models, or Dublin Core extended versions. For historical studies, the plugin Neatline is particularly interesting. The latter allows Omeka items to be plotted on a map - Google Maps, OpenStreet-Map, or custom image-based maps - and on a timeline (Puckett & Leslie, 2016). Neatline provides users to exhibit items geographically or chronologically in interactive display. Designing an Omeka project requires a planning of the creators: specifics about metadata vocabulary, thematic or chronological organisations, etc. (Hardesty, 2014). Thereafter, uploading items does not require specific technical expertise or digital projects experience. Similarly, uploading files is simple as clicking a standard upload button, and multiple files can be added to the same item. Metadata are filled in through a simple web form, and the Omeka implementation of Dublin Core is suitable for every 750

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vocabulary. Once items are uploaded and metadata are assigned, items can be tagged as related to a collection and organised into exhibits. The maintenance and the update of an Omeka framework could be easily made by any user even without specialised technical skills.

“Migration and Internal Colonization in the Early Modern Mediterranean”: An Example of Omeka Project Omeka has been tested on a number of projects with regard to exhibitions of digital library collections (Scheinfeldt, 2008; Kucsma et al., 2010; Pérez, 2011; Alcaraz-Martinez, 2012; Hardesty, 2014; Rath, 2016) and teaching (Marsch, 2013; Rosenblum et al., 2015; Saunders, 2015; Hoelscher, 2019; Thornhill, Gaede, 2019). In 2017, the Department of History, Cultural Heritage and Territory at Cagliari University started “Migration and internal colonisation in the Early Modern Mediterranean”, a digital humanities project which employs Omeka in relation to Modern History within the Mediterranean3. In the early modern period, European states planned and implemented internal colonisation policies to promote the demographic, agricultural and commercial growth of their own territories (Salice, 2017). Among 16th and 17th centuries, foreign colonists were the most favourite settlers involved in such policies. To date, most of studies on the topic refer to single episodes of territorial reorganisation and internal colonisation. Conversely, it can be observed a lack of overall synthesis of such phenomena, which analyses single episodes within a global and comparative framework. “Migration and internal colonization in the Early Modern Mediterranean” attempts to fill the above-mentioned historiographical gap. The macro geographical area and the individual places are digitally manufactured through Neatline, the Omeka plugin designed to translate historical data into space (Nowviskie et al., 2013). The geographical area built with Neatline can be integrated with the current territorial technical maps furnished by local and national administrations. The data are organised into thematic shapefiles (e.g. toponymy, land use, orography, hydrography, road system, administrative divisions, etc.), which can be overlapped on the Neatline map through georeferencing (Salice, 2017). Thus, relating directly to the environment which influenced historical and social events, the scholar can arrange a higher harvest of research information compared to bibliographical and archive research. The information provided into space can be ordered simultaneously through a timeline, which allows the consultation of the map materials in a chronological order (Salice, 2017).The first step for the “digital colonisation” of Neatline territorial areas begins by populating the virtual colonies through the bibliography: each settlement/colony hosts the segment of references related to his story which is, at the same time, part of a general bibliographical database with regard to the internal colonisation in the Mediterranean area (Salice, 2017). The bibliography database is built with Zotero plugin, open-source software which collects, organises and shares bibliographic references4, to organise the bibliography both by territorial area and historiographical theme, for instance the internal colonisation and the borders, or the concept of foreigners as well as commercial networks and diasporas (Salice, 2017). Besides bibliography and digitised documents, the virtual locations within the digital cartography can include every multimedia object which can improve the understanding of the territory considered (e.g. photographs, audio videos, reliefs, etc.). Each object is individually metadata with Omeka and placed in the related settlement. Successively, after being virtually defined on the electronic map, each colony is progressively populated with the data and information concerning its history, arising from bibliography, archival documentation and other disciplinary approaches to its study. The digital space is used both to reordering research data and to store all the materials needed to answer the research questions (Salice, 2017). At the micro scale, the map is structured to accomplish different 751

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tasks: firstly, it retains documents’ references which allow the reconstruction of debates and negotiations in the preparatory phase of settlements; secondly, the map is used to store information about the social profile of the promoters and the settlers involved; lastly, the digital cartography highlights any resistance unleashed by the natives against foreign settlements. On a more distant observation scale, the digital map of the inner colonies allows to trace and visualise the links between different settlements and human groups that circulate within the Mediterranean space. The individual case studies can be read within a system of cross-border relations and measured in comparison with similar initiatives promoted by states in other border areas accross 17th and 18th centuries (Salice, 2017). Indeed, once included within in the digital environment, the Sardinian case study can be visually linked to the colonies founded in other countries, since in some cases the documentation highlights how Sardinian settlement projects are the landing place of dispersed route within the Mediterranean. The digital tools shed light on the complexity of the topic allowing to analyse, for the first time, the Sardinian population strategies compared with other states’ experiences (Salice, 2017). As described, the digital mapping of the internal colonisations integrate the single study projects within a broad problematic framework, extended at least to the Mediterranean area. Omeka allows to organise the research materials and to work on the individual contexts in a comparative way, even changing, if necessary, the scales of analysis. Moreover, the map and the digital context encourage collaborative work among different skills and disciplines. The latter is an essential issue to deal with the study of history and places in a multidisciplinary approach as well as to build operational links between different methodological frameworks (Salice, 2017).

Case Study: Association of European Historians (AsE) Taking cues from “Migration and internal colonization in the Early Modern Mediterranean”, the author is applying Omeka-S to the study of the Association of European Historians, a network of historians from several European and non-European countries founded in 1983 which developed an innovative methodology for the study of European history. Indeed Europe, as longly discussed within the historiographical debate, is essentially an idea. The different “histories of Europe”, from the 18th to the 21st century, have been written in different contexts which have influenced their main features (Verga, 2004). Thus, religion, politics, and culture have profoundly oriented the historical writing of European history, mostly made through a Euro-centric perspective (Osterhammel, 2015). Nevertheless, “which” Europe? It is frequently presented as the sum of national narratives and cultures referred to different spatiality. Modern national narratives in Europe emerged in the late 18th and in the early 19th century, as a result of the political crises caused by the French revolution (Berger et al.,2002). The 19th century showed the rise of national histories which tended both towards national homogeneity and closure, without ever being entirely successful in achieving either (Berger, 2006). In the first half of the 20th century, the concept of national history suffered from successive crises in the wake of the two world wars. The First World War marked the first crisis of the “classic” paradigm, made obsolete both by the tragic consequences of nationalism and by the new, plural European scenario which emerged from the defeat of the Central Empires. In that period historians conceived Europe as a mix of complex histories, composed by states as well as peoples and regions which claimed the right to have their own history (Verga, 2006). Considering the historiographical debate of the period, it can be affirmed that history of Europe was a history in movement, deeply sensitive to changes in the political context. Nevertheless, the prevailing methodology remained the history of the nation-states in Western 752

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Europe (Galasso, 2017). Consequently, national histories found its apogee in several hyper-nationalist narratives between the two world wars (Berger, 2006). In the second half of the 20th century it can be observed some movements towards postnationalism; however, historiographies and historical writing were still firmly structured along national lines (Judt, 1997). During the first years of the second post-war period, the European historiography facedthe specific reduction of Europe in the world balance, as well as the phase of division among western and eastern areas. The “Iron Curtain” made clear the separation argued in the European culture since the 18th century (Wolff, 1994). As a result, Hayes (1945) and Halecki (1950) introduced the concept of “Contemporary History of Atlantic Europe” as the end of European history, resolutely projected towards the Atlantic Ocean and the United States of America (Salvatorelli, 1961). From the 1950s until the fall of the Berlin Wall, Atlantic Europe has been the main framework for European historiography (Verga, 2006). Considering the above, the Association of European Historians (AsE) proposed a paradigm change. Formally founded in 1983, the AsE was a network of historians from several European and non-European countries. The AsE was inspired by Italian scholars like Giuseppe Barone, Alberto Boscolo, Gabriele De Rosa, Franco Sartori, Armando Saitta, as well as historians of international renown like Heinrich Lutz, Eloy Benito Ruano, Albert Soboul (Guerra, 2018). The seat of the association was established at the Istituto Storico Italiano per l’Età Moderna e Contemporanea in Rome, chaired by Armando Saitta. According to a growing need of European historians and institutions, Saitta promoted the Association as a meeting point for historians from all over Europe and above, towards the “overcoming of nationalistic prejudices which still hinder a proper understanding of the history of Europe and the promotion of historiographies integration in different European countries” (Saitta, 1981). Within this historical perspective, resistance to Nazism and Fascism represented the first form of European language, and the civil commitment of historians was to impart the latter concept to new generations. The AsE built its historiographical proposal grounding on a pacifist ideal closely linked to the European revolutionary period: Rousseau and Robespierre interrupted the linear progress of the Enlightenment and made possible the transformation of peace into a concrete political objective (Guerra, 2018). The latter could only be achieved through the struggle against tyranny, as witnessed by the opposition to the French monarchy and to the European counter-revolution. Thus, the AsE’s historiographic elaboration caught a paradigm change in the European project connected to the French Revolution of 1789 and the three-year period of Jacobinism in Italy (1796-1799). The European historians were conscious involved of the difficulty in rethinking history of Europe without any nationalist logic or political subordination. Nevertheless, it can be observed their necessity to define a methodology for a collective history of Europe instead of a sum of individual national cultures (Simoncelli, 2013). The historical paradigm of the Association was confirmed by Article 1 of the Statute (1983): “The Association of European Historians has the whole of Europe within its own reach, meaning that spiritual patrimony is already reflected within itself. The ideal and the culture constitute the identity of the European continent vis-à-vis with other continents”. Within the project of the Association, strongly innovative for the period, hundreds of historians met first in Rome in 1983 for a congress on Europe; then, in Pisa in 1989 for the bicentenary of the French Revolution; and finally, in Palma de Mallorca in 1992 for the five hundredth anniversary of the Discovery of America. The AsE’s paradigm is poorly debated within studies on the 1980s historiography. The analysis of some historical trends in the European debate is accompanied only by sectoral and specialist studies, like those on individual personalities or countries, which highlights a lack of overall reconstructions. What members did the AsE involve? How was the AsE organised? Who were the most active scholars 753

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and in which field of study? How did they collaborate? What was the AsE’s role in the historiographical debate of the period? The author has applied Omeka to answer these research questions.

APPLYING OMEKA-S TO THE ASE The author had the opportunity to test Omeka software at Lu.Di.Ca., the digital humanities laboratory of the Cagliari University, within a workshop in May-June 20205. In Lu.Di.Ca. researchers and students of humanities can experience the integration between methodologies of humanistic research and digital technologies. Participants learn to digitally organise the data emerged from archives, bibliographies, and multimedia on case studies in order to improve the analytical potential of research. As a result, participants are able to produce digital objects and to publish informative texts for the dissemination of results in both scientific and educational fields. The Lu.Di.Ca. group was composed by aspiring historians, archaeologists, art historians, philologists, paleographers and archivists. The goal of the author’s collaborative workgroup was the creation of an open-source digital support using Omeka-S software, a semantic extension of Omeka, for ships and vessels in the Mediterranean during the ancient regime. The results of the project can be free accessed on Lu.Di.Ca. website6. Given the above, the skills acquired at Lu.Di.Ca. have been applied on the author’s PhD project in relation to the AsE. Currently, the sample includes 285 Italian members, 295 foreign members from 22 countries, 1.200 letters, 44 publications, 12 scientific journals, 62 universities and research centres, 4 conference proceedings. Following a preliminary study of the scientific literature on the topic, the archival survey has been the primary research methodology. The author has examined the AsE’s research fields, as well as its scientific production, its members and its research topics within Italian and foreign archives. The following archives have been consulted: the archive of Istituto Storico per l’Età Moderna e Contemporanea in Rome, official seat of the AsE; the Archival Centre of the Scuola Normale Superiore of Pisa; the National Central Library in Rome; the archive of Giunta Centrale per gli Studi Storici in Rome; the Historical Archive of the European Commission in Florence. On the basis of the documents collected, the author has analysed the network of AsE’s historians using the network analysis software Palladio. The latter is an open-source software for historical network visualiSation and analysis7. Palladio allows the examination of the AsE’s nodes - the members and their home countries, universities and research centres - and edges - letters, publications, researches, meetings, relationships among members. The network analysis has highlighted the role of every node in the AsE, the level of participation and involvement in the project, the communication method, the arrangement of the edges in AsE’s network. To manage the huge mass of information, the author has produced a digital library and a digital cartography of the AsE’s members and activity using Omeka-S, an extended version of Omeka software released in 2012. Such use of spatiality is suggested by the current debate on the spatial turn, which aims at identifying the spatial perspective as an essential issue for the development of human society and considers the spatial criteria necessary for historical analysis as much as the chronological one (Arias, 2010; Middell & Naumann, 2010). The first step towards the digitisation of territorial areas has begun with the description of AsE’s members. For every member, the author has created a digital card composed of a short biography, the main scientific production in relation to the AsE, the reference bibliography, hyperlinks, and multimedia elements. The bibliography database is built in APA style with

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Figure 1. AsE’s biographical card example

Zotero plugin. The digital description of AsE’s members allows a close reading of each scholar involved, the academic background which led to the AsE, the involvements in universities and public institutions. The geographical area built with Omeka-S is connected through georeferencing with the information derived from archival research and network analysis. The information provided on space are ordered simultaneously through a timeline, which allows the consultation of the scientific material using both chronological and spatial criteria. The timeline has made possible to relate directly the database to the historical and social events of a given place. This element allows a distant reading of the AsE’s structure, which has shown different scenarios over time: firstly, the growth of the Association during the 1980s, in the period of concerns about the European identity crisis and the barriers in the promotion of the European Community institutions; secondly, the strengthening of edges and nodes in the Soviet bloc with the approach of the fall of the Berlin Wall, an essential feature considering the “western” hegemony in the history of Europe until 1990s; finally,the decreasing of AsE’s activity after Saitta’s death in 1991, which suggests a fundamental role for Saitta and the Istituto Storico per l’Età Moderna e Contemporanea within the AsE’s framework. Once virtually defined on the electronic map, each member card has been progressively implemented with the data and information arising from bibliography, archival research and other disciplinary approaches. Besides bibliography and digitised documents, the virtual locations of digital cartography host the multimedia objects found during the research. Each object has been individually metadata and positioned in the proper card/place. At a micro scale, for every member the map is functional to

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Figure 2. AsE’s cartography with timeline in 1983

Figure 3. AsE’s cartography with timeline in 1983

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Figure 4. AsE’s cartography with timeline in 1993

reconstruct the location, the activity within the AsE, and the contribution to the scientific production of the Association. At a macro observation scale, the map combined with the timeline allows to trace and visualise the network across time. The digital space has been used to reorder research data and to store the materials necessary to answer the historiographical questions of the research project. In addition, Omeka-S provides an online portal to disseminate the results, which will be used as dissemination output of the research. The study on the Association of European Historians, conducted using Omeka-S software combined with Palladio software, is still ongoing. According to the results of the research, between 1983 and 1992 the AsE was a beacon of the European historical reflection. The presence of members from the whole Europe and beyond, relationships, letters, congresses, and scientific production suggest that the Association was a major centre in the European historiographic context with a different perspective on the history of Europe and the civil commitment of the historians. The AsE gathered a large number of scholars and progressively expanded its field of action. Despite its international dimension, the pivots were Armando Saitta and the Istituto Storico per l’Età Moderna e Contemporanea, where we can observe the most active node, likewise most of the AsE’s scientific activity and production. According to the Association’s historiographical paradigm, the history of Europe should have reflected on the common space and the richness of differences, reconstructing with a new scientific method the complex path of the European identity through convergences, contradictions and conflicts (Saitta, 1981). The digital analysis of the AsE’s network shows the increasing involvement of historians from the Soviet bloc and other continents, in addition to the members of the EEC, to narrate the historical formation of European society in a broad - but committed Euro-centric - perspective. Taking into account the “western” hegemony within the debate on Europe until the 1990s, the study shows as the AsE was a pioneer of European openness in historiography. Referring to the research progress, the digital tool will be used even for a comparative analysis of the AsE’s network with other groups of historians of the 20th century, for instance the Com-

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Figure 5. Test homepage of research’s dissemination website

mission Internationale de Coopération Intellectuelle (1922-1946) within the “League of Nations” and the Comité international des sciences historiques (1926-still active). As already mentioned, Omeka-S allows to overlap different maps through the plugin Neatline, a function which could be employed to compare the AsE’s structure with similar networks both through close and distant reading.

CONCLUSION AND PERSPECTIVES In relation to the AsE, it can be concluded that digital technologies are providing decisive support to traditional research methodologies. The use of Omeka-S is functional to manage the large amounts of data, information, documents and multimedia contents related to the AsE and organise them to accomplish the research questions The digital infrastructure allows to order and connect information on different media, to store data in upgradeable deposits and to arrange materials in different logics, both along a timeline or inside a cartographic space. The possibility to visualise data in these forms modifies the perspective of the research: in the digital context is possible to integrate information with elements which are not detectable in the analogic documentation. Omeka offers major possibilities not only referring to online exhibitions or teaching, but even in order to produce digital maps and conduct comparative analysis through different scales. Furthermore, Omeka-S improvesthe research through an orderly collection and a precise organisation of the study materials: bibliographies, archival documents digitised, archival descriptions, and georeferenced maps. Thanks to this CMS is possible to collect, classify and integrate objects even characterised by a strong heterogeneity and dispersion.. Consequently, Omeka-S functions can be exploited even to

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enhance the multidisciplinary collaboration among different actors engaged in research and the expansion of tool range used for the transfer of knowledge. For instance, the research described in this paper will be related and connected to the heritage of the Istituto Storico per l’Età Moderna e Contemporanea Thus, this CMS could be used to develop complex digital infrastructures connecting scholars, stakeholders and civil society, even using the online portal to disseminate the research results. Concluding, digital historians are aware of the urgency of conducting historical research through digital technologies, adding value to traditional methods. In accordance with international standards, the databases can be consulted, updated and used even on software platforms designed to change over time. The adoption of open code programs with free use licenses like Omeka is a decisive step to limit the obsolescence of digital media and consequently the risk of losing the results of historical research. The application of Omeka-S to examine networks like the AsE highlights how it ensures a satisfying level of historical research methodology, as well as a solid structure for visualisation and dissemination of results. The software guarantees a complete digital narration, at the same time providing a digital environment for mapping, relate cartographies to timelines and conduct comparative analysis both in close and distant perspectives.

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KEY TERMS AND DEFINITIONS Close Reading: Critical analysis of a text or object which focuses on significant details or patterns in order to develop a deep, precise understanding of the text’s form, craft, or meanings. Content Management System: Computer software used to manage the creation and modification of digital objects. Digital History: The discipline which refers to the use, study and processing of digital tools applied to historical research. Digital Humanities: The discipline which refers to the study, research and teaching through the intersection between electronic computation and human sciences. Distant Reading: Critical reading methodology that uses big data analytics and computer programs for the research purposes. Network Analysis: Set of integrated techniques to depict relations among actors and to analyse the social structures that emerge from these relations. Plugin: Additional component which enables software customisation with specific features.

ENDNOTES 3 4 5 6 7 1 2

https://chnm.gmu.edu/ www.omeka.org https://storia.dh.unica.it/colonizzazioninterne/ https://www.zotero.org https://ludica.dh.unica.it https://ludica.dh.unica.it https://hdlab.stanford.edu/palladio/

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Chapter 42

Gender Democratization:

A Content Analysis of the Social Media of Two Social Promotion Associations Giuseppe Masullo University of Salerno, Italy Angela Delli Paoli https://orcid.org/0000-0002-1463-2573 University of Salerno, Italy Sara Tomasiello University of Salerno, Italy

ABSTRACT Misogyny and gender violence are the result of social and cultural predetermination of gender roles. Indeed, eradicating role prescriptions is a real revolutionary action which implies escaping from male and masculinity hegemony and rethinking male identities. It is therefore crucial to create pathways of democratization of gender that aim to create real paths of equality between genders abandoning the social predetermination of gender roles. This challenge has become the goal of some social promotion associations which try to break down gender-based violence and the stigma attached to it. The chapter aims to investigate how these associations operate to democratize gender through a content analysis of messages posted on their respective Facebook pages. The unit of analysis of the study is constituted by the last 200 posts produced in these two Facebook pages for a total of 400 posts analyzed. Findings identify renewed spaces of masculinity (for example fatherhood) not adhering to the main stereotypes.

INTRODUCTION By recognizing that the body is not a given, but a variable construction, politically and culturally regulated within a field, Judith Butler (1990/2007: 212), a post-structuralist philosopher, asks which language can be used to understand gender in its interior signification. DOI: 10.4018/978-1-7998-8473-6.ch042

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We use the above question as a cue and starting point to delve into a deeper understanding of the hierarchical systems of power that affect gender by prescribing a precise political and practical regulation of the signification of bodies. The analysis of these mechanisms helps us to understand the incarnations of misogyny and its structuring. Both genders acknowledge the existence of misogyny. However, parts of the phenomenon are less clear – namely, its visible and invisible modalities, the tools (legitimate and illegitimate) and arbitrary processes that fulfil this hierarchizing process. Feminist studies show that the gender power system subjugates women to men (Stark 2009), providing a constant and systematic prevarication of the latter against the former. As Toffanin (2019: 8) points out, the effects of violence is strictly linked to the structures of relationships in which they are produced, thus making visible the asymmetry of violence and its gendered dimension in family, domestica and intimate life. The findings of post-structuralist sociology on gender and its power relations (Corradi 2011) are pivotal. Several questions need to be answered. First, starting from a macro analysis: How is this system of domination carried out? The philosopher Pierre Bourdieu (2002) provides an essential contribution in “Masculine Domination”, in which he well explains how it comes to be constituted and legitimated. It is necessary to investigate both these processes, particularly the former, and therefore the invisible and apparently “natural” way in which it is expressed and consolidated. As Bourdieu explains, the strength of the masculine order is visible in its neutrality: it does not need to be justified, impose itself as neutral and has no need to spell itself out in discourses aimed at legitimating it. Within this perspective, it can be seen as a social order, a symbolic machine which is aimed to ratify the masculine domination. As he points out, to understand said invisibility, it is essential to understand both its real causes and its concrete consequences. This legitimation mechanism of male dominance is sanctioned by the incorporation of social roles by both genders, considered “natural” in the symbolic world. As a consequence, the androcentric representation of biological reproduction and social reproduction is invested with the objectivity of a common sense, a practical, doxic consensus on the sense of practices. And women themselves apprehend all reality, and in particular the power relations in which they are held, through schemes of thought that are the product of embodiment of those power relations and which are expressed in the founding oppositions of the symbolic order (Bourdieu, 2002: 33-34). If this arbitrary representation is internalised by both genders, who then act naturally according to their predefined roles, this means that not only men contribute to the ratification of this domination, but women are also complicit in this construction of social signification, albeit unconsciously (Bartholini 2013). By consolidating as a necessary regulation, this dynamic becomes a founding order and is perceived as pertinent, thus making legitimate the dichotomous dominants/dominated (men/women) society. The true ontological representation of domination shows us a system of male prevarication that is not only immanent in society but also approved. As Bourdieu (2002: 35) points out symbolic violence is institutionalized by the acceptance of the dominated who adhere to the cognitive instruments of the dominant (and therefore to the domination) in shaping her thought of him, herself and her relationship with him. We can thus state that male dominance is depicted as symbolic violence because it manifests its system of force through the recognition provided to it, and it feeds on this to exist. Connell (2011) calls this whole system of social incorporation and role predetermination the “reproductive arena”. The sociologist explains that “Gender is that structure of social relations that is centred 765

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on the reproductive arena, and that set of practices that make reproductive differences of bodies part of social processes” (Connell, 2011: 128, personal translation). Connell is keen to point out that this imposed relational structure is arbitrary and that “Being a ‘man’ or a ‘woman’ is therefore not a predetermined condition, but the result of a becoming, a being that is always, actively, under construction” (Connell, 2011: 39, personal translation). The social order functions as an immense symbolic machine tending to ratify the masculine domination on which it is founded. From such evidence, we can answer the second question, namely: what are the roots of this dominance? Its existence is rooted in the ever-present dichotomy between male and female, and in the categorization that prescribes roles and attitudes to both genders according to biological sex, equalling it to gender identity. As Butler (1990/2007: 10]) points out: The presumption of a binary gender system implicitly retains the belief in a mimetic relation of gender to sex whereby gender mirrors sex or is otherwise restricted by it. When the constructed status of gender is theorized as radically independent of sex, gender itself becomes a free-floating artifice, with the consequence that man and masculine might just as easily signify a female body as a male one, and woman and feminine a male body as easily as a female one. Overcoming such social stratification requires to overcome the dichotomy because “when the relevant ‘culture’ that ‘constructs’ gender is understood in terms of such a law or set of laws, then it seems that gender is as determined and fixed as it was under the biology-is-destiny formulation. In such a case, not biology, but culture, becomes destiny” (Butler 1990/2007: 12). What is this destiny? A cultural cage that harnesses bodies in pre-constituted modes and prescribes appropriate behaviours for either gender, automatically determining specific forms of masculinity and femininity. In Bourdieu’s words (2002: 8): The division between the sexes appears to be “in the order of things”, as people sometimes say to refer to what is normal, natural, to the point of being inevitable: it is present both – in the objectified state – in things (in the house, for example, every part of which is “sexed”), in the whole social world, and in the embodied state in the habitus of the agents, functioning as systems of schemes of perception, thought and action. On this issue, Connell’s analysis of the construction of masculinities is particularly relevant. The Australian sociologist investigates how they are constituted and structured and how they learn to act depending on the model they incorporate. In a male-dominated society, masculinities must necessarily follow certain parameters and they can be configured according to different profiles (Connell, 2011). • • •

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Hegemonic masculinities enjoy full dominance over both femininity and other types of masculinities. It is the ideal standard to which many men aspire, but few manage to embody. Subordinated masculinities submit to the rules of hegemonic masculinity and confirm it with their everyday practices. We can also call them other males. Complicit masculinities fail to embody the role of hegemonic masculinity but, by sharing it, seek personal gain and benefits.

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•

Marginalised masculinities are all those sporting different, minority characters (generally related to a different ethnicity), but whose existence is still authorized by the hegemonic masculinity, who could not exist without them.

This analysis shows how the hierarchical patriarchal society provides for systems of subordination not only between genders but also within the same gender. According to the resulting matrix, the subject who most possesses the pre-established cultural standards can attain hegemony over the rest of the group. Some scholars (Paetcher 2003; West and Zimmerman 2009; Rinaldi 2018) have examined this cultural despotic organization, analysing both its causes and consequences. They highlighted how the embodiment of this role presupposes a necessary homosociality process – a relational construct and, therefore, a product of culture and society. This need to adhere to social configurations is determined by the social demand placed on genders. Were they to disregard such social configurations, they would have to abdicate their social role. Rinaldi (2018: 66) points out how the performative aspect of gender and the practices of gender exhibition force us to prove ourselves male or female every time we are in the presence of others, to avoid reputational losses and status degradation1. The complexity of this hierarchical system, which underpins all misogynistic attitudes enacted every day by both men and women, shows how difficult it is to eradicate it. Being able to understand all the intersecting areas connecting the various social dynamics allows us to understand how the existence of this male domination affects multiple spaces of existence. It is not “merely” a question of gender violence. As Rinaldi points out, several relational systems legitimize this configuration: the concept of hegemonic masculinity, based on Gramsci’s cultural hegemony, can be understood as a complex set of varied and dynamic social practices that reinforce patriarchy and male domination over women and other males basing on its subordinates’ acknowledgement of its legitimacy. Hegemonic masculinity has a relational character, it would therefore have no meaning outside of the relationship with non-hegemonic femininities and masculinities (Rinaldi 2018: 53). It is interesting to examine how the system of male domination has changed over time, while continuing to exist, after the collapse of the main patriarchal values following the feminist revolution. This would also allow us to explore what paths of gender democratisation are necessary for the creation of a society that overcomes misogyny and male hegemony. While feminism has been crucial to achieving a remarkable change in the male-dominated society, to believe that it has eradicated it would be wrong. Feminism allowed for the demolition of classic normative principles to make room for a new vision envisaging different roles for women in the social, political, and economic world (Ruspini 2014). However, this democratisation process caused a fracture in masculine identity. Men, “robbed” of their familiar territories, have found themselves stripped of the habitus in which they identified. The outcome was a “crisis of masculinity”. Institutions underpinning male privilege, control, and authority are questioned – e.g., the role of the breadwinner, deeply linked to the construction of male identity, has disappeared due to the precariousness of the labour market. This change was accompanied by contrasting feelings and reactions. On the one hand, we find an emerging, though not very visible, male desire to break with dominant models and static roles inherited from previous generations, which leads men to experiment, for example, with new ways of experiencing fatherhood, caring for others and their bodies, and critically questioning normative masculinity by distancing themselves from sexist models. On the other hand, revanchist positions are reaffirmed, expressions that convey and interpret male anger and victimhood (Ciccone, Nardini 2017: III) 767

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What resulted from this crisis? Further misogyny, determined by men’s desire to reappropriate their territories of identification, being unable to conceive themselves other than what they have always been, i.e., failing to conceive themselves beyond the virility and power that characterised them (Ciccone 2009). It is therefore crucial to pursue gender democratisation policies aimed at creating a real equality between genders. Which democratisation paths are necessary to overcome the gender dichotomy? This brief theoretical framework underlines that the democratisation process must necessarily engage both genders to affect all areas. This process should provide support not only to the dominated but also to the dominant, who can learn to conceive themselves differently from society’s rigid predetermined roles. Gender democratisation must strategically aim at equalising all types of gender relationships, as they are all discriminated against and caged in their pre-assigned role. It must emancipate not only for women, certainly the most visible victims of the system but also men, who must be freed from their culturally assigned territories. Like women, men are subjugated by their assigned roles, although in invisible and non-violent terms. As pointed out by Stefano Ciccone – sociologist and head of the Associazione Maschile Plurale – creating new types of relationships between subjects and abandoning the social predetermination of gender-assigned roles does not mean to depersonalise and flatten identities but rather to create “a relationship of difference, mutual autonomy of path and autonomous foundation” (Ciccone 2009: 217).

METHODOLOGY As we have seen in the previous section, misogyny and gender violence are the result of social and cultural predetermination of gender roles. The system of male domination has changed over time as a result of the collapse of the main patriarchal values following the feminist revolution. Although contemporary and post-feminist society is constantly trying to break down gender boundaries and to change pre-existing power relations, the dichotomous demarcation between male and female is still very present. Indeed, eradicating role prescriptions is a real revolutionary action which implies escaping from male and masculinity hegemony, from cultural predeterminations that harness both genders in specific role prescriptions and rethinking male identities. It is much broader than a superficial subversion and must entail a cultural change affecting all the identities involved. It is therefore crucial to create pathways of democratization of gender that aim to create real paths of equality between genders abandoning the social predetermination of gender roles. Such democratization implies the recognition that it is possible to be men with other habitus, different from those always known, and that this change does not lie in a loss, but in an enrichment. Men should learn to configure oneself according to multiple, stimulating, enriching identities that go beyond stigmatizing concepts, precise and clear borders that force subjects within boundaries. This means going beyond identity spaces traditionally assigned to masculinity, those of strength, virility, power, prevarication, hierarchy. This challenge has become the goal of some social promotion associations which try to break down gender-based violence and the stigma attached to it by addressing both the prevaricated, and the prevaricators, to carry out an action of democratization that can reach all subjects regardless of their gender location.

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The paper aims to investigate how these Associations operate to democratize gender through a content analysis of messages posted on their respective Facebook pages: Maschile Plurale (Masculine Plural) and Maschi che si immischiano (Males who Meddle). We chose these two associations because they are among the few in Italy to deal with the gender dichotomy and misogyny from a masculine point of view rather than merely a feminine one. In other words, they believe that the problem of gender discrimination affects not only women but also men. Our empirical research follows Rogers (2010), who suggests considering digital environments not as a specific object of study, but rather as a methodological resource useful to collect data and information about the social actors that operate within these spaces, their interactions, and forms of sociability. Maschile Plurale was founded in 2007 in Rome by a heterogeneous group of men by age, political, and cultural background interested in producing critical and practical reflections on the redefinition of male identity, which should be able to recognize itself through a plural and critical model, overcoming the patriarchal model. Its Facebook group, “Spazio aperto promosso da Maschile Plurale” (Open space promoted by Maschile Plurale) shares different kinds of contents to promote a change in male identity. Its main objective is the abolition of gender violence as a modus operandi typical of hegemonic masculinity. To do so, the Association stresses that men must join this battle alongside women since they appear more prone to engaging in violence to reaffirm their virility, weakened by the crisis of patriarchy. Maschi che si immischiano is a non-profit association based in Parma and founded in 2016 from an independent branch of Maschile Plurale. It is a local association, less structured than Maschile Plurale, but with the same goal, namely the redefinition of gender in society, to eliminate any kind of violence caused by patriarchal domination and power. It is an informal and spontaneous group, born after the city of Parma was the theatre of several femicides in recent years. The Association presents itself as a tool to combat this phenomenon, putting at the forefront no longer only women but also men – and self-aware ones. It promotes several activities in the territory and uses also social platforms to raise awareness on the issue. Its eponymous Facebook page promotes content addressed to both genders and aimed at creating effective gender democratisation. The purpose of our investigation is to understand what content is promoted online by these two associations to make gender democratisation pervasive. From a methodological point of view, we adopted the content analysis approach and tried to answer the following questions: Through what modalities and what contents does the gender democratisation take place online? How do the movements promoted by the two associations contribute to this phenomenon? Content analysis consists of translating textual information – be it single terms, two or more terms, or complete sentences – into valid data for social research and/or political decision-making (Arcostanzo, Pansardi 2017). It is based on the interpretation and classification of texts using a variety of sometimes competing and contradictory procedures (Rositi 1998) to infer from the texts their meanings and contexts of use (Krippendorff 2013: 24). This method ascribes texts to a limited number of categories through explicit analytical decomposition, classification, and coding procedures (Weber, 1990). Our unit of analysis consists of the last 200 posts in the 2 above-mentioned Facebook pages, for a total of 400 posts analysed. We adopted a deductive coding procedure. We coded the posts based on an a priori classification by the researchers and a coding scheme2 based on the conceptual map and literature review of previous research in the field. We pre-tested the coding scheme on 50 posts to verify the reliability of our tools. The coding and interpretation procedure, both in the pre-testing phase and in the actual analysis phase,

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involved three analysts, to effectively assess the quality of coding by ensuring adequate intercoder reliability3. To answer the main research questions, we examined the following dimensions: 1. Target audience. This involves understanding who the message is aimed at by analysing the prevalent recipient. This dimension allows us to understand to what extent the issue of gender democratisation is considered the province of women (as is generally the case), men or both. 2. Gender-based Violence. This refers to posts dealing with gender violence and helps to understand the extent to which communications and news about gender-based violence are disseminated to bring gender democratisation to life. 3. Gender considered from a social perspective. These posts consider gender dichotomy not limited to concrete and visible violence, but a broader issue that affects different spheres of people’s lives. 4. Gender considered from a political perspective. These posts deal with the theme of gender as the focus of political debate and a change-inspiring agent. They also support policies aimed at removing forms of social and economic inequality between women and men. 5. Gender and non-normative sexuality. These posts raise awareness for sexual self-determination and the existence of non-normative genders. They frame gender beyond the confines of traditional man/woman binomials and sexuality beyond homo/hetero categories. Gender is the precipitate of influences and conditioning that are rooted in culture before (and rather than) in nature. 6. Intersectional posts. These refer to posts dealing not with themes directly related to gender violence but with borderline issues, addressing a broader and more complex discussion on the subject: gender intersects with themes related to disability, ethnic and racial belonging, social class, and sexual orientation (Yuval-Davis 2006).

HOW DOES GENDER DEMOCRATISATION COME ABOUT? The content analysis highlights the topics most conveyed online by the two associations to promote gender democratisation. In turn, this allows for the understanding of their chosen strategies to achieve this goal. Overall, gender-based violence appears to be the most discussed topic, followed by the issue of gender considered from a social perspective. In both groups, most of the posts provide information, communications, news, or statistics on gender-based violence. This is particularly true for Maschi che si immischiano (fig. 1). The post represented in figure 2 is emblematic of gender-based violence (fig. 2). Maschi che si immischiano also gives greater importance to posts dealing with gender from a social perspective. See, for example, the post of figure 3 (fig. 3). Conversely, the topic of non-normative sexualities is voiced exclusively by Maschile Plurale, which pays more attention to intersectional issues. The post of figure 4 is a representative post for the category on non-normative sexualities (Fig. 4). As can be seen in Figure 1, Maschi che si immischiano dedicates no space to the dimension of gender and non-normal sexualities. These first bite-sized analyses allow us to discern the intentions, although merely at a superficial level, of the two organizations and their respective paths. First, the theme of gender violence is treated very differently: it prevails over other issues in Maschi che si immischiano but it enjoys just a minor 770

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Figure 1. Posts content n=400

space in Maschile Plurale. Although gender violence is certainly an issue of great importance for gender democratisation, it is not enough to address the issue fully and properly. It is necessary to also consider the marginal and invisible aspects. Gender-based violence is addressed more often and more extensively because of its high social consideration and the degree of interest it arouses since it is more visible. In other words, one needs no great empirical knowledge to critically observe this issue and it succeeds in raising awareness in a high number of recipients. This also explains why it is treated differently from a quantitative point of view, confirming the different structuring of the two associations and the resulting consequences. For gender democratisation, gender issues from a social perspective are equally fundamental and as such are treated by the two associations. However, they pertain to a more refined level of analysis. The importance of this issue shows how gender democratisation lies not only in eradicating the visible violence between genders but also in overcoming the roles, the predeterminations that delimit and define them, in considering violence not only physical and visible but also symbolic and invisible (Bourdieu, 2002). The data on gender and non-normative sexuality should not be underestimated. Again, this confirms that the two organisations have different structures and chose different empirical paths. The topic of gender and non-normative sexualities remains often marginal in gender democratisation because it stands at a much deeper and more complex level of analysis – possible for a Facebook group much more than for a simple page. The very fact that the Maschile Plurale group calls itself an “open space” shows the communicative intentionality aimed at confrontation and exchange, among members, on issues that often could remain invisible in a more structured space. This openness stems from the very purpose of the group: to give voice to unheard aspects.

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Figure 2. Post on gender-based violence

Regarding intersectional issues, we should consider the meaningfulness and globalising power of this type of content. Indeed, while not directly linked to gender, these posts develop in the same direction and are connected by the same thread: diversity, discrimination, power, domination, virility, self-denial. Therefore, the perspectives of domination related to gender should be conceived as resulting from a broader scenario in which power prescriptions are inscribed in all areas in which categorization separates legitimate from non-legitimate areas, tracing the spaces of the prevaricators and those of the prevaricated, according to the same logic of domination and power that harness bodies in gender prescriptions. When these groups deal with intersectional issues, they are considering the change from a much more complex perspective, associating it with a categorizing mindset that affects several other areas.

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Figure 3. Posts related to “Gender from a social perspective”

Categorization is a function that man uses to order the world according to his own hierarchies, with the aim of sectorising, distinguishing, creating boundaries, and then extrapolating equalities and differences. On these, he creates his cosmological vision of existence, as being-in-the-world, through which he can feel present to himself, being one and not the other, this and not that: this as something near, that as something far away. As Rinaldi explains, social categorisation “holds a practical and procedural function, it concerns the local and contextual organization of ordinary natural activities (indexicality) and the same activities being ordered, explicable (reflexivity)” (Rinaldi 2016: 76).

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Figure 4. Posts with content related to “Non-normative sexualities”

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One of the posts that fully represents the social significance of this dimension is shown in Figure 5. The image shows the face of Zozibini Tunzi, a South African model crowned “Miss Universe” in 2019. The model, although objectively beautiful, does not fall into the legitimate social categories of beauty for her skin colour and masculine haircut. This post perfectly represents Rinaldi’s concepts of indexicality and reflexivity.

CONTENTS AND DIRECTION OF GENDER DEMOCRATISATION Part of our analysis was aimed at understanding the recipients of media content. We deemed it important to analyse not only “what” is published but also “how” – that is, to whom the messages are addressed. The recipients of the messages affect their perspective of action which points out how the change, to be all-encompassing, must touch on all aspects of gender. This changes the contextualization and direction of the issues. For a long time, issues of gender discrimination have been considered exclusively female competence, involving only women in the battle for democratisation and thus achieving minor and partial results. To bring about a change, it has been crucial to consider men as equal protagonists of this battle, being trapped in forced categorization as much as women. To this end, it is important to understand how these democratisation organizations move and how much of their content is aimed at one or the other gender. To clarify the recipients, we carried out an in-depth analysis of the posts’ content, to understand whether the call for change was addressed to men, women, or both. In the posts analysed, the protagonist changes depending on the message that is conveyed and how it is expressed. Sometimes, they follow the viewpoint of the “dominant” who wants to change and asks for tools to implement such change. Other times, the narrating voice is that of the “dominated”, the woman, who claims the need for re-evaluating the gender roles and consequent predeterminations. Some examples are given below. Considering all the posts, regardless of the groups, female recipients prevail. On closer inspection, however, the prevalence is for neutral recipients who cannot be classified into gender categories. Maschi che si immischiano posts messages with mostly women as a target audience. For Maschile plurale, instead, messages seem to be more gender-balanced with just a slight majority of female addressees. Most of this group’s posts are neutral and not addressed to an identifiable gender (Fig. 6). To draw a first conclusion, we can claim that the clear differences found in the two groups are partly due to their very structures. The articulation of Maschile Plurale can already be found in its high neutral interaction: its modus operandi conceives the group as an innovative space of exchange, equally based on the participation of men and women. It is the very principle guiding Stefano Ciccone, founding president of Maschile Plurale, who highlights how “Stereotyped roles are a source of suffering and imprisonment for women and men. This does not mean flattening the two experiences into an impossible correspondence or removing the power disparities that characterize them but recognizing the possibility and the need for a transformation of these roles by men and women” (Ciccone 2009: 159-160). Overall, content related to gender-based violence is not articulated by gender, as shown by the prevalence of neutral or not identifiable recipients. Conversely, content related to the social dimension of gender is predominantly directed at a male target audience. The few posts that address gender from a political perspective have a predominantly female target audience. Intersectional themes and those related to non-normative sexualities do not appear to be divided by gender, being addressed mainly to a target audience that cannot be identified according to gender categories and/or orientations (fig. 7). 775

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Figure 5. Posts with intersectional content

Translation: I grew up in a world where a woman with my looks, with my skin and hair, was never considered beautiful and I think it’s time for that to end today

The target audience changes depending on the group observed (fig. 8 and 9). However, the posts appear to be similar in terms of gender from a political perspective, targeting an exclusively female audience in both groups. Although our observation is quantitatively limited, this suggests that gender democratisation at the political level concerns mostly women and does not enjoy particular attention from men. It is an issue close to women’s hearts since the ensuing checkmate affects exclusively women. Figure 10 shows some of the most important years that sanctioned gender democratisation at the political level. Note how the post addresses an explicitly female audience, particularly those women who do not recognize the importance and concreteness of the fundamental rights of gender equality.

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Figure 6. Target audience

The choice of an exclusively female target may appear to minimise the complexity of the issue. However, it reminds us how the male-dominated society is built and legitimized not only by the prevaricators but by the prevaricated themselves, who unconsciously confirm and uphold such a system by submitting to it. As Bourdieu (2002: 41-42) explains:

Figure 7. Post content by target audience categories (% within gender) n=400

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Figure 8. Post content by target audience in the group Maschile Plurale (% within gender) n=200

Because the foundation of symbolic violence lies not in mystified consciousnesses that only need to be enlightened but in dispositions attuned to the structure of domination of which they are the product, the relation of complicity that the victims of symbolic domination grant to the dominant can only be broken through a radical transformation of the social conditions of production of the dispositions that lead the dominated to take the point of view of the dominant on the dominant and on themselves. If posts that deal with gender from a political perspective are aimed exclusively at women, those that deal with issues of gender-based violence and gender from a social perspective are otherwise directed. Regarding posts on gender-based violence, both groups show a heterogeneous target audience. The violence perpetrated by men on women is well-rooted and an important topic of which to be made aware. Figure 9. Post content by target audience in the group Maschi che si immischiano (% within gender) n=200

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Figure 10. Post on gender from a political perspective targeted at women

Translation: Until 1946 you could not vote 1963 be a magistrate 1970 divorce 1975 you had a paterfamilias 1978 you could not have an abortion 1981 there were extenuating circumstances for those who killed you 1996 rape offended morals 2012 there were legitimate and illegitimate children Remember that when you badmouth feminists, my friend.

It informs and involves everyone, both culturally emancipated subjects and those linked to patriarchal society. Talking about gender-based violence is the most direct channel to reach all social subjects, being deprecated by everyone, including the perpetrators themselves. What remains relatively uncensored,

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instead, are the underlying mechanisms endorsing its existence. The posts on gender-based violence change in how this message is conveyed, through what information and addressing what specific audience. This changes their directionality and their very meaningfulness. We chose two representative figures of the category that show us the different modality in which the theme is construed as a problem. Fig. 12 addresses the issue from the point of view of the victims. Denouncing the cruel reality of femicides, it addresses all survivors, exclaiming “To remember what? That we are still alive.” The message conveyed is persuasive and necessary to understand what gender-based violence is. However, it does not encompass the full complexity of the battle. Fig. 11 reminds us that to eradicate gender violence we must involve not only women but also and above all men, at the same time perpetrators and victims of a society of dominants. It is necessary to take care of the abusers through a twofold re-educational project, aimed at showing women the boundaries of true and legitimate love and teaching men how to love without having to prove their virility. We must build a new way of thinking that does not consider masculinity as a “binary opposition between ‘good’ men who repudiate their innate penchant for violence and ‘bad’ men who go along with it” (Maragaggia and Giomi, 2017, p.54). Rather, masculinity should be considered as a violent social construction, urging men to learn to be different, to build new social ways of being male, without the fear of losing something that belongs to the pre-constituted role. The data show that this change of perspective is underway, and it is precisely what the organizations surveyed propose. Gender violence is considered a social problem that no longer affects only the female gender, but also the male one. Indeed, men are victims of the system that harnesses them into the role of a prevaricating individual, from which they cannot break free, not being able to be other than what society has taught them. It is, therefore, necessary that society takes care of both, the dominant and the dominated, conceiving them both as victims of a system that imprisons them in oppressive predeterminations. On this issue, Bourdieu compares male privilege to a trap that forces them to demonstrate adherence to their virility in every gesture. Regarding posts on gender from a social perspective, they are predominantly addressed to a male target audience in the group Maschile plurale, while in the group Maschi che si immischiano there is a more equal distribution between the sexes. The results are reported in Figures 13 and 14. Once again, this highlights how the social aspect of the gender dichotomy invests different types of issues because it is transversal to many situational areas. The main objective is to learn to conceive, in line with gender democratisation, sexual characteristics through new perspectives and data that emphasize their possibilities. In Bourdieu’s words, it means being able to conceive new habitus for men and women, that is, to go beyond that “practical construction imposing a differentiated definition of the legitimate uses of the body, in particular sexual ones, which tends to exclude from the universe of the feasible and thinkable everything that marks membership of the other gender” (2002: 23). This is precisely the goal of the posts in Figs. 13 and 14: learning to observe femininity and masculinity from new viewpoints and according to new framings. The post in figure 14 presents women beyond the stigma depicting them as less culturally developed “Women: they lead in education. Gender gap, my foot!”. The post in Figure 13 prompts viewers to use new parameters to conceive of masculinity, going beyond those socially predetermined. The exhortation of the post is enunciative and urges men to learn to conceive, think and determine themselves beyond those predeterminations that cage them in the schemes of the masculine man because it is possible to be something else. To this end, it is necessary to restructure and reconstruct the male identity. 780

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Figure 11. Post on gender-based violence with a male target audience

Translation: I hit her. I don’t want to do that ever again. Are you being violent? There’s always another way. Call us!

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Figure 12. Post on gender-based violence with a female target audience

Translation: to remember what? That we are still alive!

As Stefano Ciccone points out, “this is the difficulty of men ‘questioning’ their differences, especially if, as Cavarero writes, «one does not get out of a way of thinking simply by thinking of getting out of it, at least until that thought of getting out is structured on the same categories of the thought from which it wants to get out» and if they cannot draw on that resource of extraneousness to the order that generates that very thought” (Ciccone 2009: 200). Addressing gender from a social point of view is paramount to change the lenses through which it is observed. The considerable percentage of posts on this issue demonstrates its importance, although the topic is not yet sufficiently present.

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CONCLUSION The main objective of our research was to understand the paths undertaken by male-focused online communities to develop gender democratisation. The group we chose to investigate see men as a fulcrum of social change. The data offer a partial picture that shows that, although the social change promoted by these organisations establishes men as a starting point, the process still enjoys interest and participation from women. Their intentions are unequivocal and concrete: to create new masculinities and new ways of thinking and being men. We can see new spaces of masculinity that no longer use their traditional characteristics – such as violence, power, strength, virility. Instead, they take shape around other features, such as being present parents, balanced partners, men who do not adhere to the stereotypes. These men not only lash out against gender violence but address the problem in a more complex and conscious way, observing the roots and not only the fruits of the problem (Maragaggia, Giomi, 2017). Several examples confirm this, such as a man who describes the page Maschi che si immischiano as An excellent meeting point for all those who are looking for actual news and a place for constructive and proactive discussion on everything related to the issues of the gender gap, violence against women, equal opportunities and consequently also for those who want to deepen their knowledge in the field of gender studies. Mainly for those who want to stubbornly go against the obsolete and demeaning stereotypes of the average self-destructive man. The last sentence well describes the awareness of men who have begun to be part of this new reality, a new way of expressing themselves and new reference models of masculinity. This new way of conceiving oneself leads to the creation of new awareness and a different way of taking charge of the situation, demonstrated for example by a user who dwells on the theme of gender violence and states: Gender violence is OUR problem, of us men. It’s about time we became aware of it, it’s about time we started shouldering it. These comments show an awareness that goes beyond the specific event and is framed instead within a larger, thicker, more complex landscape in which men question themselves before the system, as confirmed by comments like the following: Men have never learned “how to tell their stories,” for years I thought my gender was reluctant to do this out of laziness, I was wrong, it’s something they don’t know how to do, in that place they “lack words”.....and an entire culture doesn’t teach them...... The change may seem slow and often one-way. Groups such as these, however, show the twofold will of men to promote gender democratisation. They advocate fighting the clear signs of the problem, such as violence, and addressing invisible issues, such as the new necessary relational literacy of men, who must learn to conceive manhood beyond the attribute of virility. Creating new territories of masculinity is a fundamental step for a real and all-encompassing change. Groups like those examined here seem to be moving, albeit with extreme difficulty, in this direction. As Butler argues, “the task here is not to celebrate each and every new possibility qua possibility, but 783

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Figure 13. Post on gender from a social perspective with a male target audience

Translation: In the cave you are afraid to enter is the treasure you are looking for Man, what is the cave you are afraid to enter? Changing jobs? Spending more time with your family? Dealing with your sexual frustrations? Manifesting your emotions? Asking other men to be your brothers? Be responsible for your life, Be a conscious man

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Figure 14. Post on gender at the social level with a female target audience

to redescribe those possibilities that already exist, but which exist within cultural domains designated as culturally unintelligible and impossible” (Butler 1990/2007: 148-149).

REFERENCES Arcostanzo, F., & Pansardi, P. (2017). Social media e analisi del contenuto. In P. Natale & M. Airoldi (Eds.), Web & social media. Le tecniche di analisi (pp. 33–46). Apogeo. Bartholini, I. (2013). Violenza di prossimità. Franco Angeli. Bourdieu, P. (2002). Masculine Domination. Stanford University Press. Butler, J. (1990/2007). Gender Trouble. Routledge. Caliandro, A., & Gandini. (2019). I metodi digitali nella ricerca sociale. Carocci.

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Camoletto, R. (2013). Ridere e parlare di sesso: una costruzione plurale delle maschilità eterosessuali. Salute e società, 2, 60-78. Ciccone, S. (2009). Essere Maschi. Tra potere e libertà. Rosenberg & Sellier. Ciccone, S., & Nardini, K. (2017). Approcci e pratiche per leggere tras/formazioni, resilienze e riconfigurazioni delle maschilità. About Gender, 6(11), i-xxix. Connell, R. (2011). Questioni di genere. Il Mulino. Corradi, C. (2008). I modelli sociali della violenza contro le donne. FrancoAngeli. Corradi, C. (2014). Il Femminicidio in Italia. Dimensioni del Fenomeno e Confronti Internazionali. In F. Cimagalli (Ed.), Le Politiche contro la Violenza di Genere nel Welfare che Cambia (pp. 157–169). Franco Angeli. Krippendorff, K. (2013). Content analysis: an introduction to its methodology (3rd ed.). Sage. Losito, G. (1996). L’analisi del contenuto nella ricerca sociale. FrancoAngeli. Maragaggia, E., & Giomi, S. (2017). Relazioni Brutali. Genere e violenza nella cultura mediale. Il Mulino. Paechter, C. (2003). Masculinty and Femininity as community of practice. Women’s Studies International Forum, 26(1), 69–77. doi:10.1016/S0277-5395(02)00356-4 Rinaldi, C. (2013). La violenza normalizzata. Omofobie e Transfobie degli scenari contemporanei. Kaplan. Rinaldi, C. (2016). Sesso, sè e società. Per una sociologia delle sessualità. Mondadori Università. Rinaldi, C. (2018). Maschilità, Devianze, Crimine. Maltemi Press. Rogers, R. (2010). Internet Research. The Question of Methods: A Keynote Address from the You Tube and the 2008 Election Cycle in the United States Conference. Journal of Information Technology & Politics, 7(2-3), 241–260. doi:10.1080/19331681003753438 Rositi, F. (1998). Analisi del contenuto. In La ricerca sull’industria culturale (pp. 59-94). Academic Press. Ruspini, E. (2014). Le differenze di genere. In T. Grande & E. Giap Parini (Eds.), Sociologia. Problemi, teorie e intrecci storici (pp. 259–272). Carocci editore. Stark, E. (2009). Rethinking coercive control. Violence Against Women, 15(12), 1509–1525. doi:10.1177/1077801209347452 PMID:19850959 Toffanin, A.M., (2019). La ricerca sulla violenza maschile contro le donne. Una rassegna della Letteratura, IRPPS. https://viva.cnr.it/wp-content/uploads/2019/08/deliverable07-ricerca-sulla-violenzamaschile-contro-donne-rassegna-della-letteratura.pdf Weber, R. P. (1990). Basic Content Analysis (2nd ed.). doi:10.4135/9781412983488 West, C., & Zimmerman, D. H. (2009). Accounting for doing gender. Gender & Society, 23(1), 122–122. doi:10.1177/0891243208326529 Yuval-Davis, N. (2006). Belonging and Politics of Belonging. Patterns of Prejudice, 40(3), 196-213.

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ENDNOTES 1



2



3



According to Camoletto (2013: 60), sexual experiences also fall among the collective practices of masculinity; sexual interaction with women constitutes a ground for verification and confirmation of one’s masculinity within the male homosocial community. A coding scheme is a tool very similar to a questionnaire with structured and semi-structured questions. Usually, the information thus collected is coded and entered in a data matrix and analysed using statistical techniques (Losito 1996). Intercoder reliability detects the degree of agreement between different encodings performed on the same textual information by different researchers (Losito 1996).

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National Differences and Gender Stereotypes in Days of Empire Giuseppe Maiello University of Finance and Administration, Czech Republic

ABSTRACT Days of Empire is a freemium mobile strategy video game developed and published by the company ONEMT, whose actual headquarters is in Fuzhou Fujian, China. The company specializes in fantasy video games mostly set in the Middle East and full of references to the history and mythology of the Arab and Turkish peoples. The objective is to provide a description of the game and to perform a qualitative analysis of the attitudes of selected players towards the game, their emotional drivers, and the financial commitment many of them undertake to achieve greater success in the game. As many discussions take place in the chat function of the game, the author is interested in stereotypes referencing the players’ country of origin, gender stereotypes, and even the sexual harassment to which female players are subjected. Using the emic approach, an insider’s perspective will be shown of the ways in which the players of Days of Empire look at the issues of nationalism and gender stereotypes, and the emotional connection between single individuals and a freemium game of this type.

INTRODUCTION While observing the context of role-playing games (RPG), the researcher chose to conduct a study on a game entitled Days of Empire, which reached the peak of its popularity during the period of the COVID-19 pandemic. In order to carry out the study, the researcher could not rely merely on the official – and extremely scarce – presentations of the game developer, the Chinese company ONEMT, but also had to become a participant in the game, even in the absence of previous gaming experience with any other RPG on a mobile device.

DOI: 10.4018/978-1-7998-8473-6.ch043

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 National Differences and Gender Stereotypes in Days of Empire

BACKGROUND The present work adheres to the methodological principles of netnography research, as indicated by Robert Kozinets (2020), and represents a further step on the path indicated by other authors who have dealt with videogames netnography such as Tom Boellstorff et al. (2012), Ercilia García-Álvarez, Jordi López-Sintas and Alexandra Samper-Martinez (2017), Yi-Sheng Wang, Wei-Long Lee and Tsuen-Ho Hsu (2017), Jenna Drenten, Robert L. Harrison and Nicholas J. Pendarvis (2019), Ahmad Zaidatul and Noor Aireen Ibragim (2020), and some umpublished final thesis or doctoral dissertations. The author drew on five types of possible information: those offered by the developer, the graphic and textual structure of the game, the group communication carried out in the game’s chats and on the cross-platform service Whatsapp, interpersonal communication through long-distance telephone interviews, and intrapersonal communication (autoethnography). Instead, the author does not present clear statistical data as the game developer does not appear to be transparent enough in the marketing of its product. The little data available is derived from comparison with similar online games and from a sample of 105 players. All respondent names are fictitious. Not even the aliases used by respondents in the game have been reported, so that it is not possible to trace the identity of the informant. The phrases taken from the chats of the game are instead reported with the nickname that the player uses in Days of Empire.

THE CHARACTERISTICS OF DAYS OF EMPIRE Days of Empire in the Context of the World of Freemium RPGs Over the years, video games have increasingly become an integral part of life for a large part of the world’s population. It is also a generally accepted fact (Polaková 2011: 57) that when creating a character, a player at the same time is creating an imaginary “me”. The player assumes a foreign identity, sets out from the reality of the ordinary world into another, better one. The player can eliminate the norms and demands of his everyday role and throw himself into an adrenaline adventure without the risk of any real danger. Days of Empire can be defined as a massively multiplayer online role-playing game (MMORPG) used mainly on mobile phone. Although it does not appear in the world rankings of “the best RPG games”, the interviewed players consider it among the best in the world “for its graphics”, the possibilities of “getting relief even when defeated” and the “willingness to dialogue in the majority of players”. The reviews are not numerous, and as in other cases it is not always possible to distinguish spontaneous reviews from those created ad hoc by the developers. One of the most popular public reviews goes into the details of the game and only complains that it is necessary “to improve translation, for better communication among players” (play.google.com - 10 February 2021). The business model of Days of Empire is one known as free to play (F2P). It is a model that developed particularly in some Asian countries – Korea, Japan, Taiwan – and then spread to the rest of the globe (Park & Lee 2011: 2184). Today Chinese companies have seen the largest increase in sales in the world for this type of game, and Days of Empire seems to represent only a fragment of the gigantic turnover related to digital games.1 Its specific feature is to target players interested in the glories of the Ottoman Empire – i.e., Turks from the motherland and the diaspora – to other players from Islamic countries, and finally to other interested people from various other parts of the world. The game even presents itself as 789

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“the first true war game to restore the history of the Ottoman Empire” (Apple 2020), thereby using the word “history” for a genre that is undoubtedly fantasy. The game does not have Chinese players, nor players from other countries of the Far East that dominate the market for RPGs, although there is a substantial and widespread presence of Vietnamese players. Despite the presence of players from various cultures and religious confessions, it is a good practice among players to greet each other with phrases such as “God be with you”, or “peace be with you”, or address the other players with the nickname of “brother”. In their first few weeks in the game, some more aggressive players tended to use offensive words, of which “dog” or “bastard” stood out. In the evolution of the game, there was a tendency to isolate these types of players up to banning them or reporting them to the game developers. Misunderstandings are ongoing, however, given the fact that the linguistic contexts are very far from each other and the automatic translator mechanism cannot keep up with all the slang expressions used by players. The language most frequently used in the game chats is the Turkish language, used by both Turkish and German Turks. In the continuous heterostereotypes that circulate in the game, where players often gather in linguistically homogeneous “guilds”, the Turkish-speaking players who live in Germany have a reputation for being more elegant and more accommodating than the Turkish players of their motherland. There is no official data on the number of active players. All respondents indicate an approximate number of 3000 active players playing more or less simultaneously. This would cast doubt on the economic return on investment of the game. However, considering that the game has experienced a visible expansion in the number of players since the spread of the SARS-CoV-2 pandemic, and considering the fact that new graphic enhancements and new special “events” are offered by the developers at irregular but continuous intervals, no one would assume that the company has problems financing the game. In reality, the so-called F2P business model is a model also defined in specialized academic literature as pay to win (P2W). This is a model in which addictiveness is quite evident, so much that in 2019 there was an attempt to regulate it proposed in a bipartisan bill presented by some US senators (US Congress 2019). In Days of Empire, for example, we find everywhere the notorious Loot Boxes that give immediate satisfaction to the player. The player is convinced upon opening them to increase his power and wealth, obviously virtual. However, Loot Boxes are one of the main forms of monetization for the video publisher, as there is an option to obtain more in the game through so-called “microtransactions”. Days of Empire offers in an apparently non-invasive way the option of using real money – usually fractions of a monetary unit – to buy boxes, “summons”, jewels, and other commodities. The microtransactions help the player both from a cosmetic point of view, but above all in terms of power, making him a stronger player, but economically weaker over time in real life, like the gamblers of previous generations before the MMORPG.2 Not all Days of Empire players spend large sums of money, but it is clear to everyone that the best players are such because they “put their hands in their pockets”.3 And yet this complaint, reported to the researcher by some informants, is in no way pointed out in game chat. The players usually communicate on 3 text-based chats: one of their guild, one corresponding to individual national languages, and one common to the entire “kingdom”. Every week a new kingdom is built by the developers of the game and new players can join the game in the new kingdom. With this system the game can expand indefinitely. There are very limited possibilities for communication between the various kingdoms. Chat usually is open to players from a few other kingdoms for only a few hours a month, and just in the case of special events where players from various kingdoms meet or clash.4 790

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Days of Empire players also typically write through WhatsApp when developing individual or group friendships. However, group communication on WhatsApp is limited by the fact that there is no simultaneous translation service on this cross-platform service. In the game there is also a voice chat, but this is rarely used and usually only for meetings related to the strategy to be adopted in the case of “battles” or “diplomatic talks”. The issue of big payouts is not taboo and is sometimes openly discussed, or rather the strongest players will boast in the chat and discuss in the most natural way possible how many extra crates they have bought. A sudden increase in the strength of another player is viewed by the other players with some respect,5 along with a certain selfish satisfaction because the strong player will also be able to contribute, in part, to the virtual wealth of the weaker players of his guild. Even admitting to microtransactions is not taboo due to the trifling amount of money spent; there are players who even declare that they have remained in the game for many months and have not even made any microtransaction yet still have considerable strength. To become strong, in reality, can be achieved by spending most of the day on the mobile phone and opening the dozens of Loot Boxes offered after carrying out very simple operations that only at first sight seem to be not mechanical operations. You can become strong, but not very strong, and this creates various frustrations in the players, who believe that with perseverance they can break the vicious circle of P2W.6 And yet the players seem to lack awareness of the fact that without paying you cannot match the power of those who spend money in massive amounts. One respondent told to the researcher: “I don’t understand, I’ve been spending 10 to 16 hours a day in the game for a year, and I haven’t even come close to the power of our boss!”. “Guild leaders” only in the first leveles of the game, can become “wiser” people. Over time the player’s power, derived from real money transactions of several hundred Euros or other equivalent currencies per month, seems to be the main criterion for making him a leader. Through participant observation, netnographic reflection, and talks with other players, the author found that the aim of all players is to grow individually, however without realizing the various levels of exponential growth derived not only from the hours spent in the game, but from the sums spent. This condition is sometimes detected as an “injustice” of the developers against “fair players“, but it is not considered a possibility to simply stop playing, at least for those who are in the game, because playing automatically is supporting this “injustice.”

National Differences in Days of Empire The strategy of the Chinese developers of on-line platforms is to clearly differentiate the targets. For example, the well-known Chinese social media service TikTok is aimed at all other countries in the world, but not at Chinese consumers, who instead use Douyin, an app that is quite different and more adapted to the Chinese consumer. In both cases it is the same developer, the Beijing company ByteDance (Marszałek 2020). The company Onemt based in Fuzhou (www.onemt.com) specializes in RPGs with fantasy themes, but also “historical”, at least in the definition that the company itself makes of some of its products. For the European public, for example, Onemt developed King’s choice, an RPG set in an unlikely – and very ahistorical in terms of its costumes – medieval European court, Rise of the Kings, a game that unlike the previous one openly declares itself to be a fantasy-based war strategy game, and Modern Dead, another fantasy RPG, set in a “post-apocalyptic word”.

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For players passionate about Middle Eastern mythologies, Onemt has instead developed Revenge of Sultans, aimed at the Arabic-speaking consumers, and Days of Empire, whose main target are Turkishspeaking players. Finally, in the Onemt offering there is an RPG entitled Eternal War, also based on one improbable “real history”, a continuous war between the Western world and the Middle East. Although Chinese players are very rare in all these RPGs, the developer’s willingness to compare different cultural contexts seems quite evident. In Days of Empire, as in other similar games, the player is pushed to play not in isolation but to join a “guild”. The guilds are then pushed to diplomatic and war activities, in addition to the fact that the stronger a guild is, the more “free” resources are acquired. The trend in the guild is to socialize through chat or mere presence in the guild. The guild represents a form of social group with its own moral rules of behavior. In the “kingdom” studied by the author, in addition to various Turkish-speaking, or Turkish-speaking international “guilds”, two Russian-speaking, two Polish, one German-speaking, one Vietnamese, one Italian-speaking, one French-speaking, one in Arabic, and various international guilds were also formed. The researcher participated in the game with three “castles” and did not hide his identity as a netnographist. This was not a problem in the first place because all players react primarily to the type of personality that arises while playing. An Italian gas station attendant, a Chechen professor of history, or a Turkish entrepreneur living in Germany will be perceived as they appear in the game, namely as an aggressive sergeant, a wise female ambassador, or a brave and rich captain. This is due to the fact, as noted long ago in studies devoted to the first RPG – Dave Arneson’s Dungeons & Dragons – and in general to the representations of oneself in an artifact environment, the creation of a character represents the creation of an imaginary “me” that enters a world substantially better than where he lives (Huges 1988; Goffman 1959). The research work on Days of Empire was therefore considered to be an ordinary job, and what mattered to the respondent and all the players was the behavior of the character managed in the game.7 As in each fantasy scenario, drawing from R.R. Tolkien’s stories, the first thing the Days of Empire player learns is that there is a single force of evil that is clearly delineated. All players can be regarded as enemies or potential friends at the same time, since they are human beings and it is assumed that a meeting or a clash would be possible. Even some mythical figures present in the game, which Middle Eastern mythology would consider negative characters, such as Ifrit or the beast Abra, are depicted with characters that are not excessively aggressive, indeed almost childish. Those who always assume spectral shapes, with features blurred and shadowy, are only the Byzantine Raiders and the Byzantines are also the ships that must be destroyed every day with cannon shots to gain points and resources. Furthermore, one of the recurring events of the game is the destruction of the walls of Constantinople by sea. As in the Christian feasts of the nativity or resurrection, the cycle of destruction of the walls of the capital of the Eastern Roman Empire is eternal, and it is repeated on a weekly basis. Only the walls of Constantinople can be seen, but not the city itself, as if the developers wanted to exclude any form of Greek iconography from the game. While in fact the details of the buildings and all the characters in the game are clear-cut and sophisticated, the few references to the Greek world are indistinct, gloomy, or in fact nonexistent. The researcher noticed only among the Russian players a certain embarrassment about the offensive attitude of the game towards the Greek point of view, and in general Western, of the Byzantine historical experience. The controversy on this issue, however, remained muted, and if any individual player opened the question, it was quickly overshadowed and never openly faced. The author found only a discussion 792

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between a Turkish and a Russian player belonging to the same “guild”, therefore “fraternal”, in which each stated that Constantinople-Istanbul “is ours!”: by “ours” meaning belonging historically to Russia or, on the contrary, Turkey. The Russian players were the only ones in the game who, when preparing the weekly attack on the walls of Constantinople, used an expression designed to remove the memory of the tragedy that struck the city of the Bosphorus in 1453: “Let’s go to Kostja”. Kostja in fact in Russian is the diminutive of the name Constantine, therefore of a person, not of the city. All the other players used the expressions “today we go to Constantinople”, “let’s go to Constantinople”, etc ... Of the alliances (“guilds”) mentioned above, the only ones that still retain a national character are the Russian, Polish, and Vietnamese ones. The Vietnamese players gathered in their “guild” have maintained an aggressive attitude – or defensive, depending on the point of view – throughout the game, but have not been able to open a “diplomatic” dialogue. Some players have also suspected that some Vietnamese players acquired their strength directly from the Chinese developer, meaning that some players could receive extra power gifts without paying, just to keep the “kingdom” in constant war and thus force some of the players to spend extra money to stay strong in the ongoing war with the “Vietnamese guild”.8 The majority of Russian and Russian-speaking players have dissolved into other alliances, while a small minority still plays by highlighting their national identity but has remained isolated. The Polish “guild”, given the strong national name Siła i Honor – Force and Honor – thanks to acute “diplomacy” still ranks among the strongest of the game. In the game, the author of this chapter is considered an “expert ambassador”. He therefore experimented with joining Russian-speaking and Polish players in the same “guild”. The Russian players, albeit through gritted teeth, were willing to join forces that would increase the strength of both national groups. The majority of Polish players refused, however, commenting that “there are things that go beyond the game and that cannot be overcome”. In the German-speaking “guild” there were originally some players with encrypted symbols that evoked Nazism. Discovered by other players, they eliminated the symbols and continuing to play, but in a less active way. The Turkish group is the most active and definitely dominates the game. Yet many players, in nonofficial chats such as WhatsApp chats and personal communications, distinguish between Turks from the motherland and Turks-Germans, i.e. residents of Germany. The latter are regarded by non-Turkish players with more respect, thanks to their “more cultivated” ways, even if they don’t spend as much money as the Turkish players of the motherland. It is possible, but was not confirmed during the research, that players linked to radical Islamism also participate in the game. Even in this case, however, if such types of players truly exist, they have not come out in the open, at least in the eyes of Western players. However, radical symbology and views are usually overwhelmed and, over time, flow into the game’s moderate mainstream. The main psychological types of RPG players – social, achievers, and inactive players – already described ten years ago by Tseng (2011) are represented without exception in Days of Empire and certainly the developers must have worked on these types of possible players when they designed the game. Since social players represent the majority of players, there is a spontaneous tendency to extend and generalize fair play behavior. Male players and female players are fascinated by the personalities, or rather by the virtual “I” of other players, and sometimes “engagements” and declarations “of love” between male players and female players belonging to different (offline) cultural and national backgrounds are announced.

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Gender Stereotypes In addition to virtual engagements which, due to the restrictions resulting from the SARS-CoV-2 pandemic, have not actually allowed the extension of offline dating, a female players informed the researcher about some players who were subjected to verbal sexual harassment. If a female player complained about this to a guild leader, the harassing player was removed from the guild itself, remaining for the rest of the game with a brand of bad reputation, at least among the most active and participatory players in the discussions in chat and gossip outside of official chats. Some female players preferred to handle the harassment themselves, without spreading the news among other players. The question of the presence of women in videogames in general has also been examined for some time. Even in Europe, the data on the percentage of female players on mobile devices is comparable to that of the United States and Canada, i.e. essentially an equal percentage of players (ISFE 2020).9 In the case of Days of Empire, the percentage of female avatars is 35%, therefore lower than the average of Western countries and inverse to that of Japan. The female players interviewed agreed on the opinion that “being a game where the players are predominantly Turkish men, this could be the reason why demographic parity is not achieved in the game”. The fact that in one part of the game there is a “harem” in which three “concubines” appear in sexist poses does not particularly disturb the female players, who see in the “harem” a “historical situation of the past”. The three “concubines” are accompanied by a text describing their national origins: one Russian, one Jewish and one Venetian. The concubines undress as the lord grows in level and try to have different characters, from the submissive, to one greedy for jewels and gifts, to that of a self-confident woman. In the game graphics, the foreign concubines appear as women with wide hips and small breasts. On the contrary, Ottoman heroines are represented with prosperous and maternal breasts. The perspective of having graphics also with male genuflections or male stripteases would be welcome, but the graphic and textual disparity in this area of the game is not particularly problematic as the main goal of all players is, even in this case, to “get more power” for future battles” and “acquire resources”. These are also acquired in the “harem”, but not being particularly relevant, the situation leaves them relatively indifferent. Women, if “strong and wise” are welcome as leaders of a “guild”. There are also virtual “roses” in the game that can be gifted to other players. The one who receives the most “roses” receives additional rewards. In this tournament – which is periodic like all the others – the female players are generally more successful. A player believed to be male came out after six months of playing, revealing that she was a woman and subsequently changing her avatar. When the researcher asked why she had hidden her identity, she replied that she believed that RPG was a male prerogative and therefore “was ashamed”. After seeing that there were many other female players in the game, she revealed her identity to everyone, but at the same time changed her game behavior, moving from aggressiveness to reflexivity.10 Another woman, very educated, revealed to the researcher that she had cried because of the game over the abuse she received in chat from another female player. But in the game she only showed coldness and a desire to take revenge, that is, attacking and spending money to attack more effectively. A very important element of the game are the so-called “heroes” (and heroines).11 To receive them you need to perform a “summons”; in practice, periodically click on the game buttons or spend real money. Heroes and heroines act as guardian spirits that increase the player’s virtual power. The right choice of “hero” (or heroine) can guarantee victories and increase “resources”. The animism of the conception is 794

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not openly declared, as the game tries to be as respectful as possible of the Sharia, it being understood that the game itself puts many players in a position to violate it, given that playing an RPG differs from traditional gambling only for the fact that there is no official possibility of receiving money or material goods in exchange for those invested.12 There are currently 46 “heroes” (and heroines) in the game.13 “Heroes”, depending on their abilities, are divided into battle heroes – 10 male heroes and five female heroes – which increase fighting strength, development heroes – 10 male heroes, five female heroes, and one who can be interpreted, not in any open way, as gender-neutral – who speed up the acquisition of “resources”, and assistance heroes – 10 male heroes and 4 female heroes – who speed up the training and treatment of wounded soldiers. All the “heroes” and “heroines” correspond to characters from Turkish history who have distinguished themselves for their military, scientific, and technical qualities. The game also provides some historical information on the “hero”, but does not provide precise information on the period to which it refers, thus leaving all the individual imagination in the liminal level of the fantasy genre.

CONCLUSION Rise of Empire, like all other RPGs, uses a very questionable business strategy, one which the author considers unethical. The game also replicates stereotypes of a nationalist and sexist character. The fictitious Ottoman world of Days of Empire, however, also guarantees a certain form of intercultural dialogue, however enclosed within the cultural boundaries of a moderate and modern Islam. Women are seen as fair fighters, wise administrators, or concubines not always submissive to their masters, but grateful that he behaves in a kind and not rude way. The key symbols of the game are therefore carefully calibrated to recall the Ottoman tradition, and to be generally accepted by those who are not particularly expert in history or philology, but at the same time are fascinated or intrigued by the symbols of Turkey of the pre-industrial era. By considering Rise of Empire a “game”, players are very tolerant of the game’s developers. They are curious about new graphic solutions and new heroes and new heroines or mythical animals that may appear in the future. They are also tolerant of the fact that there is a risk of financial ruin. Spending money to “play” is considered an individual choice corresponding to the principles of Classical liberalism. The fear of being locked in a dangerous cage from which it is difficult to escape exists and is strong. But the majority of players still remain in the game “so as not to leave friends alone to fight”. The game offers no prospect of an end. The basic scenario is set up in a cyclical manner, with events repeating identically from week to week. The players instead grow virtually in a linear way, indefinitely. When some players were asked by the researcher how they imagine the end of the game, no one has been able to answer. All have only indicated a more advanced point in their infinite prospect of growth, but not an end point. The few players who have found the strength to quit the game have refused to continue talking about it. The researcher interprets this refusal as a desire to permanently remove a negative experience, such as that of a gambler finally released from the tunnel of addiction.

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ACKNOWLEDGMENT This research was supported by the University of Finance and Administration [grant number 7427/2021/05].

REFERENCES Apple Inc. (2020). App Store Preview: Days of Empire. Heroes never die. https://apps.apple.com Bektic, E., Bruns, D., Gabriel, S., Kelle, F., Pölsterl, G., & Schniz, F. (2020). Mixed Reality and Games: Theoretical and Practical Approaches in Game Studies and Education. Transcript-Verlag. doi:10.14361/9783839453292 Boellstorff, T., Nardi, B., Pearce, C., & Taylor, T. L. (2012). Ethnography and Virtual Worlds: A Handbook of Method. Princeton University Press. doi:10.2307/j.cttq9s20 Çalışkan, E. T. (2020). P2W Olmayan Mobil Oyunlar Var Mı? https://enestalha.com China Daily Global Edition. (2020). Nielsen: Chinese free-to-play games in global top 10 by revenue. https://www.chinadaily.com Drenten, J., Harrison, R. L., & Pendarvis, N. J. (2019). Video gaming as a gendered pursuit. In S. Dobscha (Ed.), Handbook of Research on Gender and Marketing (pp. 28–44). Edward Elgar. doi:10.4337/9781788115384.00007 García-Álvarez, E., López-Sintas, J., & Samper-Martinez, A. (2017). The Social Network Gamer’s Experience of Play: A Netnography of Restaurant City on Facebook. Games and Culture, 12(7-8), 650–670. doi:10.1177/1555412015595924 Goffman, E. (1959). The Presentation of Self in Everyday Life. Doubleday Anchor Books. Hughes, J. (1988). Therapy is Fantasy: Roleplaying, Healing and the Construction of Symbolic Order. http://www.rpgstudies.net/hughes/therapy_is_fantasy.html ISFE. (2020). Key Facts. Demographics. Interactive Software Federation of Europe. Kozinets, R. V. (2020). Netnography. The Essential Guide to Qualitative Social Media Research (3rd ed.). Sage. Langer, R., & Beckman, S. C. (2005). Sensitive research topics: Netnography revisited. Qualitative Market Research, 8(2), 189–203. doi:10.1108/13522750510592454 Lelonek-Kuleta, B., Bartczuk, R. P., & Wiechetek, M. (2021). Pay for play – Behavioural patterns of pay-to-win gaming. Computers in Human Behavior, 115, 1–10. doi:10.1016/j.chb.2020.106592 Lippitz, A. (2020). The Departure of Avatar-Sexual Characters in BioWare’s Dragon Age: Inquisition. In Mixed Reality and Games: Theoretical and Practical Approaches in Game Studies and Education (pp. 161-172). Transcript-Verlag.

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Marszałek, W. (2020). A Thorough Guide to Influencing on Douyin - For Individuals and Businesses. https://www.nanjingmarketinggroup.com Park, B.-W., & Kun Chang, L. (2011). Exploring the value of purchasing online game items. Computers in Human Behavior, 27, 2178–2185. play.google.com. (2021). Days of Empire - Heroes never die. https://play.google.com Polaková, M. (2011). Draci a hrdinové – upíři a oběti (hledání magických bytostí ve světě vědy a techniky) [Dragons and Heroes – Vampires and Victims (Searching magic beins in the world of Science and Technique)]. Anthropologia integra, 2(2), 53-61. Statista. (2021). Gender distribution of people playing games in Japan as of January 2018, by type. https://www.statista.com US Congress. (2019). A bill to regulate certain pay-to-win microtransactions and sales of loot boxes in interactive digital entertainment products, and for other purposes. Senate - Commerce, Science, and Transportation, 116th Congress, 1st Session, S.1629. https://www.congress.gov/bill/116th-congress/ senate-bill/1629/text Wang, Y.-S., Lee, W.-L., & Hsu, T. H. (2017). Using Netnography for the Study of Role-Playing in Female Online Games: Interpretation of Situational Context Model. Internet Research, 27(4), 905–923. Zaidatul, A., & Ibrahim, N. A. (2020). Male Identity Portrayal in Virtual World. An Ethnographic Investigation in Second Life. In The Virtual Language and Communication Postgraduate International Seminar (VLCPIS) 2020 Proceedings (pp. 16-21). Universiti Teknologi Malaysia. Zendle, D., Meyer, R., & Ballou, N. (2020). The changing face of desktop video game monetisation: An exploration of exposure to loot boxes, pay to win, and cosmetic microtransactions in the most-played Steam games of 2010-2019. PLoS One, 15(5), e0232780.

ADDITIONAL READING Friedenberg, J. (2020). The Future of the Self: An Interdisciplinary Approach to Personhood and Identity in the Digital Age. University of California Press. Huizinga, J. (1938). Homo Ludens. Proeve Eener Bepaling van Het Spel-Element der Cultuur [Homo Ludens. A Study of the Play-Element of Culture]. Tjeenk Willink. Kao, D. (2020). The effects of juiciness in an action RPG. Entertainment Computing, 34, 1–10. doi:10.1016/j.entcom.2020.100359 Laycock, J. P. (2015). Introduction. Fantasy and Reality. In Dangerous Games: What the Moral Panic over Role-Playing Games Says about Play, Religion, and Imagined Worlds (pp. 1-28). University of California Press. Lyottard, J.-F. (1979). La condition postmoderne: rapport sur le savoir [The Postmodern Condition: A Report on Knowledge]. Les Éditions de Minuit.

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Mochocki, M. (2021). Role-play as a Heritage Practice. Historical Larp, Tabletop RPG and Reenactment. Routledge. doi:10.4324/9781003130956 Schalleger, R. R. (2018). The Postmodern Joy of Role-Playing Games: Agency, Ritual and Meaning in the Medium. McFarland. Yee, N. (2014). The Proteus Paradox. How Online Games and Virtual Worlds Change Us-And How They Don’t. Yale University Press. Zagal, J. P., & Deterding, S. (2018). Role-Playing Game Studies: Transmedia Foundations. Routledge. doi:10.4324/9781315637532

KEY TERMS AND DEFINITIONS Avatar: Image chosen to represent oneself in a virtual community. F2P: Games for which nothing is paid. Guilds: Associations of artisans and merchants in the European Middle Ages. The word was adopted by RPG language to mean unions of players within the game distinct from others. Heterostereotypes: Specific forms of stereotypes that group members exhibit towards other social groups. Loot Box: A virtual object, usually presented in the form of a chest, containing one or more virtual objects that offer the player improvements in the game. Microtransactions: A business model where users can buy virtual objects through electronic transfer of small amounts of money. MMORPG: Video game genre where a huge number of RPG players are involved. P2W: The reality of F2P: to be really competitive, it is necessary to spend real money. RPG: A type of game in which players play the role of fictional characters for that act according to given rules within the game itself. Sharia: In the Islamic tradition, behavior dictated by God for the moral, religious, and lawful conduct of his faithful.

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1 According to the most recent data offered by Nielsen Media Research and emphasized by the Publicity Department of the Chinese Communist Party, “world digital games revenue last year had a 3 percent year-on-year rise to hit $109.4 billion, among which free-to-play games spending accounted for 80 percent of all the revenue, thanks to strong performances from mobile games” (see China Daily 2020). 2 The model pay to win seems to be in decline, at least as regards the Steam games, but the amount of the sums paid in microtransactions, is always growing, although not exponentially as in the years 2012-2014 (Zendle, Meyer & Ballou 2020) 3 Judging by the statements in the chats and the development and strength of the “castle”, it is possible that some players spend even € 50-100 per day. An informant reported that he regularly

 National Differences and Gender Stereotypes in Days of Empire







spends 250$ a week on Days of Empire. When he is defeated and loses power, then he spends additional money. 4 The names of those special events are Fields of Flames, Dragon Slayer Storm, Top Kingdom, Infinite Hostility. 5 But a Turkish blogger, specialized in MMORPGs, launched an appeal in September last year not to fall into the trap of these games “because if you don’t plan to deposit money, you will surely be crushed”. The blogger also warns that “the character you have prepared in a month will be defeated in a few moments” by someone who has invested large sums of money (Çalışkan 2020). 6 A quantitative study recently carried out in Poland on a sample of 1702 Pay to Win gamers has focused on the types of frustration experienced by these types of players (Lelonek-Kuleta, Bartczuk & Wiechetek, 2020). 7 This has greatly facilitated the research work, because on the one hand it was possible to respect the ethical principles of traditional social anthropology, on the other hand it was possible to benefit from all the advantages of covered netnography (see Langer & Beckman 2005). 8 The hypothesis is unverifiable, because if any of the players admitted to receiving personal favors from the developer, this would lose the trust of the players, with the inevitable economic consequences of the case. 9 In Japan, however, the percentage of female players is traditionally higher than that of male players, at least as regards the Online games sector (STATISTA 2021). 10 On the change in attitude depending on the avatar feature among RPG players, see also Lippitz (2020). 11 In the game, however, only the male term “Heroes” is used. 12 In reality there is a “black market” of “castles”. When a player gets tired of the game and has a “powerful castle”, he gets to reach an agreement in parallel with a buyer, who in exchange for sums of real money acquires the passwords to become the manager of the “castle”. There is a belief among players that this is an illegal practice not tolerated by the developers. In fact, while the developers certainly have enough tech gimmicks to control such types of ownership transitions they don’t seem to care. On the contrary, they probably know that if a player spends a few thousand Euros to acquire a “castle”, he will spend more to enhance and enrich it, even from a purely aesthetic point of view. 13 But being a game in infinite expansion, the arrival of other “heroes” (and “heroines”) is expected in the future.

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Social Identity Seeking and Sharing as a Creative Activity of Art Consumers Jitka Cirklová https://orcid.org/0000-0003-2728-8203 University of Finance and Administration, Czech Republic

ABSTRACT The chapter offers an overview of dynamic processes changing the role and place of museums and art galleries in our societies. After many decades of being static displays of things, they are now changing into places of interaction and communication on a variety of levels. The text is presenting some current patterns of developing a sense of collective belonging and also it is looking at the communication processes between institutions and visitors with a focus on the role of digital technologies and social media in the process of preserving, narrating, and sharing the object of art and beauty. The purpose of this chapter is to provide a framework for further research on digital practice linked with contemporary social identities and art institutions that are a significant social institution with public value and the ability to link the local cultural heritage global context.

INTRODUCTION Museums and art galleries are permanent institutions in the cities. If they are created by transformation of historic buildings, they very often place the local narratives into the global context. If they are built in the recent period, they can be regarded as products of contemporary design and architecture style. As a rule, they play a crucial role in the formation of visual identity and image of the place where they belong. Museums transfer values, opinions, and sense of beauty between generations and locations. In recent years, a major reform in the design of public museums has taken place in many countries. In the era of new media, the process of redefining relationships between visitors and objects can be observed. Museums have been transformed from a static display of things into a place that offers access to culture and heritage as a dynamic process involving interweaving human actions, beliefs, skills, and materials DOI: 10.4018/978-1-7998-8473-6.ch044

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 Social Identity Seeking and Sharing as a Creative Activity of Art Consumers

interpreted as sources of individual identity. The items displayed in museums and galleries are often used to form the aesthetic background of product promotion or to enhance the narrative of the contemporary self. This research focuses on the visual dimension of the process of creating individual identities during individuals’ interaction with contemporary art galleries, art fairs, or museums. These institutions are significant parts of the international economy, as they are closely connected with such cultural industries as fashion and mass cultural tourism industries. Due to the pre-COVID-19 growth of tourism, the local narrative and material objects were promoted, described, commodified, marketed, and thus were easily accessible to local and international visitors who documented their visit to the art institutions on numerous photographs shared on the social media. Museums are educational establishments that help to develop the understanding of the sense of belonging to the larger social context. How does the way people consume arts, document their visits, and publish the records on social media enhance their sense of identity? What roles do social media play in influencing visitor’s choice what to visit and what to capture in the form of a selfie? The research examines museums and galleries as _ project-based public space that produces the collectively shared knowledge and values and contributes to them. It also outlines some current patterns of developing a sense of belonging of individuals to the larger collective and cultural context. The aim is to disclose some communication processes between the institutions, the content producers, and the visitors, or users. In this brief, the students involved in the research provide an opportunity to discover some interaction forms among the complexity of encounters and exchanges witnessed in the current digital societies.

FROM ‘CABINETS OF CURIOSITIES’ TO PERMANENT SOCIAL INSTITUTIONS The authors apply the term ‘contemporary museum’ to institutions that began to appear about 250 years ago and perform an educational and experiential function. Their predecessors, the so-called “cabinets of curiosities” served as rooms for collections gathered by monarchs since the sixteenth century, as well as for those belonging to private individuals, for instance, cleric Manfred Settala in Milan, around 1700. The greatest collector’s popularity was drawn by medals (Burke, 2009, p. 124). Short-term exhibitions including the Salon in Paris and art presentations can also be mentioned.. The world exhibitions in London, Amsterdam, and Chicago appealed to a wide range of people in an unbeatable way and had a significant influence at the time. On the contrary, permanent expositions leave a long-term impact (Burke, 2013, pp. 115-117). The oldest public museum of this type is the British Museum in London, which was founded in 1753 on the basis of scientist Sir Hans Sloan’s collection. In 1793, at the time of the Great French Revolution, one of the largest museums was opened to the public - the French Louvre (Protection p. 2018, p. 19). In connection with the deepening social differences and the redistribution of wealth in the revolutionary years a layer of scholars was formed, whose efforts reflected new philosophical concepts and established the basis of Enlightenment concepts. The latter proclaimed equal access of all classes to cultural achievements among other things. The opening of the Louvre to the public was preceded by a series of edicts on the confiscation of royal collections, and the Louvre was named the Museum of the French Republic in the National Assembly declaration. The process of making large museum institutions accessible did not proceed at the same pace in Europe, but gradually all major cities started considering it an important stratifying sign of cultural maturity and significance. Benett defines the emergence of museums in Western countries as a key part of the process of reorganizing public space through high 801

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culture, which was becoming the embodiment and the setting of a new social and political order associated with the accelerated rise of the bourgeois class. The institution of museum was also used to strengthen state power and build relations between the citizen and the state, where public collections can then be called the implication of the new structure of social relations (Benett, 1995, pp. 26-40). In the 18th century the Russian Tsar Peter I founded the Great City of St. Petersburg in the Gulf of Finland in the Baltic Sea, in the north of Russia. The culture following the examples of the French Enlightenment began to be cultivated there, and the city became the center of cultural events in the eastern region. In addition, the largest collection of world and Russian fine arts was concentrated, and the best ballet school in the world was established there. In general, the great era of museums can be defined by the nineteenth century, when the museum became an optimal means of combining a magnificent depiction of history with public education (Hálek, 2014, pp. 111-112). Recently, cultural institutions have found themselves in a whirlwind of change, and the traditional variety of visitors and the customs of the institutions themselves have raised some doubts. The demands for change are stimulated, and the fundamental axiom forming the basis of the cultural institutions existence has been called into question (Kesner, 2011, pp. 12-13).

ONTOLOGY OF CULTURE AND ART – A SEARCH FOR BEAUTY OR MASS INDUSTRY? According to the French thinker Charles Batteaux (2015), the essence of art can be defined as a set of activities, the common denominator of which is an attempt to imitate the beauty of nature. On the contrary, some thinkers, including Morris Weitz (1956), question the possibility of specifying art and attribute their scepticism to the distrust of the generalization of judgments in aesthetics as such. The assumptions of using the term are variable, not constant. Sceptics claim that the word art can be used either in a descriptive or normative way. The evaluation method is then a subject to personally focused arbitrary criteria, and it should be distinguished from the classification method, which is completely independent of the individual’s notions of the quality of the artwork. The problem of using the term ‘art’ deepened in the twentieth century, as, for instance, the creation of a pre-war and interwar avant-garde was incompatible with the term ‘art’ for the society of that time. The work of Marcel Duchamp Fountain, 1917, representing an ordinary toilet shell, could not be regarded as a form of traditional art then (Zahrádka 2010, p. 23-27). In the book called Liquid Modernity Zygmunt Bauman sees a parallel between the expression ‘work of art’ and observation of the identity of individuals through the lens of measuring life success and the values ​​assessed through it. “Whether it is a delusion or not, we tend to look at other people’s lives as works of art. And after seeing them as such, we strive for the same thing ” (Baumann, 2020 pp. 111-112). Walter Benjamin associates the fundamental shift in the perception of art with the introduction of mechanical reproduction of artworks. Art has always been reproduced, whether masterpieces have been copied by the pupils of a particular art school or counterfeit copies of paintings and sculptures have been made in order to raise funds. However, it was invariably necessary to use human’s hands and ability to recreate a more or less high-quality and specifically replicated object. With the advent of technologies enabling mechanical reproducibility, art objects are being commodified and devalued in general (Benjamin, 2008, pp. 3-7).

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Culture in itself is a sophisticated concept to define. It is provided by the value orientation shaped by patterns and attitudes in human behavior in the process of social interaction. The concept of mass culture and its origin can be related to the middle of the 18th century, as it is associated with the migration of inhabitants to large industrial agglomerations and cities. This topic is elaborated on by many studies, and is assessed in contrast to the so-called high culture, which is considered authentic, elite, and non-commercialized. In general, mass culture is described in a rather derogatory way, due to its connection with the “uneducated mass.” The mass then implies _ an amorphous group of individuals who do not manifest _ their distinct personality traits, and it should be opposed to the group, the crowd, and the public. The group is characterized by close ties among individual members, the crowd is a temporary phenomenon with the possibility of creating a high degree of identity, and the public is defined as a permanent community based on the ideal of rational discussion. Other terms describing mass culture are as follows: commercialized, popular, disseminated through the media, uniformed, homogenized (McQuail, 2009, pp. 64-65). Making high culture accessible to the general public is an undeniable benefit of mass culture, so it can only be positively evaluated if the result of this effort is not so reduced or modified as to affect the content quality of a message significantly. “The notions of a high and popular art form a dichtomic conceptual pair in the texts of cultural critics; the two notions are defined in relation to each other so that the notion of popular art forms a negative point of reference in relation to the notion of high art.” (Zahrádka, 2010, p. 207) Zahrádka states further that popular works of art are often criticized for the absence of aesthetic unity and intensity, for the focus on obtaining maximum financial gain, and the parasitism of creators in high art. Generally, its extensive distribution reduces the overall level of society’s culture and weakens its sense of reality. On the contrary, a high aesthetic value is always ascribed to high art, so this divergence is called aesthetic hierarchism (Zahrádka, 2010, pp. 207-211). According to Marcel Danesi, mass culture has always been an undeliberate driving force for social, economic, and political change. Today it has an incredible fundamental power to provoke an unprecedented society-wide event spreading all over the world. The role that storytelling plays in popular culture is emphasized not only reflecting, but also responding greatly to the broader discourses of identity and society that shape and build the 21st century (Danesi, 2012). One of the fundamental manifestations of contemporary society and its consumer behaviour is the search for the aestheticization of routine and for the ideal of a beautiful life based on a permanent pursuit of new experiences. It is constantly connected with the formation of new markets and the initiation of consumer demand. This hedonistic ethos promotes experience as a basic attribute of the contemporary lifestyle, and the focus on it becomes the dominant value, life goal, and main motive of life. Marketing messages accentuate this trend and seek to meet the customer’s needs for adventure, experience, and excitement to alleviate stresses existing in current time and encourage self-realization (Roubal and Zich, 2014, pp. 71-73). Economics and economic policy have rejected the historical ideal of permanence. Therefore, consumption and culture fall under the domination of transience, and the expansion of needs subordinates mass culture to the laws of obsolescence and diversity. The transience of cultural affairs prevails (Lipovetsky, 2002, pp. 244-245). In 1947, Adorno and Horkheimer used the term ‘cultural industry’ in their critical theory of Dialectics of the Enlightenment for the first time. Their work deals with the connection between culture and industry, the areas claimed not to stand side by side but to be in direct opposition. The cultural industry 803

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produces a mass culture fulfilling its critical function no longer, and devaluing ​​high culture. In his work Scheme of Mass Culture, Adorno also addresses the pathology of modern society and clarifies some concepts of mass culture. He perceives the advertising character of culture and the associated blurring of boundaries to be against empirical reality. Michael Foucault in his prominent study of the History of Ideas (1972), analyzed the transitions of meanings that exist not only in structure of institutions defined as historical world-view, but rather as a complex set of social, religious, and cultural relations. The particular cultural narrative is an expression of and the result of specific discursive reality defined by discontinuities and unified themes (Foucault, 1972, pp. 117, 115). John Fiske (2017), one of the most prominent cultural theorists, critiques the cultural theory of the time questioning the theses of left-wing one particularly. With few exceptions, folk culture does not occur today, its space in modern industrial capitalist societies has been completely filled with popular culture. However, unlike the critical approaches considering it a sphere of passivity and control, John Fiske regards it as an area of creativity ​​ and active resistance.

THE EMPIRICAL SHIFT OF THE BIRMINGHAM SCHOOL Another direction of cultural studies was developed in connection with the establishment of the Center for Contemporary Cultural Studies at Birmingham University in 1964. It was founded by three scientists prominent in the community at the time, that is, Richard Hoggard, Raymond Wiliams, and Stuart Hall. The latter became director of the institute after Richard Hoggard’s departure. Hall stressed the importance of creating a cultural identity, focused on the subcultures, and paid special attention to mass culture defining the basic paradigms of cultural studies. He described culture as “a way of life that is related to all social practices and is the sum of their relationships” (Social Theory, 2014). emphasizing the structural aspect in the research methods and analysis of culture, while the most balanced view of culture cites the research and subsequent definition of orders and contexts of individual elements, such as folklore, traditions, social behavior, art, politics, etc. Hall’s interest is focused on the culture of consumer capitalism. He states that popular culture stops being a matter of aesthetic values there, and becomes that of emphasizing power and chargeable classification. According to Balon, the University of Birmingham can be called the foundation of the first institutionalized form of cultural studies, where S. Hall, R. Hoggard, and R. Williams transformed the study of culture radically and set the path for a new culture assessment. The researchers believe that culture predetermines the structure of society, shapes and transforms the overall way of life. The concept of subjectivity and the cultural construction of subjective identity are examined thoroughly (Balon, Sociological Journal, p.58-62). The key part of cultural studies development is the naming of discursivity and textuality in the context of using the knowledge of semiotics and structuralism. (Social Theory, 2014, online). When comparing the critical theory of the Frankfurt school with the Birmingham’s one, a fundamental shift in the empirical direction of the Birmingham school can be mentioned, as there is a scientific study of cultural phenomena, induction of these materials, and subsequent academic discussion. Both of these schools have fundamentally altered the perception of culture as such, and their ideas are applicable in many situations today.

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MUSEUMS AS CONTACT ZONES - SOCIAL REBIRTH OF CULTURAL INSTITUTIONS James Clifford is one of the most prominent figures, who had a major impact on the current appearance of museums and galleries. In 1997, he published his idea of museums as contact zones. According to his opinion, different cultures, personalities, and traditions cooperate and interact with each other in this context. This results in further collaboration in order to create a museum environment. The latter leads to dialogue establishment, understanding, partnership, and the opportunity of exchanging ideas and thoughts. The main idea is that the large number of viewpoints and interactions will stimulate the emergence of multiple opinions and increase of knowledge, thus reducing the possibility of the exhibition’s distortion and amount of it’s offensive aspects. Nevertheless, this does not exclude certain challenges, for example, in the field of ethnography. Because the presentation and display of culture not in the place of its origin to an unenlightened audience leads to inevitable questions, criticism, and controversy. Nowadays, museums are not just the places of passive observation and learning, but places of solving one’s own ambivalence through a reflection on the controversial exhibition. This is done via pondering questions without having to find an answer to them. In today’s world of deindustrialization, it is highly important to address the right to represent a culture or group of people in a globalized world and how a minority can be represented without facing an exploitation. Clifford claims that this can be done by creating some reciprocity in contact zones and moving from object exchange to decentralization versus hierarchical reference (RX art, 2014, online). This application of Clifford’s concept of the contact zone originates from Mary Louise Pratt and refers to the space of colonial meetings. This issue has been widely discussed in recent years. On the one hand, it is criticized as a mere conceptualization of a reformist. While some research data on that issue have shown that the museum can still function as “a place where a complex network of demands and articulations is expressed, negotiated and challenged” (Mc Carthy 2007, at Museum & Society P Schorch, 2013, online). Schorch considers the term “contact zone” as an experience of a museum visitor. Cultural differences can then be used to highlight a cultural festival that mediates the path of dialogue in today’s cosmopolitan world (Museum & Society, 2013, pp. 68-70, online). According to Gere, Clifford’s intention is to rethink the role of the museum in relation to other cultures. Gere suggests applying Professor Clifford’s ideas to the current model of communication, offered by the Internet and the Web, in an effort to highlight the museum’s new role in the information technology and new media ages. Modern technologies ensure unprecedentedly efficient archiving and retrieval of data, excellent liaison with other institutions, alongside with the creative transmission of information within the exhibition site. The question remains whether the individual institutions will perceive them as a tool for fundamental change in a positive sense, or will they take it as an apocalyptic denial of the traditional function of the museum. Gere seeks to describe the paradigm for the development of museums as an institution vulnerable to great change. The museum can be described as a place of visual consumption, mediated by vision technology. Nowadays, the Internet acts as the emancipatory reciprocal medium that Hans Magnus has long since dreamed of. Cultural anthropologist Perin studies the means of interaction between museum visitors and the professionals, who run these museums. He strongly reiterates the need of building relationships in these particular communication circles. Thus the museum can be considered as a hub in the network of interactive relationships, where cultures, people and communities can meet (Museums, Contact Zones, and the Internet, 1997, pp. 60-63 online).

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MUSEUMS AS COMMUNICATION ZONES - CONTEMPORARY TRANSFORMATION OF CULTURE INSTITUTIONS Present-day cultural organizations can be characterized as institutions with a specific goal associated with the production, presentation, and dissemination of culture, as well as education. They represent a huge variety of activities related to making art and cultural heritage accessible to the public. (Hagoort, 2009, p.19). Exhibition spaces and the way artworks are presented in the galleries and other cultural institutions play a crucial role, as the creation of artworks serves primarily for being exhibited. The work of art would partially lose its significance, if it is deprived of a public presentation. Rensch emphasizes the need to alter the conduct of galleries and compares the traditional model of their functioning with the new one. The novel role in the communication concept can be outlined, according to Rensch’s stress on the importance of creating a brand for a cultural institution. The authors define this role, in line with Rensch’s accentuating the importance of creating a brand from a cultural institution. “For artists, the gallery acts as a showcase in the decision-making phase prior to any sale.” (Rensch, 2018, p. 62)

Museum Visitors as Art Consumers A treatise on two typical visitors of cultural institutions and galleries can be found in the literature. For the so-called cultural lifestyle group, a visit to the gallery represents the peak of leisure activities, while a socially active group participates in social events not being exclusively cultural ones, and it occasionally has a specified spectrum of cultural interest. In the last decade, a new type of audience in cultural institutions has emerged, which differs from the traditional visitors of galleries, who possessed primarily an artistic experience. This group is called cultural consumers. They are also interested in art, but their main motivation is to meet the need for entertainment, as they have not been brought up to appreciate “high art”, popular culture is a normal part of their lives instead (Bačuvčík, 2012, p. 87). While discussing the concept of cultural institution audience, the most relevant definition to be considered is that of McQuail. The audience calls the product of a certain social context, which corresponds to the specific gallery model, so attendance is a reflection of the time usage, diurnal activities, and lifestyle. People visiting modern cultural institutions are characterized by their individual customs, expectations of content, and rules. Generally, an urban institutionalized phenomenon is considered, which is often built on a commercial basis and differentiated according to classes and positions (McQuail, 2009, pp. 407-408). Nowadays, due to the high degree of audience fragmentation, the audience ceases to be a social group, individual patterns of segmentation are created instead, so it is increasingly difficult to manage and predict its composition and subject of interest. According to Patočková and Šafr, participation in activities related to high culture is conditioned by belonging to a certain social class in connection with other socio-demographic aspects. In recent years, a significant increase in cultural participation among women, rather than men, can be observed. The differentiation of cultural consumption according to the size of the settlement is decreasing, as the environment of villages gradually begins to absorb elements of high culture. There is a significant decline in the influence of education as an individual status characteristic, while cultural homology between classes in connection with a wide range of lifestyles is growing in terms of importance and cultural consumer preferences. (Proceedings of the conference, Patočková, and Šafr, 2014, pp. 20-26).

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Ways of art consumption can be examined in the European or global context. In the United States, due to the lack of cultural traditions, art is still perceived as something exclusive, and it is necessary to have a proper education and interest in it, which defines higher social status. The subcultures related to the worldview, faith, and region play a major role in the process of deciding on shopping behaviour, whereas reference groups, especially family, are defining in the cultural environment. The family conduct can be described as one of the key factors determining the later perception of art and culture on the whole. If children attend cultural events with their parents and can recognize their interest in these events in general, the likelihood increases that the offspring will repeat these patterns of behaviour acquired in childhood. (Bačuvčík, 2012, pp. 90-91). Exhibition observation requires developed perceptual and cognitive abilities forming an important basis for cultural competence. It is connected with the criteria of education and personal development of each potential cultural institution visitor, so insufficient cultural competence becomes a major barrier to participation in cultural events. However, it is possible to solve the issue by combining personal motivation and the dynamics of the social group the individual belongs to. Sociologists and psychologists refer to museums and galleries as places of behavioral setting, that is, an environment encouraging individuals’ active action in the context of the institution’s program. Therefore, the present-day cultural institutions success depends on the quality of visitors’ experience, which, however, cannot be reduced to some instant product suitable for all groups of visitors (Kesner, 2011, pp.111-116). Pierre Bourdieu was one of the first researchers who combined empirical study with an elaborate theory of culture and its distribution, and now it is a classic study of cultural institutions visitors. According to its results, a successful visit to a cultural institution is conditioned by the ownership of social capital, in this case, knowledge of conventions and codes that make it possible to decipher works of art and is, therefore, a way of reproducing and maintaining social and class differences. Many other types of research have been carried out on this topic, generally, according to them, it is possible to define the difference between the basic sociodemographic characteristics distinguishing the cultural institutions audience as more educated people with higher income and social status. The proportion of women predominates over that of men. Among the most fundamental factors shaping and influencing a potential cultural institution visitor are attitudes to leisure activities, lifestyle, children’s experiences and personal background, and previous interaction with culture in general (Kesner, 2011, pp. 98-102). In contemporary cultural institutions focusing on architecture and art there is a transformation of the typical visitor, as the audience changes in the age and structure. Of the former segmentation groups that dominated in classical institutions, the profile of current visitors to the new ones differs due to the declining age of visitors (the 23-42 age segment prevails) and gender (compared to the previous predominance of women, men dominate in these institutions now). The new type of cultural institutions audience is generally more demanding, so, apart from thorough analysis and segmentation, it is necessary to offer a comprehensive product as well as a presentation and accompanying events associated with it. The aforementioned T.W. Adorno created a typology of the visitor, defined according to sociological, psychological, and historical criteria. At present, it is obligatory to include the entertainment and education aspect in creating the customer’s profile and the presentation associated with it. In the time of information chaos and abundance, the widest possible cultural audience should be addressed with an exclusive presentation. (Bačuvčík 2012, pp. 80-89).

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NETWORKS AND RELATIONS – NEW MEDIA IN CULTURAL INSTITUTIONS Social networks evolved in the first decade of the 21st century. Now they are based on users creating most of the content themselves, on the relationships between them, alongside with mutual comments, links, and ratings. So it is not only the largest possible number of users that is important, but also the greatest possible number of relationships between them above all. In contrast to traditional community servers, which have been designed for a relatively limited clientele, these networks are targeted towards the widest possible range of Internet users. According to Maiello and Roubal, there are several reasons for the growing interest in social networking, blogging, and chatting. It is beyond the “appeal of fast time and breaking the boundaries of work and leisure that these forms of communication represent sought-after sources of self-confidence”(Maiello and Roubal, 2020, p. 93). As a result, social networks become an integral part of their complete social existence, whereas online communication redefines, concretizes, and completes these social relations. Building a content strategy is closely related to the use of so-called new media. So what exactly are new media? “The old media involved the creative activity of a person who personally assembled elements of texts, images, or sounds into a particular composition or sequence. New media, on the other hand, are characterized by their variability. Instead of identical copies, a new media object gives rise to many different versions - before they are created by humans, they are automatically assembled by a computer” (Manovich, 2018, p. 75). Gallery institutions were significantly affected by the spread of Internet use among the general public. The accelerated development of computer technology, longdistance data transmission, and audiovisual means has now a major impact on the world of museums and exhibitions. The current intensive application of trends in new technologies does not only apply to the museum activity itself but can also be observed outside the exhibition space (Šebek 2014, p. 5254). Due to the building of a content strategy with the use of new media and technologies in the field of cultural and gallery institutions, the following can be considered as an example of the most important means of ensuring interactivity: • • • •

visitor tablets, projectors, touch screens, audio systems usage of QR codes along with additional information for visitors augmented reality as the embodiment of the experience of a particular work of art NFC technology, enabling connection to the institution’s online database (Šebek 2014, p. 52-54)

However, when using modern equipment and technology, it is essential to focus cultural institution visitor’s attention on the content of the exhibition, not only on the technologies that enable and accompany the exhibition. The main tools of a new form of content communication are as follows: social networks, blogs, blog posts, SEO optimization, PDF file sharing on the Internet, videos on YouTube, presentations on Prezi, webinars, emailing (with the subsequent evaluation of the obtained data). Halligen considers high-quality and consistent usage of the YouTube’s channel to be one of the most important components of visual communication use due to the possibility of disseminating content through videos. He claims that they can play a major role in educational activities related to cultural institutions. The key recommendations he makes in this regard include: • 808

To be creative and experiment. The key role play surprise and innovative content solutions.

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• • •

Video may not be made at a professional level, the content itself is important. The importance of interconnection with all communication channels is of paramount importance. Publish videos frequently, continuously, and with a storyline. (Halligan and Shah, 2010, p. 120)

Globally, social networks as a marketing platform work best in combination with various forms of promotion. In particular, the interconnection of all methods carried out through means of mutual reinforcement and referencing, is the limit for the successful use of these promotional tools (Bednář, 2011, pp. 27-78). According to Solomon, blogs and microblogs play an important role, for the potential of personalities and their institution-oriented views, in particular, can be used. Video sharing especially stimulates the discussion, but the correctness and relevance of the content must also be taken into account (Tuten and Solomon, 2014, pp. 5-6). Přikrylová believes that the fundamental difference of social media compared to other media and marketing tools lies in the creation and maintenance of a mutual relationship between museum and visitor and the related interactivity. The visitor of the cultural institution has control over the communication process and created relationships. The possibility of controlling the content, time, and path of communication plays a major role here. This interactive strategy then brings maximum communicative and marketing effect (Přikrylová and Jahodová, 2010, p. 219). Kotler considers the concept of an influencer in marketing and the related designation of opinion leaders in terms of social structure. The society is perceived by him as an entity formed by cells and small groups, whose proximity affects mutual communication and interaction. Between cells, there are people who play the role of liaison officers. They mediate the interconnection of groups and at the same time fundamentally influence group behavior. Thus, three types of people can be identified. The type of guru who drives the forces of all information persuasive flows. The type of clutch, that includes people who know a large number of groups and successfully communicate with. And the type of a salesman who ultimately achieves the persuasive effect of the whole group (Kotler and Keller, 2013, p. 591). The targeting is the selection of the most suitable segments on which cultural institutions will focus for the implementation of marketing communication. It is also an important moment in focusing on the potential visitor. Kotler regards Generation Y, or Millennials, represented by people born in 1979-1994) as the main group suitable for targeting and at the same time the most influenced one by marketing activities. Taking into account that this generation has encountered technology ever since its birth, marketing efforts must be focused in the direction of the use of all online channels. Direct offers aimed at emphasizing value, not stereotypes and stimulating the interest caused by online commotion or exciting events work best here (Kotler and Keller, 2013, p.259). The last phase of the process is the positioning, which results in an institutional or product image. In essence, it means emphasizing some aspect that distinguishes a cultural institution from the competitors, such as the unique personality of a collaborating artist, a celebrity, or the architecture of an institutionbuilding (Bačuvčík, 2011, p. 74).

THE STORY OF THE SELF – IDENTITY, SELFIES, AND MUSEUMS In the environment of cultural organizations and institutions, building a brand in continuity with communication in the form of stories is of particular importance. It is possible to combine there the use of the emotional power of the exhibited objects and symbols of personal stories. In the trends of regionalism 809

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and territorial decentralization on a European scale, museums capable of creating and further developing their stories play a very important role (Brabcová, 2003, pp. 33-34). Pictures of self (selfies) and sharing them with an online audience is an important part of the “contemporary self” (Kozinets, Gretzel, and Dinholp, 2017). The contemporary self is less stable than the individual identity which stems from consistent personal growth and development. The contemporary self is a more fluid, flexible subject of a quick change. Selfies also help us to construct the contemporary narratives of self (Kozinets, Gretzel, and Dinholp, 2017). Visiting museums (and taking/posting selfies from there) can both help with the construction of narratives of self and give the personal identity (Rounds, 2006, referenced in Kozinets, Gretzel, and Dinholp, 2017). While museums can help people to explore themselves, or to find their “alternative selves” (Kozinets, Gretzel, and Dinholp, 2017) as they are often cultural and historical (and well known) sites. Kozinets, Gretzel, and Dinholp (2017) claim that museums and works of art have become (in a sense) props and background material for a person to “act out the experiences that give their identity its uniqueness and their life its meaning”. Another way visiting museums (and sharing it with the world in the form of selfies) can be the way of building an identity (apart from seeming sophisticated) is through building a national or “tribal” identity. A good example of this would be a sort of a trend among young people in India, who are taking selfies in front of Indian Tribal Museums and sharing those on social media (Ross, 2019). A reason for using specifically Indian Tribal museums (and artifacts) in India as a backdrop could be that the people want to showcase their knowledge (or passion) for their country’s history and their own roots. This would also bring a sort of nationalistic or tribalistic aspect to the action of taking selfies in a museum, rather than only showcasing themselves as sophisticated. An interesting point about this trend is that, according to Ross (2019), people taking selfies in front of the museums and the artifacts in the museum felt some sort of shame for their actions. More detailed research would deserve digital evidence of new forms of relationship between museum and source communities. The term ’source communities‘ (Peers and Brown, 2003) emerged in the context of collections that originate from indigenous or ethnic groups, people living on ancestral lands, diasporic and migrant populations. We can observe the changes in the relation between source communities and institutions from confrontations and criticism of appropriating cultural heritage to innovative co-creation and participatory projects. This might be a cultural phenomenon, or it might have something to do with the possibility of a perceived negative perspective. Some of them could see the act as a “source of danger to the exhibited objects” (Ross, 2019), which might affect the selfie-takers frame of mind while taking the picture. Many museums around the world also have a policy of “no cameras allowed” in the museums. If people must break the rules to take the selfie, it might bring some shame to one’s taking the picture, especially in cultures that value the following rules highly.

SELFIE AS A FORM PROSUMPTION AND STEREOTYPING PRACTICE Modern digital technologies and new media, along with other ways of presenting content, are one of the most effective means of museum and gallery communication. This is due to the nature of these technologies, which help to use visitor’s abilities more effectively. Further research of the new digital relation model between the visitors and institutions can bring more understanding to the dynamic relationship between people and material objects. Places of material culture and heritage can not be considered as 810

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static items,as they are understood and interpreted by the viewers themselves in the set of interactions. When a photograph is shared on social networks, the process of commodification of an illusion starts (Cirklova, 2020). This illusion is perceived as evidence of the visit of the art institution. The illusion becomes a form of an ideal that is followed by others. Photos from galleries and museums, that have been taken there and shared on social media, can be understood as a commodified presentation of the personal experience in order to narrate a desired social ideal. Very often, the specifically selected institution or image from a museum may be massively reproduced and then becomes a form of a place representation or an aim of travel. The official accounts of cultural institutions on social media channels serve as an example of “good choices” for the visitors in taking photographs. This may be identified as a process of expert’s knowledge commodification that weakens the certainty and confidence of the individual (Durkheim: 1969) based on their own independent judgment. The process of constructing the desired selfie is understood here in the theoretical framework of “Representation” as addressed by Hall (1997: 24). According to Hall, representation is how we “make sense of the world of people, object and events, and how someone is able to express a complex thought about those things to other people or communicate about them through language in ways which other people are able to understand” (Hall, 1997: 16). Based on Hall’s concept of representation, it is necessary to stress the necessity of further research in the field of digital practice as a form of “stereotyping”. That should be done by following the links between representation and stereotypes that are closely connected with the production of an image of the self as long as with the consumption of art and heritage in digital societies. The social reality of selfies has made it possible through a series of technological innovations such as smartphones, front-facing cameras, cheaper mobile phone data, and the availability of the Internet directly in public institutions. The process of taking photos of the self is now easier than ever before. Photos can be published, shared, and sent out quickly and very easily. George Ritzer in his blog post (August 2015) published the text The Selfie as a form of Prosumption, where he considers the activity of taking selfies as a clear example of prosumption, because the producer of the image is „almost always it is first, ane frequently only, consumer“ (Ritzer, 2015). The aim of the students involved research that took place between 2019-2020 was to produce a visual analysis of modern selfie trends in order to understand current ways of enhancing identity via creating and consuming images and portraits. The changing patterns and techniques of visual self-presentation that pointed out shared cultural values were also addressed to. The latter one’s are now becoming certain global standards and institutionalized conventions of the personal snapshots shared on social media and professional commercial photography produced by museums (Cirklová, 2020). For the successful functioning of marketing communication, cultural institutions use not only the target visitor segment, but also the value and cultural orientation of consumers in relation to the consumption of the selected target group’s cultural activities. Cultural institutions emphasize a certain aspect that distinguishes them from the competition, such as collaborating with an interesting artist, celebrity, or associating their name with a unique architectural object. In terms of marketing it is the so-called positioning. For generations of visitors who grow up in the world of technology and social networks, the use of marketing tools concentrated in this direction is very effective. They respond best to digital content that is not stereotypical. It causes an uproar online and is considered as smart and cool. The influence of social media marketing on art institutions that are taking the leading role in defining the standards of the

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expected content, quality, and visual forms, instead of promoting a critical judgment of art lovers (and their coherent identity) is obvious.

YOU ARE THE MASTERPIECE – THE GALLERIES AS AESTHETIC BACKGROUNDS OF SELF-PROMOTION Some specific locations or art objects already have stereotypical characteristics. This may bring a sense of identity when such a photo is posted on social media. For example, the Eiffel Tower in Paris is considered as a symbol of elegance, romance, and beauty. People’s personal brands or their contemporary identity is defined by the places they are photographed in. When we enter #eiffeltower into a search string on social media, we get pictures of attractive people in perfect compositions. The colors are vibrant and impressive. The place looks like a dream or a utopia. The Internet and social media provide endless inspirations and recommendations when it comes to travel pictures. Moreover, the filtered photos enhance the unrealistic illusion of the destination. „Do it for the ‚gram“! is the term young people encourage each other to put a lot of effort into following a trend on Instagram with and trying to have their own style to stand out from the crowd. There is a lot of peer pressure on users and social media influencers to live up to the expectation of their audience, to follow trends but at the same time to be unique and interesting. Social media allows creating the illusion of glamour and romance even when there are a lot of discrepancies between the shared picture and the reality of the travel. The centrality of the location and/or art object captured in images is a crucial element of stereotyping and enhancing the personal identity. Some selfies are clearly a product of successful marketing communication that uses distinctive identification symbols associated with progressive visual presentation. The institution thus becomes a brand that is very easy to remember. As the illustration of the above-described practice that is linked with the local setting the DOX Contemporary Art Center in Prague could be presented. When pronouncing the name of the DOX gallery, most of their regular visitors and admirers will be reminded of it by the airship located above the courtyard of the building. The Gulliver airship, inspired by flying machines from Jules Verne’s books, is one of the institution’s strongest symbols. It was designed in 2013, by one of the leading Czech architects, Martin Rajniš, who can rightly be called the greatest expert on wooden structures. The Gulliver building is supported by a wooden structure, which creates an unusual contrast to the reinforced concrete structure of the gallery itself. The cladding is conceived as a dense system of laths, delimiting the space and at the same time leaving transparent intersections allowing interaction with the environment. It serves mainly to connect and confront literature and contemporary art (ARCHTV, 2018, online). The program of the institution is deemed to be very balanced, interactive, emphasizing the current best artistic, musical and literary representatives. In the current pandemic situation, this institution responds well to traffic restrictions and makes many of its exhibitions and concerts available online. Furthermore, in this extremely challenging time for culture, it tries to make some events available free of charge through its communication channels, such as the free broadcast of a concert from the Kuppelhalle in Berlin as part of the Strings of Autumn festival. For those interested in the current exhibition, which cannot be visited due to the travel restrictions, a YouTube channel, on which video tours are published has been launched.

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The Facebook account has 68,000 followers. Contributions are made almost every day, presenting all activities. The Institution also receives an overview of their fans using the Facebook Insight tool, which allows them to focus the communication considering the demographic segmentation or targeting by the user’s gender. The art gallery is also very active on Instagram. The gallery’s Instagram account is viewed by a respectable number of 22,800 followers. Presentations of all events and exhibitions organized by the institutions are well prepared and the individual contributions are evaluated and discussed by the followers. The gallery also works very actively with the so-called stories, which it creates for almost all presentations. The quality of the Twitter account’s management is evidenced by the award from 2013 when the DOX account was placed in the Tweet of the Year survey. The Twitter account has 6, 500 followers and it exercises a significant influence over the exhibition’s audience. The social media content of the art institution tells many visitors who are following and consuming the exhibitions the story of the art. It predefines what visitors should see and understand as the central piece of the exhibition. The additional elements of the entertainment can be associated with interactive, immersive and multi-sensory technologies that are more often used in galleries and museums. Based on the demand of the youngest visitors to be in the center of the documentation picture, a new form of cultural institutions have recently emerged. Seattle Selfie Museum (https://seattleselfiemuseum. com) can be considered as an exemplary and pioneering institution taking the interactive and digital dimension into the further possible end. While curators and art critics are debating whether we can call this type of entertaining place a museum when the place has no educational mandate, other similar institutions are emerging in tourist popular places. Some of them opt for education linked elements in their name referring to selfie installations backgrounds of the optical illusions settings as in the case of Museum of fantastic illusions in Prague (https://muzeumfantastickychiluzi.cz/en/). The promoters call it the new form of immersive art providing a multisensory level that gives visitors a chance to work with their own emotions and experience the visuality in a new form. Further research should focus on critical analysis of this digital and social practice. When the primary aim of the institution and of the visitor is oriented towards taking selfies, the goal is consumerism rather than art-orientation. The official webpage promoting the Selfie Museum in Seattle is defining it as a place where the visitor becomes the central masterpiece because “When somebody’s taking a picture of you, you become a piece of art within the installation” says Igor Benchak, one of the founders of the Selfie Museum (Crosscut, 2020). To conclude the observation of digital practices connected with contemporary social identities and art institutions, it is necessary to mention the collaboration of popular singer Beyoncé with Louvre in 2018. According to The Guardian (Jan 3, 2019), the Louvre, the world’s most visited museum, broke last year all ticket office records. More than 10 million people saw its Paris collection of fine arts and antiquities boosted by foreign tourists and the interest in Beyoncé and Jay-Z filmed there. This collaboration brought the younger generation visitors back to the galleries, as they started to be interested in the original meaning of the art pieces selected for the video and also in the reinterpretation of Western paintings and sculptures by the musicians. Their work was seen by critics as „a celebration of black bodies and empowerment in an institution that was built on the spoils of conquest and imperialism“ (Smalls, 2018).

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FUTURE RESEARCH DIRECTIONS In recent years in many countries, a major reform to redesign the public museums took place. However, some scholar argues that this market-based transformation results in endangering the concept of public value as the main objective of these institutions. On the other hand, museums and public spaces are defined as places that contribute to the public by collecting, preserving, and interpreting the tangible objects, documents, happenings, and experiences that shape our societies. In the era of new media, growing tourism, and redefining relationships between persons and cultural heritage brings to existence many dynamic processes that need to be researched. The processes involve interweaving sets of human actions, beliefs, skills, and materials in the process of interpretation of physical objects as the source of identity for the civil society and for particular individuals. In further research collaboration between experts, the institutions, and the general public is vital for effective engagement aimed at improving the knowledge about the current digital practices. Together, we are going to explore diverse cultural perspectives, trans-generational practices of consumption of culture, art, and local-to-global connectedness. Future projects will promote innovative narratives of participation in art viewings such as virtual museum tours, on-line exhibitions, and educational programs that became trend during the global COVID-19 pandemic.

CONCLUSION Umberto Eco (2015) has addressed the issue and the possibility of the free reader’s activity which subjectively influences the interpretation of the work in the 1960s already, that is, a long time before the expansion of social media. Understandings of the work can vary significantly, and Eco considers it a creative process when the recipient, or visitor, is left free to elaborate on artistic works. From this theory of an open artwork emerges the possibility of direct spectator participation. As the passive viewer is transformed into the active one in terms of interaction with the exhibition, the origin of the experience based on the aesthetic exhibition qualities changes simultaneously, and the visitor’s position becomes more and more central, in some instances, the art objects serve as a background or a frame of reference. At this point, Jean Baudrillard’s work (1994) should be introduced, as well as his concepts of culture and identity in the overwhelming reality of information with the decrease of meaning. Social media centered around visual content contributes to reality creating more pictures with less meaning. There are so many images in our world, that we are not able to absorb them all and understand the ideas they carry. Social media creates stereotypes that make it easier for us to see the reality, as there is someone else deciding for us what to make a reality. By replicating individual images and stories we accept them as our reality as well. As a result, an individual loses not only his identity, but also his experience in working with the complex reality of the world. It happens because the information that is no longer explained to us and is adjusted to the form of social media becomes less understandable and important. The presented work is just an introduction to the dynamic processes of the identity’s social construction with the interaction of art and digital visual manifestation of our time as a representation of the challenging complexities of our world.

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Centrum současného umění DOX. (2020). [DOX Center for Contemporary Art]. DOX. https://www. dox.cz/?gclid=Cj0KCQjw2or8BRCNARIsAC_ppya1drmqFIG7rXaTCEGvjxlK1t5cL_1zEfB_3Xvrnw h3o5oEdxxAhF4aAvr1EALw_wcB Cirklová, J. (2020). Reaffirming Identity Through Images. The commodification of Illusions in the Contemporary Presentation of Self. Methaodos Revista De Ciencias Sociales, 8(1), 103–110. doi:10.17502/m. rcs.v8i1.351 Danesi, M. (2012). Popular culture: Introductory perspectives. Rowman & Littlefield. Eco, U., & Obstová, Z. (2015). Otevřené dílo: Forma a neurčenost v současných poetikách [Open work: Form and uncertainty in contemporary poetics]. Argo. Fiske, J., & Bílek, P. A. (2017). Jak rozumět populární kultuře [How to understand popular culture]. Akropolis. Foucault, M. (1972). The archaeology of knowledge. Tavistock Publications. Gere, C. (1997). Museums, contact zones and the internet. Archimuse. https://www.archimuse.com/ publishing/ichim97/gere.pdf Hagoort, G. (2009). Umělecký management v podnikatelském stylu [Artistic management in business style]. Praha: KANT pro AMU. Hálek, I. (2014). Český management v evropské kultuře: Hledání východisek z krize současné společnosti na základě historicko-teologické analýzy [Czech Management in European Culture: Searching for Solutions to the Crisis of Contemporary Society Based on Historical-Theological Analysis]. Tribun EU. Hall, S. (1980). Recent developments in theories of language and ideology: A critical note. Routledge. Hall, S. (1997). Representation: Cultural representation and signifying practices. The Open University/ SAGE. Halligan, B., & Shah, D. (2010). Inbound marketing. John Wiley & Sons. Kesner, L. (2011). Marketing a management muzeí a památek [Marketing and management of museums and monuments]. Grada. Kotler, P., & Keller, K. L. (2013). Marketing management [Marketing management]. Grada. Kotler, P., & Kotler, M. (2013). Market your way to growth: 8 ways to win. John Wiley & Sons. Kozinets, R., Gretzel, U., & Dinhopl, A. (2017). Self in Art/Self As Art: Museum Selfies As Identity Work. Frontiers in Psychology, 8, 731. Advance online publication. doi:10.3389/fpsyg.2017.00731 PMID:28536549 Lachmann, F. (2014, January 16). Stuart Hall: kulturální studia, kultura a politika [Stuart Hall: cultural studies, culture and politics]. Sociální Teorie. http://socialniteorie.cz/stuart-hall-kulturalni-studia-kulturaa-politika/ Lipovetsky, G. (2020). Říše pomíjivosti [The realm of transience]. Prostor.

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Maiello, G., & Roubal, O. (2020). Média a kultura: Od primární orální kultury ke konstrukci kyberprostorových identit v éře digitální komunikace [Media and culture: From primary oral culture to the construction of cyber-spatial identities in the era of digital communication]. Praha: Vysoká škola finanční a správní. Manovich, L., & Janoščík, V. (2018). Jazyk nových médií [The language of new media]. Praha: Univerzita Karlova, nakladatelství Karolinum. Marciano, O. (2014, April 21). James Clifford’s “Museums as contact zones.” RxArt. https://rxart.net/ blog/james-cliffords-museums-contact-zones/ McQuail, D. (2009). Úvod do teorie masové komunikace [Introduction to the theory of mass communication]. Portál. Muzeum fantastických iluzí. (2020). Museum of fantastic illusions. Home page. https://muzeumfantastickychiluzi.cz/en/ NIPOS, Národní poradenské a informační středisko pro kulturu. (2014). Proměny kulturní infrastruktury v České a Slovenské republice po roce 1989 [Changes in cultural infrastructure in the Czech and Slovak Republics after 1989]. NIPOS, Národní poradenské a informační středisko pro kulturu. Peers, L. L., & Brown, A. K. (2003). Museums and source communities: A Routledge reader. Routledge. Přikrylová, J. (2019). Moderní marketingová komunikace [Modern marketing communication]. Grada Publishing. Resch, M. (2018). Management of art galleries. Phaidon. Ritzer, G. (2015, August 12). The selfie as a form of prosumption. George Ritzer. https://georgeritzer. wordpress.com/2015/08/12/the-selfie-as-a-form-of-prosumption/ Ross, I. (2019). A guilty pleasure? The Indian museum as a popular backdrop for selfies. Museum Management and Curatorship, 34(4), 433–447. doi:10.1080/09647775.2019.1596827 Roubal, O., & Zich, F. (2014). Marketingová sociologie: Marketingová komunikace a moderní společnost [Marketing Sociology: Marketing Communication and Modern Society]. Praha: Vysoká škola finanční a správní. Schorch, P. (2013). Contact zones, third spaces, and the act of interpretation. Museum & Society, 68–81. https://epub.ub.uni-muenchen.de/69605/1/223-451-1-SM.pdf Šebek F. (2014). Moderní technické prostředky komunikace muzeí s návštěvníky [Modern technical means of communication between museums and visitors]. Praha: Asociace muzeí a galerií České republiky. The Guardian. (2019). Beyoncé and Jay-Z help Louvre museum break visitor record in 2018. https:// www.theguardian.com/world/2019/jan/03/beyonce-jay-z-help-louvre-museum-break-visitor-record Tuten, T. L., & Solomon, M. R. (2020). Social media marketing. SAGE Publishing.

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Vansynghel, M. (2020, January 31). Welcome to Seattle’s new selfie museum, where you are the masterpiece. Crosscut. https://crosscut.com/2020/01/welcome-seattles-new-selfie-museum-where-you-aremasterpiece Weitz, M. (1956). The Role Of Theory In Aesthetics. The Journal of Aesthetics and Art Criticism, 15(1), 27–35. doi:10.1111/1540_6245.jaac15.1.0027 Zahrádka, P. (2010). Estetika na přelomu milénia: Vybrané problémy současné estetiky [Aesthetics at the turn of the millennium: Selected problems of contemporary aesthetics]. Barrister & Principal.

ADDITIONAL READING Birch, D. (2014). Identity is the new money. ProQuest Ebook Central https://ebookcentral.proquest.com Bligh, M. C., & Riggio, R. E. (Eds.). (2012). Exploring Distance in Leader-Follower Relationships: When near Is Far and Far Is Near. ProQuest Ebook Central, https://ebookcentral.proquest.com Cerra, A., & James, C. (2011). Identity shift: Where identity meets technology in the networked-community age. ProQuest Ebook Central https://ebookcentral.proquest.com Czarniawska, B., & Gagliardi, P. (Eds.). (2003). Narratives we organize by. ProQuest Ebook Central https://ebookcentral.proquest.com Kendal, M. (2019). Cyber and you. ProQuest Ebook Central https://ebookcentral.proquest.com Powell, H. (2013). Promotional culture and convergence: Markets, methods, media. ProQuest Ebook Central https://ebookcentral.proquest.com Ruvio, A. A., & Belk, R. W. (Eds.). (2012). The Routledge companion to identity and consumption. ProQuest Ebook Central https://ebookcentral.proquest.com

KEY TERMS AND DEFINITIONS Art Institutions: Public or private places where art viewing is taking place. Consumer Culture: Is a form of material culture facilitated by the market. Social science is interested in research of sets of relationships between the consumer and the material object or services purchased. Digital Media: Is content encoded in machine-readable format, created, edited, and distributed on electronic devices, broadcasted through a screen. Identity: By identity we mean experiencing who an individual feels to be. It is a process of internalizing sets of characteristics and values at the same time also realizing differences from other people and groups. Prosumption: Is a process that involves both production and consumption without focusing on either one. In case of selfies the producer is often the direct consumer-viewer of the photograph. Selfie: A photograph taken by oneself, the most often it is taken with a smartphone or and shared via social media.

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Social Media: Social media are digital tools that allow people to create, share, and exchange information and visual content with each other within a virtual community or network. Stereotyping: An overly simplified generalization while interpreting objects or people, regardless of individual differences.

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Chapter 45

Processes of Socialization to Sexuality and Discrimination in the Web Society: An Exploratory Research on Transgender People Marianna Coppola University of Salerno, Italy

ABSTRACT The diffusion of new media, of online communication, and the increasingly evident overlap between online and offline environments generates a specific question for scientific research on how these contents can represent an opportunity for “emancipation” and at the same time new areas in which can experience processes of exclusion, in particular for the LGBT community. In this sense, social media offers transgender people a wide range of tools and applications to create new knowledge, interact with other people, create new meeting opportunities, or trace new relationships and/or new emotional and sexual experiences. This research work aims to investigate the psychological, relational, and social aspects of transgender people who use social media and dating apps as communication spaces and relational environments in order to outline the peculiar aspects of media consumption, regulatory access and processes of stigmatization, and social discriminations by the web.

INTRODUCTION The diffusion of new media, online communication and the increasingly evident overlap between online and offline environments, generates a specific request of scientific research on how these contents can represent an opportunity for “emancipation” and at the same time new areas in which to experience discrimination and processes of exclusion.

DOI: 10.4018/978-1-7998-8473-6.ch045

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 Processes of Socialization to Sexuality and Discrimination in the Web Society

These processes of communicative emancipation would seem particularly suitable for LGBTQ subcultures and specifically for transgender people. In this sense, social media offer transgender people a wide range of tools and applications to create new acquaintances, to compare themselves with other people from the LGBTQ community, to create new opportunities for meetings or to find new relationships and/or new emotional and sexual experiences. In order to satisfy this growing demand for online socialization, in recent years several apps for dating have been created for non-normative identities and non-heteronormative sexual orientations. In fact, it is possible to find apps for homosexual dating (e.g. Grindr for male homosexuals and Wapa for female homosexuals) and dating apps for transgender people (e.g. the Transgender app). These new communicative arenas would become true “virtual marketplaces” where sexual identities and affective, emotional, and sexual compatibilities can be experimented with and tracked. In this chapter we will present a research, with a qualitative approach, which has set itself the goal of investigating the psychological, relational and social aspects of transgender people who use social media and dating apps as communicative spaces and relational areas, in order to outline the peculiar aspects of media consumption, the regulatory lines of access and the processes of web-mediated social stigmatization and discrimination. A first part will briefly present a description of the current scientific literature on the subject, analyzing the concepts of sexual identity, gender identity and transgenderism, fundamental aspects to build an epistemological frame of reference in which to understand and interpret both the results and the reflections expressed; in addition, the first theoretical part will be completed by the presentation of two “ecological” theories of human interaction: the ecological model of Bronfenbrenner (2006) and the theory of sexual markets (Gagnon e Leumann, 2004). Also within the first part of the chapter, the concept of medial capital will be briefly presented, and the main social media and dating apps used within the process of socialization to sexuality of transgender people will be presented. A final part, however, will describe and analyze the psychological, social and relational aspects expressed through the use of social media and apps for dating, particularly in relation to the processes of social inclusion and exclusion experienced through the web by transgender people in Italy.

THE EVOLUTION OF SEXUAL IDENTITY: FROM THE VETEROSEXUAL MODEL TO THE MULTIDIMENSIONAL MODEL The concept of sexual identity, of the relationship between biological sex and the social construction of gender, of the complementarity between male and female, of non-normative sexual identities and orientations, have been debated in Woman’s Studies and LGBT Studies from the second half of the 1980s to the present day, initiating a rich scientific production that has fueled the sociology of sexuality (Piccone Stella, Simonelli 2007; Leccardi, 2002; Saraceno et al. 2008). Thanks to the interaction with other important disciplines that have sexuality as their object of study (just think of psychology, biology and gender medicine), in recent years the multidimensional concept of sexual identity has been consolidated, overcoming the veterosexual model that had dominated studies and scientific research on sexuality for over three centuries.

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The veterosexual model postulates the conception of human sexuality articulating it on five assumptions considered normative and from which to create the ideological and functional separation of normal sexuality from abnormal and deviant sexuality; specifically, the veterosexual model admits as normative 1. 2. 3. 4. 5.

the anatomical dichotomy between the biological sexes; the gender binarism described by the complementarity of male and female gender; the heteronormative conception of erotic and sexual attraction; the immutability of gender and gender expressivity; finally, the reproductive and generative function of sexuality (Ruspini, 2017).

The anatomical dichotomy between the biological sexes describes the criterion according to which in nature there are only and only two biological anatomical sexes, male and female, through the unambiguous recognition of somato-anatomical configurations clearly differentiated according to genitalia, hormonal arrangements, chromosomal kits and reproductive functions. The main criticism to the criterion of anatomical dichotomy is the existence in nature of somato-anatomical configurations not definable as intersexuality and hermaphroditism, in which there are the simultaneous presence or absence of sexual characters of one or the other sex. Gender binarism describes the social and/or cultural criterion according to which there is the existence of only two sexual genders: male and female, creating de facto the univocal correspondence between biological sex and social and sexual gender. The heteronormative conception of erotic and sexual attraction describes the criterion by which only the heterosexual orientation is considered normative, relegating to the category of non-normativity and abnormality all homoerotic or bi-panerotic orientations. The criterion of the immutability of gender and gender expressivity describes the impossibility of an individual to change his correspondence between biological sex and sexual gender, describing normativity only in the cis-gender condition and considering the other non-normative identity constructions (transgender identity, genederqueer, genderfluid). Finally, with the criterion of the reproductive and generative function of sexuality, sexual activity aimed at the continuation of the species and with generative purposes is established as normative, reaffirming and consolidating the social and cultural hegemony of heteronormativity. The multidimensional model, thanks to the contributions of feminist movements and LGTBQ, criticizes and subverts the gender order established by the veterosexual model and expands, complexifies and articulates the construct of sexuality, giving it back the variance that allows to accommodate within it all the possible combinations of biological, psychological, social and cultural aspects. Specifically, the multidimensional model of sexuality describes five constituent elements of sexual identity: 1. 2. 3. 4. 5.

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biological sex; gender identity; sexual orientation; the gender role; gender expressivity.

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Biological sex describes, as already provided in the veterosexual model, the somato-anatomical conformation of the individual, introducing the intersex condition to the dichotomous male and female configuration. The innovation lies in the creation of a continuum between male and female, placing intersexuality in an intermediate position and giving back to the condition of hermaphroditism the biological nature and the possibility of being treated from a medical and health point of view. Gender identity indicates the unitary and persistent perception of oneself, or self-identification, as belonging to the male or female or ambivalent gender (Simonelli, 2002). This criterion breaks down the hegemony of the cis-gender model as normative and adds to this the transgender condition, which within it contemplates different identity constructions also subverting another pillar of the veterosexual model, gender binarism, with the introduction of gender identities nuanced and not stable (such as nonbinary and gendefluid). Sexual orientation represents erotic and sentimental attraction to another person. While in the veterosexual model was considered normative and possible only the erotic and sentimental orientation towards the opposite sex (heterosexual), in the multidimensional model of sexual identity are contemplated different sexual orientations and considered as an expression of the process of self-determination; there are, in addition to the heterosexual orientation, the attraction to persons of the same sex (homosexual orientation), to both sexes (bisexual orientation) and to no sex (asexual orientation). Gender role describes the set of expectations, social behaviors, and cultural beliefs ascribed to one or the other gender. This criterion establishes the behavioral script of masculine and feminine with what is considered primarily attributable to being a man or being a woman. Gender expressivity represents the set of phenotypic and expressive characteristics that can be traced back to the models of masculine and feminine, representing aspects such as clothing, makeup, social attitude, etc. Thanks to the multidimensional model of sexual identity, contemporary scientific research has opened up to the study and analysis of sexual identities and orientations considered non-normative, focusing mainly on homosexual people and people with not cissexual gender identities (transsexual and transgender people). It is important to distinguish two terms that are often confused and mistakenly considered synonymous: the concept of transgenderism and the concept of transsexuality. Transgender is the umbrella term used to indicate the condition of people who do not recognize themselves in the sex assigned by birth and identify with the gender of the opposite sex indicating the latter as corresponding to their inner feelings. This condition generates a misalignment with the traditional expectations, roles and attitudes of the biological sex gender, creating a clear psychological, emotional and social discomfort (Ruspini, 2009; Connell 2011, Masullo, Coppola 2021). The transgender condition, therefore, includes those who were born male but feel like women (MtoF), those who were born female but identify with masculinity (FtoM), those who do not assume a precise gender identity and remain suspended in a non-definition of gender (genderqueer), those who reject binary gender assignment (no binary), and finally those who reject a gender identity and do not find themselves in any label (agender). The term transsexual, however, describes the condition in which a subject with gender mismatch has already begun a process of transformation of their physical and sexual characteristics in order to assume the new gender identity from a somato-biological point of view, as well as legal and social (D’Agostino, 2103; Valerio et al. 2013).

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THE LEVELS OF HUMAN INTERACTION: ECOLOGICAL MODEL OF BROFENBRENNER The construction of gender identity from a constructivist perspective and according to the multidimensional model requires reflection on the importance of social contexts and interactions experienced within for human development and social skills. One of the main theories, straddling psychology and sociology, is represented by the ecological theory formulated by Urie Brofenbrenner (1992). The basic assumption of the ecological model is the interdependence between the processes of growth and identity construction and the contexts of life in which the subject is inserted. The object of analysis, therefore, of the ecological perspective are the processes of signification and construction of meaning produced, shaped and renegotiated in the social systems of individuals, with a concentric and interdependent vision. Brofenbrenner writes: the characteristics of the person at a particular point in his or her life are a joint function of the characteristics of the person and the environment over the course of the person’s life up to that particular point in time (Brofenbrenner, 1992: 190). The author distinguishes 4 different levels of human interaction, in which the individual experiences different levels of social intimacy and at the same time different levels of social control and discrimination, processes that are decisive for the construction and formulation of personality and social and normative development. The most external and social level of human interaction is represented by the macrosystem, understood as the superstructural context that represents the frame to other social contexts. It can be assimilated and often superimposed on the concept of culture, with its own normative systems and its own value system, consisting of social representations, values and ideologies. The macrosystem, therefore, represents the background and the containment system within which the other contexts necessarily refer to the normative subsystems and is necessarily conditioned and coconstructed with the cultural system of reference. In fact, the author, referring to the contributions of cultural anthropology and studies on the normative systems of other cultures, refers to the “culturalist” nature of the macrosystemic framework, pointing out that the normative lines of social inclusion and exclusion are different, for example, between Western countries and countries of the Far East, where behaviors, practices and actions can be normative in one culture and deviant and abnormal in others. A second level of social interaction is called the exosystem and represents situations in which the individual does not participate directly, but in which events and situations occur that indirectly influence and shape socialization processes. Brofenbrenner, in order to better clarify the notion of exosystem proposes an example that represents the most explanatory, the work context of parents and/or partner. A third level, where the level of intimacy and socialization becomes increasingly restricted, direct and intense, is represented by the mesosystem where generally two or more intimate and elective social contexts interact. It is a context where the effects on the individual of social interactions and socialization processes can often be traced. A classic example reported in the literature is the interaction between work and family contexts, between formal and informal contexts.

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A fourth level of social interaction, with a direct level of intimacy and sociability, consolidated with an important emotional, relational and social impact is represented by the microsystem thus constituted by the set of interactions between the person and the direct and elective environments, in which intense, consolidated and interdependent relationships are experienced. These microsystems represent the reference groups and contexts where the direct value and normative systems are mainly consolidated. Included in microsystemic contexts are the family, school, one’s work context, support networks and associations. Within the microsystems it is possible to trace a further context considered by the author to be important: the reference group often considered to be superimposable on the microsystems, but at other times it can be represented by social systems external to the microsystems of reference (family, school, work) playing a fundamental role in the description of the parameters considered by the individual as normative and at the same time representing the social system within which the individual intends to experience and experiment with processes of identification and social comparison.

SOCIAL INTERACTION IN SEXUALITY: THEORY OF SEXUAL MARKETS OF GAGNON AND LAUMANN Within the wide range of human and social interactions, sexuality has also been analyzed through the constructivist matrix of thought. Sexuality, considered by many scholars as one of the social interactions with the highest degree of intimacy and personal and social involvement, became the object of study of sociology only in the second half of the last century. One of the most influential theories on the study of sexuality as a social and systemic interaction is represented by the theory of sexual markets formulated by Gagnon and Laumann (2004). The authors expand on a previous theorization formulated by Gagnon and Simon (2005) known as the theory of sexual scripts1, emphasizing in their analysis the importance of social and cultural contexts in sexual interactions and behaviors, describing the social contexts where sexual interactions are experienced as sexual arenas where sexual behaviors, practices, and expectations between partners are dramatized according to culturally recognized, accepted, and regulated norms and normative systems. These arenas represent sexual markets that may be offline or both online and virtual. Gagnon and Laumann (2004) identify five factors that determine the construction of sexual markets: a) social networks: made up of networks of interpersonal relationships, both real and virtual, within which the subject can initiate processes of social and sexual interaction; they are influenced by parameters of affinity and correspondence (ethnicity, religion, ideologies, geographic belonging, etc.); b) physical space: made up of networks of interpersonal relationships, both real and virtual. They are influenced by parameters of affinity and correspondence (ethnicity, religion, ideologies, geographic belonging, etc.); b) physical space: it represents the geographic boundaries (real and virtual) within which a partnering process can be expressed; c) sexual culture: the cultural construction of the sexual, it can be internal, related to specific social and cultural groups, and external, related to combinations of rules, roles and expectations regulated by the macro-culture of belonging; d) sexual scripts; e) institutional environments: religious organization, the educational and pedagogical system, the normative and legislative system, which contribute to the construction of the rules and norms that establish the ideal line between what is recognized as normative from aberrant.

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Sexual markets are organized, both in face-to-face interactions and in web communities, by plazas that represent the places (physical or virtual) where individuals can implement sexual scripts, provide information about their sexual capital, and construct sexual coordinates to orient and direct sexual behavior, their own and that of others. In sexual markets and in the construction of a “sexual theater scene”, the concepts of sexual capital and sexual coordinates are therefore important: sexual capital refers from the set of characteristics of the physical, affective, psychological, and erotic aspects that confer the erotic value of an individual who is about to “descend” into the sexual arena represented by the sexual marketplace (Green, 2014); sexual coordinates represent the informational constructions that the individual provides in the sexual marketplace, adding to his or her own social capital, the social capital of the sexual object sought in the sexual marketplace (Masullo, Coppola, 2021).

WEB SOCIETY AND SOCIALIZATION: FROM THE OFFLINE TO THE ONLINE The relationship between technological change and cultural change can be better understood by taking an overall view that allows us to focus on the close connection between micro and macro transformations linked to the digital revolution. In order to take this overview, it is necessary to look at the way in which communication infrastructures, on the one hand, and the use that users make of them in their daily lives, on the other, have transformed the very organization of daily life, the forms of our relationships, our relationship with leisure, entertainment and our knowledge of and about the world, as well as the way we relate to institutions and the market. Several authors have endeavored to formulate interpretative paradigms functional to explain the transformations that have affected Western societies as a result of innovation in the field of communication and information technologies. Initially, the paradigm of informationalism, which sees in information a new model of development of production that takes the place of the agrarian and the industrial one, has become popular. The information society (Castells, 2001) is the result of a third industrial revolution that is not simply based on the centrality attributed to information and knowledge, to which all societies, moreover, have given importance and centrality. On the contrary, the web society is based on a vision of information as raw material (Castells, 2001), whereby the information and knowledge produced are applied to devices that in turn generate information and knowledge (Bennato, 2009). This view laid the foundation for the development of a new paradigm that Manuel Castells exemplified in the concept of the network society (Castells, 2001; van Dijck, 2005). The network society is not only the consequence of an innovation of technologies and a change in the structures assumed by capitalism, but it is the result of a cultural transformation based on the alternative projects and values promoted by social movements since the seventies, which are oriented towards individual freedom and social autonomy and support identity claims (Bennato, 2009). Contrary to the industrial society, where the dominant model of communication was vertically oriented, in the network society communication assumes a horizontal structure, capable of relating the center and the periphery of the world, the global and the local. Therefore, the network is not only a means, but also a place that relates social structures and their activities in a system of nodes and junctions that do not necessarily converge around a single center, because each node is autonomous and connected to other nodes.

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Therefore, institutions, practices and social relations, cultural and economic policies are organized in a network and can relate to each other without being spatially and temporally constrained. A “real virtuality” is thus structured that is: “virtual insofar as it is constructed through electronically based virtual communication processes. It is real (and not imaginary) because it is our fundamental reality, the material basis on which we nose our existence, build our systems of representation, practice our work, connect with other people...feed our dreams” (Castells, 2001). Unlike Castells, van Dijck believes that social and media networks are not the very substance of the network society, but represent only a mode of organization of the most important structures of society, the main element of which, according to van Dijck, remains individuals. One of the evolutions brought about by the network society and by being always on is what Wellman (2002) called networked individualism. This concept describes the condition of contemporary individuals who are increasingly disengaged from their parental and/or neighborhood networks and increasingly anchored to a multiplicity of social networks in which they connect to individuals rather than groups, including through social networks that have increased their centrality. We are therefore witnessing a thinning of the barriers that made people think of online and offline as separate dimensions. No longer able to count on the solidity of parental relationships and/or neighbors, the link between what happens online and offline becomes increasingly strong because many of the social practices that take place online become compensatory of the essence of what offline seems to have lost solidity. In a recent review of Castells’ model, it was observed how digital platforms indicate and describe technological systems that are configured as real environments in which economic and social relations take place. The new information ecosystem that derives from this has been called “platform society” (Van Dijck, Poell, 2013): it is based on the very strong intertwining between commercial organizations that propose the sale of goods, but also of user data; social relations understood as places where communicative practices, forms of being together and participation in public life are exchanged; and technologies that allow both citizens and institutions to enter into relationships and achieve their goals. The platform is therefore not only an infrastructure, but a real economic model that feeds and grows on the basis of data produced by users, which will be useful not only to the development of the platform itself, but also to subjects outside the platform. Talking about new media today represents a sort of terminological contradiction, since the idea of the new referred to the rise of ICT (Information Communication Technology) seems to have been overtaken by its “quotization” to the point of arriving, as some authors argue, at a trivialization of the Internet (Grahman, 2004) and the tendency of CMC (Computer Mediated Communication) towards ordinariness. The concept of new, of course, finds its raison d’être at a time when the innovativeness of emerging technologies, their permeability to personally specialized users and the different ways of using them with respect to the initial design purposes marked a watershed between new media and mass media, soon to be renamed traditional media, old media or mainstream media. The first issue of New Media and Society magazine, published in 1999, contained a special section entitled “What’s New About New Media?” (Silvestrone, 1999), in which several authors, in an attempt to answer this question, offered their point of view on the role of the media in society and its effects, converging towards the idea that perhaps the time was not yet ripe to talk about their real development. 827

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The same journal in 2004 reproposed the question of formulating a question in: “What’s Changed about New Media?” (Liewvrow,2004). The scenario, in fact, was already configured as profoundly different, the pervasiveness of new media in the everyday life of individuals, but also in different areas (from politics to ‘education, from economics to leisure), had led to the awareness of a mainstreaming in new media. In other words, the new media have spread rapidly to the point of massification based on the fact that users have built their daily routines on them. It is significant that among the themes of reflection proposed for the 2020 pre-conference of the European Communication Research and Education Society is the idea of considering “digital disconnection” as a real field of research, that is, the practices of resistance, detoxification and abstention from digital technologies. The shift from the novelty of new media to their trivialization is attributable to a number of aspects, according to Graham (2019), the first of which is that they have become ordinary because they have produced ordinary. They have slowly become invisible tools, undergoing a miniaturization that has contributed to make them imperceptibly present in the objects we are surrounded by (just think of toys) beyond the digital devices we use for communication purposes. Moreover, normalization is due to the fact that the world of information seems to have abandoned the extreme enthusiasm or fears with which the advent of technology was initially greeted, but has begun to move towards more balanced discourses in which the controversial role of new media is analyzed. Another aspect that is indicative of the downsizing of the concept of new media is probably the issue of terminology. The concept of social media in particular refers to “a group of Internet - based applications that rely on the technological and ideological foundations of Web 2.0 and that allow the creation and exchange of User Generated Content” (Kaplan, Haeinlein, 2010). Web 2.0, compared to web 1.0, gives everyone the opportunity not only to create and publish content but also to modify in a participatory and collaborative way such content and applications. User Generated Contents (UCG) have specific characteristics, they are contents produced by users themselves that: must be publicly accessible, must be the result of a creative effort by those who produce them so that they are not copies of existing content, they must not be created with the market in mind or following professional routines (Kaplan, Heinlein, 2010). According to some authors, the elements that make social media distinguishable are based on certain key aspects, such as: ensuring a different degree of social presence, understood as physical, visual, auditory contact between two individuals; offering a quantity and wealth of information that once exchanged allow to reduce the risk of ambiguity in the communication of the same subjects involved. Another aspect is the different degree of possibility of presentation and disclosure of the self, i.e. the possibility that users have to disclose personal information in order to control or influence the idea that others have of them and that they want to give of themselves. In view of these variables, social networks and virtual worlds offer a high level of social presence, communicative richness and a high level of self-presentation. The same variables, on the other hand, result in a lower degree in social media such as Wikipedia or Youtube. Therefore, although Facebook, Instagram, Youtube and Wikipedia are classifiable environments as social media, there are differences within them in terms of the possibilities offered by interactions, in self-representation and in the sharing and exchange of content. Beyond definitional aspects, just like mass media, social media also have their own logic that consists of “a set of technological, economic, and sociocultural mechanisms and strategies on which the dynamics of these platforms are based” (Van Dijck, Poell,2013). There is, therefore, a set of dynamics, principles, and practices through which platforms process news, information, and other communicative content; this logic can be exported outside of platforms, contrary to their specific technological, discur828

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sive, economic, and organizational strategies that are not made explicit or are attempted to be made to appear neutral. According to Van Dijck and Poell (2013), social media logic strategies are based on four elements: programmability, popularity, connectivity, and datafication. Programmability indicates the ability and possibility that users have to manage content and create information flows although conditioned by algorithms (instruction sets that operate based on data inferences). Programmability takes the place of what in media logic was scheduling: an editorial strategy by which content is organized into lineups and schedules in order to keep the audience glued to the screen. Popularity is a strategy that consists in giving visibility and greater relevance to certain topics, issues and characters and that, in different ways, has always been pursued by media logic. At the inception of social media, it was expected that popularity could be distributed in a more egalitarian manner based on users’ ability to manage content and their presence on the platforms. However, in this case, it is the algorithms of each platform that decree criteria of visibility and promote them; the substantial difference with respect to the popularity strategies used by media logic is the emergence of systems for evaluating visibility and popularity that lead users themselves, but also businesses and politics, to intervene in the mechanisms that determine it. Connectivity is a property of platforms, enabling a process of mutual shaping between users, platforms and advertisers. If we think of the role of emerging influencers, we can see how their initial innovative way of giving visibility to products and services has been absorbed by the promotional strategies of advertisers, to the point of conditioning them. At the same time, once formalized and standardized, these criteria ended up being adopted almost as a guideline by new influencers, giving rise to a sort of new advertising format. Datafication refers to the ability of networking platforms to produce data and, above all, to transform data into information about aspects of reality that were previously unreliable: Spotify’s introduction of a function that lets users know how many hours of music they have enjoyed, with reference to which genres and which groups, gives a measure of how music too has become dataable. The media logic has always based its strategies on a dataification produced through auditel data, audipress, or opinion polls, but the information produced by these data were limited to the activity of media consumption and fruition and could say little about different areas, but especially the activity of data collection was visible and explicit where today it is produced by the users themselves, often unconsciously, during the daily attendance of virtual environments (Van Dijck, 2005). The elements of dialogue between media logic and social media logic reinforce the idea that talking about old and new media as two separate and distinct realities is misleading and not representative of the mutual influences that make the media system an increasingly complex and interconnected system. The role and changes of the television industry testify to this complexity starting with the ubiquity of technologies incorporated into mobile digital devices (smartphones, tablets, etc.) and the important spaces that seriality has been able to carve out within platforms such as Youtube, Netflix and Amazon Chrome, capable of offering a huge amount of content to watch (Kaplan et al. 2010). Social media accounts for more than a third of our connected time, with people now spending an average of nearly 2.5 hours per day on social platforms. This means that the world now spends more than 10 billion hours per day using social media, which equates to more than 1 million years of human existence. The main social networks used daily for different uses and different purposes are: •

TikTok, which started as an amateur music video platform, but has since expanded its pool to include all types of short videos. The app allows users to watch music clips, create short clips up 829

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• • • • • •

to a maximum of 60 seconds, and most importantly, edit and add a large number of special effects as desired in a very simple way. Twitter is a news and microblogging service where users post and interact with very short messages of only 280 characters, called tweets. Mainly used by journalists, public figures and politicians, it has a great success abroad, less so in Italy. Facebook is the social network par excellence in the history of social networks, it marked a new era for communication. It is a free web service that allows you to get in touch with your friends near and far and open new relationships. Whatsapp: the application dedicated to instant messaging most famous and most used in the world. Instagram: is a social network for sharing photos and videos that has revolutionized the way we communicate. On instagram are available stories, short videos made by the user, lasting a few seconds, visible online are for 24 hours that allow you to show the “behind the scenes” of any event. Pinterest: social based on images and more generally on visual content. It is not a social to talk about oneself, but it is used to show one’s interests through the content shared. Images and videos are the masters: tutorials, video-recipes, info-graphics are just some of the contents shared. YouTube: web platform for publishing and sharing videos. It is the second most visited website in the world. During the quarantine, 295 million views were reached in the first 14 days of March and 404 million in the following two weeks.

Over the years, traditional social media have been joined by online socialization tools based on a level of “intimacy” that is increasingly exclusive, selective and with merely sentimental and sexual relational purposes. These tools are mainly represented by dating sites and apps for dating. The reasons that make dating apps particularly suitable for relational and sexual needs that are considered more intimate and private lie in the main characteristics of the tool, such as gratuitousness, immediacy and selectivity. The gratuitousness describes the feature of dating apps that allows all users, regardless of social class or socioeconomic status, to register and create a user profile, which over time can be customized, enriched with multimedia products (photos and videos) and express in an overt or covert way their psychological, physical, phenotypic and cultural characteristics. The criterion of immediacy, on the other hand, describes the characteristic of the speed and performativity of the communicative process, as the information exchange within the fo dating apps is extremely rapid and focused on the purpose of the relationship, greatly reducing the time of interaction and allowing, at the same time, a skimming on communications and interactive exchanges. The criterion of immediacy recalls, finally, the criterion of selectivity, as the app allows the user to make explicit the phenotypic, psychological, relational and sexual characteristics of the “hypothetical partners”, creating real frames of social reference within which people can manifest, express, experience and, over time, refine their social and sexual scripts and draw contacts in a functional and selective way from the relational and sexual market proposed by the network (Masullo, Coppola, 2021). The range of possible virtual contexts in which to track down potential friendships and intimate and private relationships provides different tools and different applications that represent the “online relational and sexual market” (Laumann, 2004). Dating apps tend to be open to all individuals regardless of their nationality, ethnicity, sexual identity and social class. However, there are some dating apps that are “polarized” based on sexual orientation and the primary function for which it was designed, so it is possible to find dating apps used predominantly 830

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by heterosexual people, homosexual and/or bisexual people, people with non-normative gender identities (e.g., transgender and non-binary people), and finally people with no sexual preference (asexuals). The LGBTQ community has, over time, proposed fo dating apps and dating platforms as possible virtual spaces where they can trace reference models to sexuality, create new emotional and sexual relationships, and freely experience their sexuality and non-normative identity (Bacio, Peruzzi, 2017; Masullo, Coppola, 2020, 2021). In particular, transgender and transsexual people orient their choice of app for dating according to specific parameters and tend to provide indications about the purpose of use, the possibility of choosing a specific target as a sexual partner, the possibility of using the app as a test of their gender and sexual identity. Specifically we can report 3 apps for dating used by transgender and transsexual people: 1. Badoo or Tinder: apps for dating mainly of a heterosexual nature; they provide a series of tools to create a consistent profile and indicate parameters of the partner sought, through the possibility of making explicit: sexual orientation, type of partner required, physical and psychological characteristics, hobbies and preferences. Both apps provide the possibility of creating a gold list and a black list of contacts, encouraging the process of selectivity; 2. Grindr and Wapa: apps for dating for homosexual and bisexual people, specifically Grindr for homosexual and/or bisexual men, Wapa for homosexual and/or bisexual women. They provide a series of functions to create profiles more or less calibrated according to the relational and sexual needs of users, like many apps for dating allows the invisibility of their image, geolocation and the ability to ban (block, hunt) unwanted users. 3. Transgeder app: represents one of the two major dating apps specifically for transgender, transsexual, non-binary and crossdresser people. Little used by some categories of transgender people, it tends to be used in European countries and in many regions of Northern Italy. It provides the possibility of combining dimensions such as gender identity and sexual orientation allowing users to clearly and specifically define their sexual coordinates, so as to facilitate the search for possible partners (Masullo, Coppola, 2021).

RESEARCH DESIGN AND METHODOLOGICAL ASPECTS The purpose of this research work was to investigate the psychological, relational and social aspects of transgender people who use social media and dating apps as communicative spaces and relational domains, in order to delineate the peculiar aspects of media consumption, the normative lines of access and the processes of stigmatization and social discrimination of web-mediated socialization processes. The following research questions were formulated: 1. Which social media were used the most and whether there was a greater/minor frequency during the health emergency period? 2. What are the main purposes of dating apps used by transgender people? 3. Can social media and apps represent new communicative and social spaces or conversely contexts in which to experience marginalization and discrimination?

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Aware of the methodological limitations, including finding the sample and the difficulty and resistance of transgender people in participating in research, due to inhibition and difficulty in arguing and talking about aspects related to their gender identity and sexual practices and experiences, it was agreed with reasoned sampling through the users of one of the main accredited centers for gender transition, specifically the reference center for the city of Rome. The approach is qualitative and has provided semi-structured interviews in depth, to about 30 transgender people (15 MtoF - 15FtoM), residing in different regions of Central and Southern Italy, aged between 23 and 45 years, during the period considered from March 4 to May 4, 2020. Precisely because of the restrictions, due to the pandemic containment measures, the interviews took place in online mode through different platforms (Whatsapp, Google Meet, Skype). The semi-structured interview is a data collection tool with a qualitative approach, characterized by a defined degree of structuring: a compromise between a structured interview and a free interview. The main characteristic of this procedure is represented by the possibility, on the part of the researcher, to define upstream the dimensions and the salient aspects to be investigated, leaving the interviewee free to proceed in the order and manner he or she prefers (De Carlo, 2002). The semi-structured interview used for the present research work sought to analyze the following dimensions: 1. socio-anagraphic information; 2. the construction of media capital, in terms of use, purpose, and frequency of social media 3. the use of apps for dating, in terms of use, frequency, purpose and experiences both within the online context and in the possible offline context; 4. discrimination and anti-social behaviors suffered in cyberspace and in the web society (misgendering, body shaming, hate speech); Each dimension identified in the semi-structured interview will be treated separately in the following sub-sections of analysis.

DATA ANALYSIS AND RESULTS From the analysis of the semi-structured interviews conducted, it is possible to highlight how the media capital of transgender people would seem to consist of three specific “communicative squares.” They would resort to thematic Facebook groups, specific Whatsapp groups, and sex dating apps for a more intimate and private level. Facebook would represent the macrosystemic level and reflect the Extended Society with its own level of normativity and value system of reference. Transgender people create a profile of their new identity on Facebook and move to dedicated Thematic groups to find information and to discuss the transition process or to socialize in a protected environment. Thematic groups aim to provide a space for structured discussion and in-depth analysis on the concept of sexual identity, non-normal identities and non-conforming sexuality; they also represent real “communicative squares” where to express experiences, share opinions and build friendly and sentimental relationships in a virtual environment where the “sharing of the transgender condition” represents a factor of protection and safety. As the interviews below highlight: 832

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“After I joined a group on facebook to get information about the path I felt understood and accepted” (Ludo, 22 years old, MToF, Rome). “Initially I was skeptical about using social media to have a confrontation, also due to the fact that I was not a very technological person. Then a friend told me about a transgender group on facebook and I decided to check it out. Many friendships were born, I didn’t think it could be possible.” (Marco, 34, FToM, Bologna). The level of “social intimacy” would present a stratification in progressive steps, in fact, after a period of “militancy” on the Thematic group of Facebook, the “stronger relationships” are transferred to the next level of intimacy which is represented by the access or not to the official Whastapp group of the Thematic Group. This aspect would therefore highlight criteria of social inclusion and exclusion even within the Facebook groups of the specific subculture themselves, as the admission and exclusion criteria would reflect normative parameters that would refer to aspects related to aesthetics and passing for nomals for MtoFs and criteria of hegemonic masculinity for FtoMs (Rinalidi, 2016; Connell, 2011). “I wasn’t accepted into the transgender Facebook Whatsapp group because I’m not as muscular as they would like.” (Mattia, 25, FToM, Rome). “I didn’t agree with many of their positions on important issues like activism, such as self-determination processes in the transition path. They excluded me from the Whatsapp group, only some can enter, I was not eligible (Ivan, 21 years old, Rome). “They excluded me from the group because I was too unfeminine for their taste.” (Katia, 22, MTF, Rome) “I was banned because I openly said I didn’t want to modify too much of my body, some of the trans women in the Whatsapp group had a straw tail and got me kicked out!” (Monica, 24, Rome) Thematic Groups and Whastapp groups would represent more restricted microsystems with an increasingly direct and immediate level of social intimacy. In fact, in these “online social squares”, it is possible to create more or less stable relationships and interactions, with the possibility of cultivating common interests, sharing experiences and creating, at times, social solidarity among group members. However, transgender people would experience the greatest degree of intimacy with apps for dating, such as Badoo, Tinder, Meetic, Grindr, Wapa and Transgender apps. The wide range of apps for dating proposed by the social media landscape provides the possibility of being able to choose a “specific communicative square” based on the characteristics of the erotic and sexual object. The main purpose of the use of apps for dating would seem to be the construction of a real online sexual market where it is possible to start a complex and articulated process of affective-sexual selection through which the transgender person on the one hand could experience the neo-identity, on the other hand satisfy both affective and sexual needs through the transfer of the encounter from the virtual reality to the offline world.

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“Personally I use Tinder because at this stage I am interested in sex, in this App I go for sure! I find sexually interesting people and then I am still the one who chooses and selects!” (Julia, 33, MtoF, Rome) “If I want to find a woman who is sexually interested in an FtoM I choose to use explicit apps because it cuts down on presentation time and you don’t have to go around it, it’s faster” (Jacopo, 33, FtoM, Rome) On the other hand, with regard to the use of social media and “online communicative squares”, transgender people highlight a double communicative function: on the one hand the need to find information about the transgender condition, the path of gender transition and life experience, on the other hand cyberspace would represent a context in which they can express themselves and recognize themselves from an identity, affective and relational point of view. The informative function of cyberspace would be very important for transgender people in the early stages of approaching the LGBT community. In fact, it is possible to understand how, in order to start the process of socialization to sexuality and eventually approach the path of gender transition, enrollment in thematic Facebook groups represents the “first step” for identity construction. The Facebook group, which, as mentioned, would represent the most open and social “online communicative square”, would be the first social environment in which to experience the new identity and at the same time track down all the information necessary for the process of construction and consolidation, as the parts of the interviews below highlight. “I have met so many people on different transgender facebook groups. I’m happy because I feel understood.” (Elena, 33, MToF, Potenza) “Thanks to transgender facebook groups, I was able to ask for some opinions about hormones and operations from people who had already transitioned” (Lorenzo, 25, FToM, Rome) Analysis of the interviews also shows the identity and relational function of the use of social media. In fact, after an initial phase in the more social and public online communicative square, transgender people would resort to more private and selective areas in order to experiment on the one hand their gender expressiveness on the other hand the erotic and sexual potential with potential partners, as the following interviews show. “I met a guy on the transgender Facebook group, shortly after we decided to talk on Whatsapp. As soon as we saw each other, chemistry and a strong attraction clicked immediately.” (Lucrezia, 26, MToF, Rome) “A few months ago I was listed on a Whatsapp group for trans people, every day continuous messages came on the group chat, until one day I notice a trans girl among the contacts. I look at her Whatsapp profile picture and didn’t hesitate to write to her. Now we are dating.” (Luca, 31, FToM, Rome) In summary, the data on the use of social media and online communication spaces of transsexual and transgender people, through the interviews, are oriented on the one hand on an informative function that would concern a broader and more public online communication space, such as the Thematic Groups; on the other hand there is an important function of identity and socialization that would concern increasingly private and more intimate communication spaces, such as apps for dating. 834

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The interviews carried out would also highlight an important discriminatory expression of cyberspace for transgender people. This discriminatory expression would seem to have a double matrix: on the one hand, the online communication spaces would present specific characteristics of access, represented by psychological, social, cultural and anthropometric parameters that would reflect the criteria of “normativity”; on the other hand, the intrinsic characteristics (such as anonymity, immediacy and virtuality) of the web would encourage online shaming processes2 especially for social categories and subcultures stigmatized as transgender people. These, therefore, would incur in discriminatory processes that would present different levels of contextual proximity. In fact, there would be discrimination experienced at a macrosystemic level, in the cis-heteronormative society, which establishes as normativity the condition of alignment between physical identity and psychic identity and with heterosexual sexual orientation, as evidenced by the following interviews: “I was targeted on Facebook, under a post of mine, for being transgender. I was constantly insulted until I decided to take myself off social. It had become a nightmare!” (Lola, 28, MToF, Rome). “I had for 8 years a Youtube channel that had reached a million views, then when I came back here in Lecce, I had to black out the videos because a colleague of mine from my same cooperative found these videos, I premise that he lives in a small town where they have a very closed mentality, he started asking me questions like “What are these videos? What operations have you done?” and I answered him “I now obscure them, if you talk I will take you to court”. (Jacopo, FtoM, 33 years old, Lecce). A second level of discrimination is experienced by transgender people at the microsystemic level, that is, within the LGBT community itself. This discriminatory process would be explained by the presence within the LGBTQ community of the homonormative principle3, which would define the characteristics to be part of the category considered elite: white cis-gender and homosexual male and at the same time would consider marginal and marginalized the characteristics of other non normative identities (lesbian women, transgender people, non binary), as reported below: “I was targeted on Facebook by some lesbian girls, they were telling me that I was not a woman but a man pretending to be a woman...how much ignorance and meanness” (Alessandra, 28, MToF, Rome). “I had become very good at video games so I decided to open a channel on Youtube. Under the videos only negative comments, especially about my physical appearance, from some gay guys, but I didn’t give a damn and kept doing what I liked to do.” (Luca, 22, FToM, Pisa). In addition, transgender people would also experience intra-group discrimination within the transgender community itself, as even among people in the same social category, over time, a “normative fit” would be created that would track passability and gender expressivity as the gateway skills to context, as reported below:

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“I have faced several discriminations from a transgender person, like myself. I didn’t think something like this could ever happen since we live the same condition. This is because according to her point of view I was not aesthetically conforming to being a woman. Speechless” (Faith, 33 years old, MToF, Padua) “I joined a Facebook group to get information about transitioning. Then under every post I made, punctually, there was this person who was part of the community, he started to denigrate me because he didn’t like my physical appearance because I was a little fleshy at the time and he said to me verbatim “You are obese, instead of joining this group you should go to a doctor who will forbid you to eat everything in front of you. You are obscene!” (Alessio, 29 years old, FToM, Milan) Finally, in line with the theory of sexual markets (Gagnon, Laumann, 2004) transgender people in intimate and dyadic interactions online, through apps for dating, would experience forms of social discrimination in relation to normative characteristics and sexual coordinates explicitly established by the app. Indeed, if the app for dating is predominantly heterosexual, it is possible to trace cis-heteronormative criteria, conversely if the app is predominantly homosexual (e.g. Grindr or Wapa) then the criteria would reflect homonormativity, as reported in the following interviews: “The danger in using apps like Badoo or Tinder is that not everyone can accept your transgender status, I tend to prefer not to say it right away for a matter of modesty and because I don’t like to talk about it with strangers, but the risk is there and often you have to run it otherwise you can’t get out of the niche (Marco, 22, Rome). “Honestly, as a transgender homosexual boy, I already made my condition explicit in the presentation of my profile. You don’t always find gay guys willing to accept a different sexual relationship, so I prefer to make it explicit so that gay people specifically interested can contact me “(Alex, 28 years old, Civitanova). Table No. 1 describes, through its own elaboration, the levels of social interaction in relation to the levels of discrimination experienced by transgender people in online contexts, describing for each online context the normative criterion.

CONCLUDING REFLECTIONS AND FUTURE PROSPECTS The digital society has provided in recent years several new opportunities for socialization and relational processes. These opportunities, on the one hand, allow relationships in which dimensions such as space and time are strongly restructured, but on the other hand, they create new paths to discrimination and social exclusion. The Web society has represented, for many subcultures, an important social environment in which to experience new relational possibilities and new defining processes. For the LGBTQ community, in particular, the opportunities of the web and online socialization have initiated important and decisive processes of identity self-determination and social and cultural emancipation.

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Table 1. Own elaboration, the levels of social interaction in relation to the levels of discrimination experienced by transgender people in online contexts, describing for each online context the normative criterion Level of Social Interaction

Virtual Square

Normativity Criterion

Macrosystemic

Facebook

Cis-heteronormativity

Microsystemic

Thematic Group LGBTQ

Homonormativity

Microsystemic/Reference Group

Thematic Group Transgender Gruppo Whastapp

Passability Heteronormative Gender Models

Diadic Interactions

App for dating

Sexual characteristics and kind of the app

The present research work analyzed the media capital, the use of social media and apps for dating of a specific social category of the LGBTQ community, transgender people, also trying to define the processes of discrimination and social inclusion experienced through the web-mediated experience. Through telephone interviews with a sample of 30 transgender people, 15 MtoF and 15 FtoM, a picture was outlined (although the results are exploratory and not generalizable) in which the media consumption of transgender people would cover three main axes that would reflect as many levels of social intimacy: a more macrosocial level represented by Facebook, where the social dynamics and normative lines of the Extended Society are reproduced; a more microsystemic level, constituted by the Thematic Groups on Facebook, the official Whastapp groups, where more direct, explicit, functional relationships are experienced and consolidated by dating, albeit virtual; and an intimate and private level represented by fo dating apps, where relational and sexual needs can be traced. The use of social media and apps would also reflect a different criterion of social exposure that runs along a continuum that goes from a public pole, where social media and apps are used to find information or to track down good practices, to an intimate and private pole where the purposes are relational and sexual approach and where one can express one’s new identity in the best and most authentic way. Discrimination suffered on social media by transgender people would follow a concentric layering approach: while at a more external level, in Society, transgender people are discriminated against on the basis of a social order set on parameters such as cis-heteronormativity (society is divided according to a gender binarism and a heterosexual sexual orientation), within the LGBTQ community transgender people would be discriminated against as individuals in whom the cis-sexual order (gender binarism) is lacking and therefore not aligned with the rest of the people in the rainbow community itself. It is also possible to trace discriminatory processes within the transgender community itself to the detriment of people who do not meet particular criteria, which we might define as skills, such as “passability” and aesthetic appeal, gender binarism and social repositioning within society. Finally, at the level of dyadic and intimate interaction experienced through dating apps, it is possible to suffer discrimination based on criteria considered normative within the online sexual context, cisheteronormative within heterosexual dating apps and homonormative within homosexual dating apps. Future research could be oriented on the enlargement of the sample of reference, trying to include other non normative sexual identities (such as non-binary, asexual, pansexual and agender people) not very visible within the LGBTQ community and lacking a clear and defined narrative and expression; moreover we will try to go deeper into the processes of discrimination suffered within the web society trying to highlight any coping strategies or protective factors.

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REFERENCES Bacio, M., & Peruzzi, M. (2017). Alla ricerca della felicità: Gay su Grindr, tra sesso e solitudine. In C. Rinaldi (Ed.), Copioni sessuali: storia, analisi e applicazioni (pp. 289–306). Mondadori. Bennato, D. (2009). Le metafore del computer. La costruzione sociale. il Mulino. Berger, P. L., & Luckmann, T. (1969). La realtà come costruzione sociale. il Mulino. Brofenbrenner, U. (1992). The ecology of human develompment. Cambridge Press. Castells, M. (2001). La nascita della società in rete. Università Bocconi Editore. Connell, R. W. (2011). Questioni di genere. il Mulino. D’Agostino, A. (2013). Sesso mutante. I transgender si raccontano. Mimesis Edizioni. Dijk, J. V. (2005). The Network Society. Sage Pubns Ltd. Dijk, J. V., & Poell, T. (2013). Understanding Social Media Logic. Media and Communication, 1(1), 2–14. doi:10.17645/mac.v1i1.70 Gagnon, J., & Simon, W. (2005). Sexual conduct. The social sources of human sexuality. Aldine-transaction. Graham, M. (2019). Society and the Internet: How Networks of Information and Communication are Changing Our Lives. OUP. doi:10.1093/oso/9780198843498.001.0001 Kaplan, A. M., & Haenlein, M. (2010). Users the world, unite! The challenges and opportunities of Social Media. ScienceDirect, 53(1), 59–68. doi:10.1016/j.bushor.2009.09.003 Laumann, E., & Gagnon, J. H. (2004). A Sociological Prespective on Sexual Action. In Conceiving Sexuality. Approaches to Sex Research in a Postmodern World. Routledge. Leccardi, C. (2002). Tra i generi. Rileggendo le differenze di genere, di generazione, di orientamento sessuale. Gerini e Associati. Lievrouw, L. (2004). What’s Changed about New Media? Introduction to the Fifth Anniversary Issue of New Media & Society. New Media & Society, 6(1), 9–15. doi:10.1177/1461444804039898 Masullo, G., & Coppola, M. (2021). Scripts and Sexual Markets of Transgender people on online dating Apps: A netnographic study. Italian Sociological Review, 11(4s), 319–341. Rinaldi, C. (2016). Sesso, sé e società: per una sociologia della sessualità. Mondatori Università. Ruspini, E. (2017). Le identità di Genere. Carocci. Ruspini, E., & Inghilleri, M. (2009). Transessualità e scienze sociali. Identità di genere nella postmodernità. Liguori. Saraceno, C., Inghilleri, M., Ruspini, E., & Trappolin, L. (2008). Omosapiens III. Per una sociologia dell’omosessualità. Carocci.

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Silvestrone, R. (1999). What’s New About New Media? New Media & Society, 1(1), 10–82. doi:10.1177/1461444899001001002 Simonelli, C. (2002). Psicologia dello sviluppo sessuale e affettivo. Carocci. Simonelli, C., Rossi, R., Tripodi, F., De Stasio, S., & Petruccelli, I. (2007). Maschi e femmina si nasce: uomini e donna si diventa. Franco Angeli. Valerio P., Vitelli R., Romeo R., & Fazzari P., (2013). Figure dell’identità di genere. Uno sguardo tra psicologia, clinica e discorso sociale. Franco Angeli.

KEY TERMS AND DEFINITIONS Exosystem: Situations in which the individual does not participate directly, but in which events and situations occur that indirectly influence and shape socialization processes. Mesosystem: Context where the effects on the individual of social interactions and socialization processes can often be traced. Microsystem: The background and the containment system within which the other contexts necessarily refer to the normative subsystems and is necessarily conditioned and co-constructed with the cultural system of reference. Sexual Market: Social contexts where sexual interactions are experienced as sexual arenas where sexual behaviors, practices, and expectations between partners are dramatized according to culturally recognized, accepted, and regulated norms and systems. Transgender: The transgender condition includes those who were born male but feel like women (MtoF), those who were born female but identify with masculinity (FtoM), those who do not assume a precise gender identity and remain suspended in a non-definition of gender (genderqueer), those who reject binary gender assignment (no binary), and finally those who reject a gender identity and do not find themselves in any label (agender). Transsexual: The term transsexual describes the condition in which a subject with gender mismatch has already begun a process of transformation of their physical and sexual characteristics in order to assume the new gender identity from a somato-biological point of view, as well as legal and social.

ENDNOTES 1



2



3



According to the authors, sexual scripts represent maps of meanings and behaviors established by reference groups and adapted by each individual with the aim of contextualizing (and in many cases normalizing) his or her own behaviors, experiences, and performance. Online shaming: a form of public shaming in which Internet users are harassed, mocked, or bullied by other Internet users. Homonormativity: a policy that tends to normalize the lesbian and gay subject, making them acceptable, worthy of acceptance, and pushes a little further the boundaries into which to relegate the unacceptable and abject.

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Chapter 46

Social Research Methods in Cybersecurity: From Criminology to Industrial Cybersecurity Felix Antonio Barrio https://orcid.org/0000-0002-1660-0479 University Isabel I de Castilla, Spain Raquel Poy University of Leon, Spain

ABSTRACT The application of social research methods in cybersecurity requires a multidisciplinary combination since the security of technologies and communication networks is made up of a set of uses, techniques, and results directly conditioned by the parameters of confidentiality, data availability, integrity, and privacy. However, each of these technological concepts is prepared and subject to conditions of use that involve ethical, sociological, economic, and legal aspects. Firstly, social engineering techniques in cybercrime tend to combine social investigation techniques with computational engineering and telecommunications elements. Secondly, research on cybersecurity phenomena in industrial environments implies the adaptation to the organizational specificity of each sector. In this chapter, the social research topics commonly addressed by leading companies and researchers in cybersecurity at a global level are analyzed from a comparative point of view, extracting a taxonomy of social research on cybersecurity.

INTRODUCTION Universal access to Information and Communication Technologies (ICT) and, significantly, global access to the Internet have increased our dependence on the normal functioning of accesses, data manipulation, and transmission of data. It is unnecessary to point out how this dependency has reached critical values for people or organizations. Consequently, we must be aware that security has become a substantial eleDOI: 10.4018/978-1-7998-8473-6.ch046

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 Social Research Methods in Cybersecurity

ment of the digital society and economy. As Ulrich Beck anticipated in the 1980s, the growing social concern about the risks humans had created made the risk of new technologies one of the main interests in the social sphere (Beck, 1992). The ‘risk society’ predicted by Beck was becoming a reality in the ‘digital society’ (Lupton, 2016). Beck, Castel, or Luhmann led the sociological analysis of the end of the past century on uncertainty and fear of risk (Castel, 1991; Luhmann et al., 2017). These authors stand out in a broad theoretical movement that puts the concept of risk at the center of sociological theory (Adam et al., 2000). This debate highlights the relationships between concepts such as risk, technology, social communication, or uncertainty management. As a whole, this debate allows us to verify that our post-industrial society has had an accelerated dependence on technology in recent decades, notably represented by cybersecurity. COVID-19 pandemic has boosted the consumption of digital services, including remote working or education and electronic leisure, pushing consumption patterns that will consolidate to a great extent among users even after the recovery of normality (Ting et al., 2020; Papadopoulos et al., 2020). But the displacement of traditional consumption and economic activity to the digital world also drives the motivations of attraction to cybercriminals, whose guidelines for action have become more sophisticated (Lallie et al., 2021). Cybersecurity is an area of technological risk management that combines purely technical aspects with behavioral issues about how people use information and communication technologies regarding confidentiality, integrity, and data availability. Otherwise, the fact that 95% of the technological risks related to suffering a cyberattack by cybercriminals ’are human-enabled’ (Nobles, 2018), implies that the relevance of social research has had exponential growth in the last decade. Given this perspective, the importance acquired by the social study of cyber risks is understood, which has only recently received the necessary academic recognition. The existence of a disciplinary field such as cybersecurity barely acquired a birth certificate a decade ago. In 2010 the MITRE corporation commissioned the JASON Advisory Group to write a report on a possible scientific disciplinary area named cybersecurity. The group of experts linked the successful development of the new discipline to the joint effort of an academic, industrial, and laboratory network that should feed with knowledge an authentic research body (JASON, 2010: 6-7). Although initially, they conferred a secondary role to the social sciences, they recognized that their observational methods should establish synergies with those based on the technological field. But social research had already begun its journey within the framework of studies promoted by specialized companies and government agencies with interests in this field, establishing two different lines of work. In the last decade, large consulting firms such as Gartner or Forrester and multinationals such as Microsoft, IBM, Cisco, Deloitte, or Accenture, which have developed business divisions specialized in the research, have monopolized knowledge production on cybersecurity. This fact is part of a corporative strategy to maintain the necessary competitiveness within the framework of the technology industry. (Walton et al., 2021). Often in collaboration with academia, these research centers have proven essential to contribute to the generation of research on risks and threats and their impact on society and the economy at a global and regional level. However, they are not exempt, as we will see, from the controversy over their possible biases in impartiality (Maschmeyer et al., 2021). On the other hand, the role in social research of government agencies as the reference centers for cybersecurity in developed countries has proven fundamental to promote cybersecurity research about public issues. Although enabling different regulatory and strategic frameworks, American, European, and Australian institutions have strategic research agendas to consider (Wang et al., 2016). 841

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Regarding the debate on if cybersecurity is an actual disciplinary scientific area, the controversy is still alive. On the one hand, there is no consensus on the definition of the concept of cybersecurity itself, which is very recent and, in a decade, has replaced disciplinary fields traditionally restricted to computer science and telecommunications engineering domains, such as Security of the Information and Networks (Von Solms & Van Niekerk, 2013). On the other hand, the definition itself has undergone constant changes in its object and content scope. Even more, the governments of different countries differ in accepting one or the other content for fear of its repercussion on international relations (Giles & Hagestad, 2013). What is clear is that this scope is growing, and the domain of cybersecurity beyond Information Security and Communication Networks has broadened its spectrum to issues related to the security of States, the economy, and society in general (Dunn Cavelty & Egloff, 2019). The third element of controversy is the classic moral debate behind the supporters of an approach focused on aspects of computation and engineering compared to others. Bossong & Wagner (2017) consider that computer experts have more analytical advantages because “they prefer more technical and precise concepts, such as information security, which is made up of definable attributes of integrity, availability, and confidentiality.” At the same time, other academic approaches introduce topics in their studies more focused on cybercrime, national security, or cyberwar dangers and how cybersecurity is justified. In this way, ethical and political questions regarding the claim of restricting uses and freedoms, limiting privacy, or forcing the penalization of behaviors, while the ICT experts in their neutrality start from a conception of cyberspace as egalitarian. According to Edgar & Manz (2018: 53-54), cybersecurity is not a science to understand cyberspace, but rather a science to understand how cyberspace is built in such a way that it acquires desirable characteristics or rules, in terms of the combination of hardware, data, and human beings. These engineers from the prestigious Pacific Northwest National Laboratory believe that the most fruitful field of scientific research in cybersecurity lies in studying the intersection between IT systems and data. Most of the studies on cybersecurity have emphasized in the last three decades the instrumental nature of technology as a resource to commit crimes, initiate cyberwar or cyber espionage actions, and as an instrument to exercise power and coercion, so they are not strictly exempt from neutrality (McCarthy, 2018; Leese & Hoijtink, 2019). The fact is that the design carried out by engineers since the 1960s assigned an especially relevant role from the point of view of security interests for ICT that explains its original links with the military industry (Naughton, 2016). The very concept of ARPANET as a project and its release as the Internet rested in principle resilience of communication nodes to military attacks that compromised the communication epicenters, and software engineering standards had their development, especially in software programming military aeronautics with the support of the United States Department of Defense, at the origin of the Software Engineering Institute at Carnegie Mellon University founded by professor and military Angel Jordan (Jordan, 2008). Bourbeau et al. (2017) identify as the primary academic fracture or debate in the field of cybersecurity studies the opposition between what they call problem-solving theories and critical theories, that is, those that do not question the social or power relationships that articulate human behavior around new technologies and those that consider the intentional character behind them (Aradau et al., 2014). from the pragmatic perspective of engineering experts. Only later are the issues treated in think tanks and only later become part of academic analysis and debate as topics. This transition would, from our point of view, have a double impact. First, the study of social researchers is conditioned by the technocentric perspective of computational analysts and engineers who mark many of the elements related to 842

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the modes and limitations of use and behavior of technology and its users. Secondly, when the social researcher has already assumed an adequate understanding of the phenomenon or incident related to cybersecurity, he has to include in the scope of his study the influence that the reactions of government and economic agents involved. This chapter will consider these debates and propose a taxonomy on the social studies of cybersecurity, based on the scientific literature and the classifications referred to in our area. Likewise, we will analyze the particular case of the social investigation of one of the most significant recent phenomena for global cybersecurity, such as the WannaCry case.

BACKGROUND As stated in the Global Risks Report published annually by the World Economic Forum, it is noteworthy that the effect of cybersecurity risks, both in terms of their prevalence and of their disruptive potential, is a growing global concern (Evans et al., 2017). Attacks against companies and organizations of all kinds have practically doubled in the last five years globally (Insurance Information Institute, 2021; ICCC, 2021; World Economic Forum, 2021), taking advantage of human factors and failures, so incidents that were previously considered something out of the ordinary are becoming more frequent. Cybercriminals adopt increasingly sophisticated schemes and technologies. Consequently, the traditional threat of malicious software and the well-known computer viruses in their different forms have acquired sophisticated forms that attackers use according to strategies. They try to multiply the effectiveness of their attacks through social engineering techniques and the deployment of zombie computer networks called botnets or with advanced persistent threats (APT) that have been growing exponentially in recent months. Thus, in the last five years, we have witnessed new attacks that have had enormous media repercussions, characterized by their viral dimension and because they represent new threats and combinations of attack vectors (Bada & Nurse, 2020). The relevance of the attacks known as WannaCry, Petya, and NotPetya, a combined series of the same malicious software that affected numerous companies and organizations worldwide between May and June 2017, represents a compelling case that illustrates the approach taken since social research. It is the first major global attack that affects organizations of all kinds and demonstrates the ability to paralyze society and the economy, particularly hospitals, banks, freight transport, and other sectors that had not been affected by this type of attack until then. The social impact and the number of people involved in organizations worldwide highlighted the enormous economic and social implications of cybersecurity.

The Two Approaches to Cybersecurity Research: From Engineering to Social Research Saltzer and Schroeder (1975) established in the mid-1970s the so-called design principles of secure systems, including aspects such as simplicity in design, reduced accessibility by default, activity verification controls by part of the agents, the open layout, and the limitation of access privileges on the part of the different users of the technology or digital services. They summarized the prevailing current in the previous two decades focused on solving engineering problems. The design and manufacture of hardware and software had to allow innovation to develop computer and telecommunications systems.

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Although the principles of security design in the new technologies of these authors have remained in general terms acceptable in the field of engineering, the subsequent evolution of connectivity and the massification of devices in the market has included other principles, such as those of technological accessibility and usability, especially promoted by the founders of the World Wide Web, as well as the focus of security by design, which seeks to universalize technology (Cerf, 2012). Security in design refers to the importance of exempting the user from expert knowledge. The production of devices and services has security conditions suitable for any user profile, including a profile with zero knowledge. The success in extending new technologies to all kinds of people, and the massive incorporation of minors and older people into the enormous consumption of technology and digital services since the late 1990s, would be the basis for the most remarkable transformation of our civilization since the Industrial Revolution (Pandita, 2017). But at the same time, they have led to the very prominent emergence of issues related to the impact of cybersecurity on global society and the economy. According to research focused on solving engineering problems, the fundamental concepts of cybersecurity research include those related to the secure technical configuration of devices, systems, and networks and the behaviors embodied in actions, methods, and processes that articulate defenders and attackers of the security of those. The combination of these elements makes it possible to define the concepts of technological risk and threats. However, this research is specifically oriented to analyze how technologies can create more cybersecurity, solving problems independent of political, ethical, or moral issues, in a technological reductionism and according to the positivist tradition of engineering (Dunn Cavelty, 2018). However, to contextualize the risk situation associated with cybersecurity technological problems, threats are primarily determined by the use and habits that users develop and logically evolve from adopting and innovating these new technologies. In this way, security problems for people derived from social media have an essentially different nature from other pre-existing ones, such as cyberbullying, which presents essential differences concerning traditional school bullying (Olweus & Limber, 2018). Fake news is another extreme example of how a phenomenon of digital disinformation can become the subject of solid debates about its classification as a threat or crime (Reilly, 2018; Marda & Milan, 2018; Monsees, 2020). From the 1980s, information and network security began its way towards becoming a subfield of research (Edgar and Manz, 2017: 33) when network computing leads the Internet to be a consumer-grade resource. Even the definition of cybersecurity did not become popular until the early 2010s, displacing the dominant concepts, such as discipline, of Information Security or Security of Communication Networks. It was born with notable popularity, as happened with the first mention of the concept of ‘cyberspace’ from science fiction literature (New Video Group, 2003). This union of cyber and security tries to emphasize the idea of cyberspace to bring together cybersecurity elements analyzed from the perspective of the security of information systems, new technologies, and communication networks. Cyberspace as a concept tried to combine data systems, digitized information, and their encoding. In the same way, the idea of ‘cybersecurity’ tries to intuitively overcome the restricted approach of Security Technologies to include the interaction of humans. Social and individual agents interact in a space characterized by physical and virtual scenarios where storage and transmission systems can acquire a physical component. This perspective of cyberspace is aligned with an analytical perspective of technology as a cultural artifact both in anthropology and sociology (Woolgar, 1996). As Deborah Frinckle points out in her Foreword to Edgar and Manz’s research handbook (2017: xvii), the challenges that cybersecurity research presents are too many for a field of security sciences that com844

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bines enough complexity to carry out reasonably diverse interdisciplinary approaches, from mathematics or software engineering, past telecommunications to sociology and social research in general. A digital field such as cybersecurity has followed a parallel development to the science dealing with its study. At the same time that a new discipline was born, the first debates about its scope began by broadening the object of political and social study. In this way, Eriksson and Giacomello analyzed the challenges for the discipline of international relations when trying to include cybersecurity issues in their analysis frameworks, pointing out the great difficulty derived from the technical complexity intrinsic to digital conditions (2006: 236). Cybersecurity Research required a greater or lesser degree of synergy with the observational analysis methods typical of physics, mathematics, and data engineering applications. Social, political, or economic research became an essential topic for understanding the phenomenon. The intrinsic cybersecurity issues that were of most significant concern between the 1960s and 1980s focused on reducing design failures, the risks of intentional tampering, human failures in the operation of computer systems, or the confidentiality of systems. But in the 1990s, the growing irruption of terrorism, cyber wars, and cyber espionage or activism on the networks brought cybercrime to the main object of concern. According to the annual reports of the European Cybersecurity Agency (ENISA), at present, the leading global threats to cybersecurity correspond to those perpetrated with criminal purposes. These include attacks using ransomware, malicious software that hijacks systems and devices to extort victims subsequently, and which in 2018 accounted for 56% of all malicious software victims (Kettani & Wainwright, 2019). Regarding the second type of computer virus infections, 32% corresponded in 2020 to cryptomining: virtual coins mining with a part of the computing capacities and data of the infected computers and devices. The first ransomware virus appeared in 2012, and five years later, there were more than 200 families of viruses of this type. However, ransomware has been an unprecedented global phenomenon due to its revolutionary characteristics, being more popular since 2017, under the name WannaCry. In 2018, cybercrime revenues from extortion of ransomware victims would have skyrocketed, and according to a report by the company Symantec (2019), a single criminal gang known as ‘SamSam’ would have obtained more than six million euros in 2018 through this technique. The innovation of new ransomware families has multiplied in recent years, mainly due to the global confinement established during the crisis of the COVID pandemic (Pranggono & Arabo, 2021). This kind of attack shows many difficulties to contain since 77% of the victims were, despite having virus protection and updated patches on their computers or mobile devices (Sophos, 2018). Especially devastating has proven to be this type of “ransomware” attack in traditional industries such as the manufacturing sector, transportation, and others where the recent incorporation to the Internet and its multiple technologies, interconnected and equipped with ICT components, have grown exponentially under the form of programmable automation, robotics or artificial intelligence in all production lines and the supply chain. Cybercriminals have also found in these industrial sectors an attack vector to carry out their attempts at extortion or sabotage remotely, and they have also done so with organizations that did not have sufficient digital protections such as health centers, schools, and in general small and mediumsized companies (Zimba & Chishimba, 2019).

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The WannaCry Analysis Case as a Social Research Model Among the most notable examples of the new trends in viral and global attacks in the industrial field, we have, among the new forms of attack type “ransomware,” the so-called WannaCry, which in May 2017 affected more than 300,000 computers out of 150 countries. Their variants, baptized as Petya and NotPetya, caused quarterly losses of 300 million dollars to the affected companies just one month after the previous one (Castillo, & Falzon, 2018). The research literature review gives us two series of studies that incorporate social research to a lesser or greater extent. The first group of studies focused on the technical operation of malicious software and the characteristics of its infection, reproduction, and spread in the networks of the affected organizations. Since its detection on May 12nd, at least three variants of the WannaCry virus have been identified, indicating that different computer viruses from the same family were involved in this cyberattack: 1. The first of them, WannaCrypt.A, was carried out as a first step before encrypting the documents on the computer and attempting to connect to an internally encoded web page. If the connection was successful, it did not encrypt any documents. If, on the other hand, it could not connect to the website, it would begin the encryption of the documents and request the payment of the ransom for the encrypted documents. 2. The second variant, WannaCrypt.B, immediately began with the encryption of the files and later requested the payment of the ransom of the encrypted documents. 3. The third variant attempted to connect to an internally encoded web page as a first step before encrypting the documents on the computer. The virus was of the combined type, acting primarily as a ‘worm.’ It entered organizations through a phishing or spoofing attack, which required user interaction with an attachment received by email. The attached file had to be downloaded and be run by the user, who thought it was a legitimate file from a trusted source. The worm proceeded to encrypt 166 types of files on the user’s computer and published a message demanding payment in bitcoins in exchange for the decryption code. The virus acted in a second moment as a ‘Trojan’ trying to spread from an ‘exploit’ that ran on the Server Message Block (SMB) protocol, a protocol of Microsoft Windows systems that allows the connection of computers with devices such as printers and share files with them and with other computers. The exploit took advantage of the fact that many computers with Microsoft systems had not installed the security patches published on March 14, 2017, by the Microsoft Company to avoid that vulnerability baptized as CVE-2017-01447. The virus then acts as a ‘ransomware,’ proceeding to encrypt files on infected computers on the network, like other types of ransomwares common at that time and known as CriptXXX, Crysis, BlackShades, Jigsaw, Apocalypse, FLocker, RAA, GOOPIC, Kozy.Jozy, MIRCOP, Locky, TeslaCrypt, MSIL or Samas (SAMSAM), Xorist, CryptorBit, Criptowall, or CTB-Locker. The worm element triggered the alarms: the virus replicated among network users even after disconnection to the Internet. For this reason, those responsible for cybersecurity opted, as the first mitigation measures, to order employees to completely suppress activities and abandon their jobs from the same Friday to Monday to identify how to proceed against the propagation. If this innovation caused stupor, another issue was associated with the purpose of the attack and its motivations. This topic became the object of analysis in the second group of investigations. The Eter846

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nalBlue exploit, which allowed the use of the SMB protocol vulnerability mentioned above, had been leaked by a group of hackers called “Shadow Brokers” (Hsiao & Kao, 2018; Koujalagi & Akkimaradi, 2018). There are different theses about who discovered the exploit; North Korean groups, the National Security Agency of the US Government, are accused of having released the exploit and even a young man who initially became world-famous for having claimed to stop the attack (Trautman & Ormerod, 2018; Christensen, & Liebetrau, 2019). Studies on the criminal behavior of attackers are relevant from the point of view of problem-solving in that they allow anticipating techniques and methods and introduce fascinating investigative elements, such as the political motivations that could exist behind the attack (Geers et al., 2014; Albahar, 2019; Egloff, 2020). The former government employee of the United States Edward Snowden pointed to the interest in the leak of the exploit and how its origin would be in the North American agency NSA (Verble, 2014). In contrast, other studies suggest some doubts produced by the fact that it was not withdrawn the bitcoins of extortion by the initial cyber attackers for three months (Turner, 2019; Fordoński & Kasprzak, 2019). In addition, even if the victims paid the ransom in the first weeks, the decryption key was not obtained, as is the case in most kidnappings, which reduced the securing of benefits. Finally, it is estimated that only about 140,000 dollars were paid when the figures indicate that approximately 40% of private victims and up to 70% of corporate victims tend to pay for blackmail (IBM X-Force Research, 2016: 13; Hernandez-Castro et al., 2020). These facts have led analysts to suppose that, in reality, those who organized the attack might only be interested in seeing its effects, which is called a proof of concept. Likewise, the immediate inactivity of the attackers could mean that they were surprised by the impact of the attack. Another exciting issue present in the studies is related to the reaction of organizations and companies to an attack that demonstrated their protection systems useless and that surprised those responsible for security, which in some cases decreed the abandonment of thousands of employees of their jobs (Rey et al., 2017). Analysts are especially interested in the behavior of the manufacturer Microsoft in the face of the attack episode and its role in solving the problem. Although Microsoft had already reported the SMB vulnerability in its March 2017 bulletin (MS17-010), on April 14, the exploit mentioned above was disseminated, allowing the attackers to act. However, until the explosion of the attack in May, Microsoft did not react by developing the security patch needed by users of operating systems that were no longer supported by the company, pressured by the worldwide clamor (Christensen & Liebetrau, 2019). The pressure would not come only from individual users but from the massive network of affected companies and organizations, demonstrating the high proportion of systems supported by old and outdated operating systems and software. Precisely, the virality of the worm that spread to computers without a direct connection to the Internet was what caused the extensive damage by blocking from port control systems to point-of-sale terminals in supermarkets through medical equipment in the operating room. On June 27, a new attack, called NotPetya, replicated the WannaCry attack, linking ransomware already known as Petya to the same EternalBlue vulnerability. Petya exploits a vulnerability by encrypting the master boot record (MBR) of hard drives. NotPetya again wreaked havoc in many countries, showing that WannaCry had not solved the problem of updating and maintaining equipment supported by older systems. Damages to companies such as the maritime operator Maersk are still a case study. The attack vector for NotPetya also used phishing to trick users into downloading a supposed custom software update package delivered over HTTP from the MeDoc software system. The ransomware has a worm component that uses the Windows Management Instrumentation Command-Line (WMIC), and the MS17-010 (EternalBlue and EternalRomance) exploits to spread laterally via SMB. 847

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The lessons learned from the series of attacks unleashed by WannaCry showed that the complexity of the instruments deployed required a level of protection and response capabilities much higher than the existing one. The simple solution of installing the Microsoft patch that solved the MS17-010 vulnerability is not the definitive solution since the patching process must be integrated into the procedure and control metrics of all the software systems of the organizations. Therefore, installing and monitoring critical patches must be programmed and experimented with in parallel before their installation in our network. In parallel, deploying continuous monitoring of effects and interactions should allow us to activate our business continuity plan and disaster recovery plan beyond the attack. In addition, the pattern of viral attacks from networks of systems based on obsolete technological systems is another element that has been repeated since then in multiple episodes that continue to reproduce the strategy opened by WannaCry, so, if it was about a proof of concept perpetrated by a government or criminal organization, demonstrated its effectiveness to date.

Social Engineering at the Base of Most Attacks Today The studies on the identification and analysis of the criminal groups behind social engineering attacks called APT (Advanced Persistent Threat) constitute an essential group of investigations. This research area unites the considerations of procedures and computer techniques with the criminal behavioral analysis of the criminal groups to extract predictive knowledge and intelligence in defense and security. Another example of this type of research is the series of studies on this type of attack that has affected industrial environments in the last decade. One group investigated was the so-called APT1 (Rid & Buchanan, 2015), and during the period 2010-2012, other research targeted groups such as Sandworm (Hultquist, 2016), which begin to design more APTs. A long series of research papers have focused on analyzing the first malicious software designed for Industrial Control Systems (ICS): Stuxnet (Langner, 2011; Falliere et al., 2011). Between 2013 and 2015, researchers have also been attracted to monitoring the performances of a new group known as Dragonfly (Langill, 2014). In the last five years, more and more groups and APTs supported by governments have appeared, attracting the interest of researchers, such as the 7 Unique groups. Other related issues cover raising new forms of malware such as Blackenergy2 and Havex, based on an attack directed at a metallurgical plant in Germany similar to the historical attack on the nuclear system Stuxnet virus. Other malicious software such as Crashoverride and Trisis, which attack electricity distributors and petrochemical factories, respectively, have also attracted analysts’ attention (Greenberg, 2017). In summary, in recent years, there has been a growing interest in cyber-attacks that aim to do human harm through the boycott of industrial infrastructures and, in particular, attacks on energy distribution networks. A report by Drago’s (2017) reported that 64% of the vulnerabilities identified in industrial control systems globally could not be eliminated, and 72% of the attacks they received could not be mitigated.

INCREASE OF SOCIAL RESEARCH IN CYBERSECURITY The strategic nature of information and data for digital business has relegated social research initially by studies based on solving technical and engineering problems. Of the more than 9,500 academic articles

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registered with references to the WannaCry attack in Google Scholar between May 2017 and May 2021, the vast majority focus on the technical analysis of the attack. However, immediately the interest in the human factor behind attacks increasingly mediated by criminal, political, or social activism interests has led to the growing number of investigations that seek to outline the attackers and the response mode of organizations and people in the face of these threats. A lucrative cybersecurity technology, services, and consulting industry has fueled the formation of investigative teams by global market leaders in the cybersecurity industry. Meanwhile, the study of people and social agents involved in cybersecurity issues has been restricted to groups of researchers in academia and public organizations. Only a few giant corporations have invested in the social responsibility for cybersecurity, like Microsoft Corporation. In 2008 this company founded its Digital Crimes Unit (DCU) in Redmond (Washington) to incorporate its cybersecurity means to fight against three types of criminal behavior: malicious software, child protection, and intellectual property protection. (Dupont, 2017). However, we can consider that the main issue is relative to the double speed of research on the phenomenon of cybersecurity in social agents compared to economic agents, in favor of the business world. Another problem for academic and social research is the enormous lack of cybersecurity professionals in all labor markets. Finding professionals specializing in this area raises job offers for them to high levels. This problem harms the possibility that public organizations or universities can compete for wages when hiring expert researchers. In other words, the availability of cybersecurity experts in social science departments is limited and limits the possibilities of incorporating sufficient knowledge to expand research in this discipline.

Taxonomy of Social Research in Cybersecurity The instrumental utility of creating Research ontologies is especially relevant in the field of engineering and computer science. They help to facilitate the work of characterization, exploration, and analysis of the research landscape. In scientific areas such as Biology or Physics, there are large-scale taxonomies to describe research fields. In Computer Science, the prestigious Association for Computing Machinery (ACM) has a classification or taxonomy framework that contains more than 2000 research topics (Salatino et al., 2018). A systematic evaluation of the scientific-technological literature on cybersecurity research of the last five years will help clarify social research topics’ issues. Biolchini et al. (2005) propose a method to carry out systematic reviews focused on software engineering, which other authors apply in works of systematic reviews concerning the management of computational engineering projects and that facilitates the thematic analysis of the state of the art of research in the field of computational engineering (Mesquida et al., 2012). The protocol marked by Biolchini et al. (2005) starts with the definition of the objectives of the evaluation to determine the focus of the question. This limitation establishes the sources of consultation and the exclusion and inclusion criteria to be applied in the review to proceed with data extraction and synthesis of results. Starting from the confluence base between theoretical approaches and multiple researchers raised above, questions are posed in this phase of the systematic review, whose answers could be found in the scientific literature. According the protocol defined in Biolchini et al. (2005), the following questions are answered:

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Step 1: Problem (contextualize the objective of the systematic review). Social research in cybersecurity. Step 2: Question that guides the systematic review. What social research is done in the analytical field of cybersecurity? Step 3: Expected results. Initiatives, topics, and proposed methods. Step 4: Measurement of the result. Step 5: Number of proposals identified. Step 6. Observed population. Publications focused on social issues. Step 7. Application of the results: Social researchers in cybersecurity. Following the protocol of Biolchini et al. (2005), the sources are defined in the Table 1, for which recognized databases had been chosen. Five top consulted databases have been selected targeting publications related to the area of computing as well as publications in the domain of social sciences. The purpose is to cover the research topics related to cyberattacks. The five databases used for the analysis were the Digital Library of the Association for Computing Machinery, the International Bibliography of the Social Sciences (IBSS), JSTOR Digital Library, SCOPUS of the publisher Elsevier, and Springer Database. The reference period has been the last five years since 2017, when the WannaCry attack occurred. Table 1. Systematic review 1

ACM Digital Library

https://dl.acm.org/

2

International Bibliography of the Social Sciences (IBSS)

https://search.proquest.com/ibss/advanced/socialsciences/

3

JSTOR

https://www.jstor.org/

4

SCOPUS

https://www-scopus-com

5

Springer

https://www.springer.com/gp

We tried to find a significant sample, proceeding to select an analysis case already described above, the WannaCry case, which would allow us to analyze the presence of topics, methods, or our approaches to social research. Keyword searching has been restringed to ‘WannaCry,’ a unique name for a cyberattack. This strategy guarantees that selected publications cover this cybersecurity phenomenon regardless of the analytical approach. Thus, the systematic review was performed for referred keyword {WannaCry}.

Table 2. Resultados de búsqueda: publicaciones sobre WannaCry Source:

Last Access

Rejected

Relevant

Not Available

Primary

1

ACM Portal (Digital Library)

2021-06-12

15

117

6

111

2

International Bibliography of the Social Sciences (IBSS)

2021-06-03

32

58

0

58

3

JSTOR

2021-06-20

110

119

38

219

4

SCOPUS

2021-06-03

77

103

19

180

5

Springer

2021-05-16

0

766

0

766

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 Social Research Methods in Cybersecurity

The column “Rejected” indicates the number of publications that were rejected based on the following factors (Kitchenham, 2007): • •

Documents not related to cybersecurity despite including references to WannaCry. Duplicated documents between databases.

Results Following the guidelines of Biolchini et al. (2005), in this section, the proposed protocol is adapted to present the findings found from the studies carried out on the WannaCry phenomenon. Regarding the keyword used for the search, not all the publications include WannaCry as the main one. Some scientific or technical journals of organizations do not impose the use of keywords in their publications. After reading the abstracts and, in some cases, the complete document, WannaCry is only mentioned as an example or secondary reference. The following Table 3 shows the search result, including the papers, chapters, and publications discarded because they are duplicated in other databases or are not available. Table 3. Content Type Number

Percentage

Chapter

Type

689

51.6

Conference Paper

301

22.6

Article

230

17.2

Book

78

5.8

Conference Proceedings

17

1.3

Reference Work Entry

16

1.2

Reference Work

3

100

Table 4 shows a total of 1,334 publications, including references in the investigation to the WannaCry attack, whether or not it is specifically a monographic investigation or as part of their more general analysis on cybersecurity. The predominance of engineering and computer science studies, and other sciences such as mathematics and physics, is observed for the most part. 36.6% of them correspond to research that explicitly includes some research related to the social sciences and political science or economics. The division of the sub-disciplines is shown in Table 5. Finally, we have tried to refine the disciplinary classification by selecting the main research topic that allows classifying the research from the point of view of a social researcher. This refinement will enable us to observe that 14.9% of the investigations use social investigation techniques such as the interview or the questionnaire in a supplementary way to investigate aspects of problem-solving such as malware analysis and forensic analysis on cyberattacks. The other 9% are publications directly related to “social engineering” as a technical criminal strategy. The most significant source of interest in social research focuses on the legal aspects of the political and police fight against the considerable threat of cybercrime, in the first place and with 25.6% of

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Table 4. Disciplines Topics

Number

Percentage

Computer Science

662

49.6

Engineering

294

22.0

Business and Management

99

7.4

Political Science and International Relations

51

3.8

Finance

38

2.8

Criminology and Criminal Justice

32

2.4

Philosophy

31

2.3

Law

29

2.2

Social Sciences

27

2.0

Mathematics

18

1.3

Economics

16

1.2

Other fields

36

2.7

1,334

100

SUM

publications, followed by studies on industry and organizations in the field of management, with 23.3%, which focus on the study of cybersecurity management systems in organizations. With 14.7%, the works framed as research related to international relations, political analysis at the macroscopic level, and power relations and political analysis are prominent. The study on the repercussions of these attacks on the map of international relations has been of particular interest.

Sources Regarding data sources, the increasing limitation in access to public and open sources of information related to investigations on patterns and behavior of threats and attacks has been mentioned above, increasingly limited to statistical sources provided by the authorities, such as reports of cybercrime or security incidents managed by the public Computer Emergency Response Teams (CERTs) in each country. In-depth studies on threats such as the groups behind APT attacks or criminal organizations that generate botnets are usually reserved for specialized corporations and government intelligence services. Still, they are difficult to access for the research community. The reluctance of companies to report many of the cyber incidents and share information about the attacks received for fear of revealing confidential information and affecting their reputation has caused the information sharing and analysis platforms not to work correctly. Experts with high responsibilities in police forces estimate that only an average of 15% of the attacks received by companies is reported to the authorities. Also, platforms for exchanging information on attacks or cyber threats, such as NATO’s own, are underused when they do not lack in use by the majority of the governments or participating entities. The statistics also indicate a low resolution of crimes by the different police forces, less than one in three. Police unions complain of the lack of means and personnel to deal with an increasingly specialized and changing type of crime, which requires hours and hours of in-depth study to unravel the computer plots

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Table 5. Specific Social Research Subdisciplines Topics

Number

Percentage

Computers and Society

46

9.4

Legal Aspects of Computing

40

8.2

IT in Business

34

7.0

Cybercrime

33

6.7

Security Science and Technology

32

6.5

Data Mining and Knowledge Discovery

27

5.5

Management of Computing and Information Systems

26

5.3

Privacy

24

4.9

User Interfaces and Human Computer Interaction

24

4.9

Business Strategy-Leadership

23

4.7

Game Theory, Economics, Social and Behavioral Sciences

21

4.3

Health & Medical

19

3.9

International Security Studies

19

3.9

Crime Control and Security

18

3.7

Computer Crime

16

3.3

Computers and Education

16

3.3

Public Policy

16

3.3

Political Science

12

2.5

Computer Industry

12

2.5

IT Law, Media Law, Intellectual Property

11

2.2

Public Administration

11

2.2

Criminology and Criminal Justice SUM

9

1.8

489

100

Table 6. Research topics related to Social Research Topics

Number

Percentage

Awareness

21

4.3

Malware analysis and forensics

73

14.9

Victims’ behavior

23

4.7

Attackers’ behavior

44

9.0

Management systems and business strategy

114

23.3

Internet crime and legislation

125

25.6

Sociology and Education

17

3.5

International relations & Cyber Defence

42

8.6

Political analysis

30

6.1

SUM

489

100

853

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(Harkin et al., 2018). These limitations oblige Law Enforcement Agencies to resort to the help of the private sector. Collaborations with expert security companies are essential and are reflected in the fact that many of the research authors belong to these or are part of the research team, very useful in a field in which threats are constantly evolving and redefining themselves.

Social Research Methods In the 1980s, the cinema featured the first films starring lonely young hackers, who risk the security of the Internet, governments, and large corporations to the test, trying to access confidential information irregularly. However, today cybercriminals have a very different profile. We can affirm that there is a real value chain around technological crime. We are no longer talking about exploits of individual hackers seeking to overcome technical challenges and community recognition of their exploits. Still, we face fully professionalized criminal organizations that recruit experts in cybersecurity for its practices with various illicit purposes. New players have entered the security scene with unique motivations, such as cyberterrorism, cyberwar, or hacktivism, not related in principle to economic benefit. Their actions can significantly impact and consequences since their objectives are no longer users, but governments, companies, and so-called critical infrastructures. Those organizations and industries on whose normal functioning the continuity of daily economic and social life depend. The methodological perspective of the profiling of users behind the attacks and the social analysis of the victims’ profiles are part of a long tradition of studies usually identified as “socio-technological” (Olayemi, 2014). This criminological perspective is fundamentally based on qualitative research techniques, including the in-depth interview, the semi-structured interview, and the analysis of discourse and legal and regulatory sources such as public policies to combat cybercrime. The recourse to the study of cybercrimes from the perspective of the social investigation of crime is commonly based on the alternative to analyzing these qualitative data sources from the existing crime theories and understanding the intentions, purposes, and methods of the crime agents involved in cybercrime. The four theories of crime, namely the frameworks of structural functionalism (Rosenfeld, 1989; Burton & Cullen, 1992), the Marxist theory (Heidensohn, 1989; Giddens, 2001), the theory of routine activity (Peguero & Popp, 2012; Miller, 2013) and technology-enabled crime theory (McQuade, 2006), continue to frame the interpretation of such motivations and are the basis for interpreting and trying to predict the illegal behavior of criminal groups (Olayemi, 2014). As we have pointed out above when referring to the studies on the WannaCry case, the number of research publications on the phenomenon of cybercrime is especially numerous, especially from the field of criminal law, international law, and the legislation of the states regarding how has come to typify and prosecute the behaviors of cyber attackers. In this sense, scholars focus on the limitations of the international framework for cooperation between States and the limits of the prevailing Budapest Convention, or Council of Europe’s Convention on Cybercrime (2001), which typifies attacks on the CIDAN parameters of systems (confidentiality, integrity, availability, authentication, and non-repudiation). There is a consensus on the necessity of reshaping the global index of legislation issues related to information and communications, fraud and falsification, crimes related to digital content such as child pornography, and crimes against intellectual property. It is a very restricted approach to new technologies as an instrument to commit traditional crimes, extended to the digital object as an asset or value that should be protected, that is, they include the com854

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puter or networks as an instrument to commit a crime or illegal activity (Thomas and Loader, 2000; Canadian Government, 2002; Maat 2004). Researchers display in their work a deep concern about the need to reform national and international legal systems, which facilitate working with the new types of crimes that have acquired relevance or are beginning to emerge as subtypes of hate crimes, or the generation of “fake news” that are not even included in many of the criminal laws. In this sense, the constant growth of publications that broaden the criminalistic approach facilitates the consolidation of studies in law and political science, especially using theoretical frameworks such as the Theory of Technology-Enabled Crime (McQuade, 2006; Holt & Bossler, 2015). This approach makes it more manageable for the researcher to understand the new forms of crime that have become a reality with the emergence of the digital world. In this sense, this theoretical framework assumes that there is something more than changes in the instrumental plane of criminal activity but also a cultural change. There is a profound change of meanings of human actions and relationships that are based on the flows and interactions that it allows the digital dimension, in the sense that M. Castells establishes it as the transformation of our’ material culture’, by the work of a new technological paradigm organized around information technologies” (2001: 59-60). This tendency to broaden the focus of social research is also seen in approaches such as those pointed out by Buzan and Hansen (2009: 39-40). They identify six dynamics that explain the evolution of social research on cybersecurity policies. At first, in the 1980s and 1990s, these were studies that initially covered topics such as military security and state security. In that early period, they focused on analyzing human behavior based on deviations from appropriate use following technology rules, respect for technical and legal statutes, and regulations that govern acceptable practices. In this way, the ethical or moral questioning of the behavior was emptied, ruling out that the dysfunctional use could be due to an error, chance, or action necessary to solve a more significant problem. This perspective has predominated in studies on the use of technology since, from an engineering perspective, respect for the function of technology prevails over other variables. However, the same authors Buzan and Hansen (2009: 258) consider that political studies have progressively broadened their focus to the problems of users and citizens, environmental security environments, or economic phenomena. Our analysis concludes that there is indeed a greater diversity in the coverage of the studies, although the central aspect of the State continues to be remarkable in this subdiscipline. For their part, Dunn Cavelty & Wenger (2020) have recently reviewed political research on cybersecurity, noting the predominance of the post-positivist research tradition and its use of multiple theoretical and conceptual frameworks to analyze the phenomenon. The classification of these authors concerning the research literature on cybersecurity policies groups the sets of research approaches into three: 1. Post-positivist research tradition. 2. Analysis of public attributions of cyber incidents, the norm-setting day-to-day behavior of intelligence agencies, and discussion on the consequences for governments and governance. 3. Exploration of the knowledge-shaping practices of IT-security companies, the co-production of risks and vulnerabilities by technology and experts, and understanding of the role of firms and experts in strategic state interactions. Regarding the studies that resort to quantitative social research techniques, the influence of most current studies in the field of management is noted. In this sense, the works try to resort to social research as a 855

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type of data source that allows collecting the opinion, degree of knowledge, or dissemination among the organizations’ members. Studies such as those by Li et al. (2019) apply to the establishment of indirect measurement procedures, through surveys or interviews, of controls of the level of awareness that the personnel of organizations have, and therefore help deploy cybersecurity management systems following internationally accepted standards and guides of good practices in cybersecurity governance such as ‘COBIT® 5 - the framework for the governance of enterprise IT’ or the ‘ISO / IEC 27001 - Information security management’ standard (De Haes et al., 2013). They also facilitate the measurement of the level of extension in compliance with policies, procedures, or legal measures, which from the point of view of accountability born in the quality management models of the 1990s and facilitate the development and evaluation of public policies and private (Brechbühl et al., 2010). Returning to our analysis on the WannaCry phenomenon, we note that studies on the business environment as well as those related to health or educational organizations, effectively focus on analyzing the level of awareness of employees, managers, or users of services and their importance for the prevention and response to this type of attack.

FUTURE RESEARCH DIRECTIONS In an environment in dizzying evolution, where the opacity of criminal organizations and where the governments see cybersecurity as an instrument of cyber defense or their offensive capabilities, social issues demand special attention. The low costs, opacity, and high effectiveness for attackers stress cybersecurity professionals under increasing pressure to access knowledge and constantly renew their preparation against the innovating threats. The availability of expertise not only in the field of engineering and computing but also of advances in social research is increasingly necessary for an area such as cybersecurity. The reason is that human behavior and its anticipation at the social, political, or economic levels becomes critical. Unfortunately, the governmental strategies of the States to promote the financing of social research in the field of cybersecurity are not included at the expected level. Future trends in cybersecurity research, both nationally and internationally, are based on an analysis of the strategic lines of research in cybersecurity that prioritize the Strategic Research Agendas such as those of the United States government or the European Strategy for Cybersecurity An analysis of the European Cybersecurity Research Strategy of the last five years allows us to identify the following top ten priority research objects in the field of cybersecurity (Bisson et al., 2015; European Cyber ​​Security Organization, 2018): • • • • • • • • •

856

Partial identities. Credibility and development of big data. Data protection techniques. Big data analytics privacy. Secure data processing on encrypted data. Prevention and intrusion control systems. Continuous monitoring and security in the cloud. Authentication in IoT devices. Networks for intrusion detection systems (IDS).

 Social Research Methods in Cybersecurity

The analysis of research trends in these fields indicates that the efforts of the public and private research network are fundamentally being directed to the development of protection techniques and systems, particularly in critical infrastructures and services. These include the business sectors of energy, the nuclear industry, the chemical industry, and the financial sector, comprising approximately 80% of the research actions. The aeronautical industry represents 7% of the activities. The topics considered tend to correspond to prevention and intrusion control engineering systems, authentication in IoT devices, continuous monitoring and security in the Cloud, the privacy of extensive data analysis, big data, and operations on encrypted data. This emphasis on developing technologies and information management systems and networks contradicts the growing weight that social research is acquiring in strategy design. However, an analysis of the experts summoned by the European Commission to elaborate the Strategy shows a monopolistic preference for the disciplines of computing and telecommunications. Consequently, public funding is currently limited for social research and should be expanded in the future. Research on the actual dimension of risks and threats to cybersecurity is particularly relevant since government reports often try to create currents of opinion favorable to security and defense policies, focusing on those threats they consider to be the most pertinent or priority. Likewise, there is the risk that the cybersecurity industry will generate numerous documentation that sometimes tries to enhance the negative image of the inherent risk for companies and users. So that in both cases, we may face a bias and lack of neutrality in the presentation of results of their research, either for political or purely commercial interests (Lindsay, 2017). Indeed, the most relevant technological changes in terms of the transformation of the security conditions of the space will focus on the proliferation of the Internet of Things devices, the hyper-speed of the new 5G and 6G communications protocols. These environments will enable more and unknown vulnerabilities. The massive incorporation of Artificial Intelligence into industrial organizations and robotics will require a more in-depth and extensive analysis. A study carried out in 2016 on the attacks reported in the news through data analytics reflected that the distribution of cybersecurity incidents has multiplied incessantly in these industrial environments (Reusch, 2016). The growing risks are based on the increasing complexity in developing ubiquitous devices connected to the internet and the combination of the Internet of Things with the Web of Things. The development of regulatory models and technological standards that help prevent the wild and chaotic action of products, solutions, and communication protocols for the so-called IoT is insufficient (Reusch, 2016; Newell, 2017). An example of these limitations of standardization is the absence of wireless communication standards for IoT devices. The regulatory nature of engineering has always driven adopting measures that facilitate and standardize the design, characteristics, and uses of technology of information and communications systems. These systems are characterized by high dispersion and a lack of consensus among manufacturers who compete rather than collaborate. Yes. A clear example is the communication protocols between devices, regardless of the World Wide Web. Many organizations are trying to generate new cybersecurity standards that impact IoT environments, Cloud, 5G connectivity, Quantum Computing, and other disruptive technologies. These include organizations as diverse as the International Organization for Standardization (ISO), World Wide Web Consortium (W3C), Internet Engineering Task Force (IETF), National Institute of Standards and Technology (NIST), The European Union Agency for Cybersecurity (ENISA), ITU Telecommunication Standardization Sector (ITU-T) [22], Association for Automatic Identification and Mobility (AIM), IoT 857

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Security Foundation, Industrial Internet Consortium (IIC), Open Connectivity Foundation (OCF), Open Geospatial Consortium (OGC), Global Language of Business (GS1) and the International Council on Systems Engineering (INCOSE). These organizations have generated more than forty technical standards just of IoT applicability and intervene directly in the security of these environments. However, they present different measurement parameters, controls, and metrics. This anarchy constitutes a problem since there is no single library or repository, which reduces transparency for developers, who differ in their level. In general, overlap and chaos hinder the interoperability and usability of cybersecurity standards since they reflect competition between manufacturers, distributors, and customers, which monopolize the technical standardization committees (Ileszczyna, 2018). In this sense, the lack of control and audit entities, in the form of accredited cybersecurity laboratories, is a limitation for the thousands of manufacturers and even more so for the owners of an industrial system who want to carry out a configuration origin check device safe according to this type of standards. In parallel, from 2017 to 2020, companies’ spending on Industrial Internet of Things (IIoT) security has increased by 50% due to this growth in attacks on industrial infrastructures and growing regulatory pressures (Lhereux, 2018). In this sense, the economic basis for the development of IoT devices, and also the explanation for their proliferation in industrial environments, is the financial advantage it represents concerning ad hoc developments of products or solutions that required specific programming adapted to a line assembly or factory. The availability of products in the market at affordable prices thanks to the globalization of the economy has made it easier for industrial environments to open up to the massive incorporation of devices with very little investment. Regarding social studies within criminology, researchers point to an enormous degree of sophistication of criminal organizations. We can say that in cyberspace, police activity loses weight: you can try to prosecute the criminal, but you cannot avoid that the crime continues to be committed. Cybercriminals adopt increasingly sophisticated schemes and technologies to perpetrate attacks. Thus, malware, the significant threat of the current moment, has specialized for some time now to give rise to zombie computer networks or Botnets, and Advanced Persistent Threats (APTs, Advanced Persistent Threats) that combine the use of malware with social engineering techniques and are growing exponentially in recent years.

CONCLUSION We share with Dunn Cavelty and Wenger (2020) that cybersecurity transcends levels of analysis, requires considerable interdisciplinary knowledge, and will be shaped by the availability of new data and methods in the years to come. In this sense, it is possible to coincide with the postpositivist perspective that it is difficult to reduce to a single theoretical framework of analysis the understanding of the complex model of interaction between technology, data, and humans, which has generated a cultural and civilizational framework unprecedented in just three decades (Castells, 2001). We are facing a scientific field of cybersecurity that has progressively transcended from computer science and systems and network engineering to an interdisciplinary approach where social sciences have reached a dominant weight. The strong linkage of cybersecurity with the aspects related to crime, the behavior of users with technology, social media, the sociology of organizations, and a wide range of topics is commonly accepted. The success of a concept under construction, such as cybersecurity and its ability to go beyond Information Security and Communication Networks, expanding its spectrum to 858

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aspects related to the security of states, the economy, and society in general, is a fact (Dunn Cavelty & Egloff, 2019). In other words, the social construct that cybersecurity represents as a framework of conditions that facilitate the normal development of social and economic life, respect for human rights, and in particular the privacy of people, cannot be limited to conceiving security as a mere instrumental element based on the characteristics and limitations of the uses of ICT tools.

ACKNOWLEDGMENT This research received no specific grant from any funding agency in the public, commercial, or not-forprofit sectors.

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Pandita, R. (2017). Internet: A change agent an overview of internet penetration & growth across the world. International Journal of Information Dissemination and Technology, 7(2), 83. doi:10.5958/22495576.2017.00001.2 Papadopoulos, T., Baltas, K. N., & Balta, M. E. (2020). The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice. International Journal of Information Management, 55, 102192. doi:10.1016/j.ijinfomgt.2020.102192 PMID:32836646 Peguero, A. A., & Popp, A. M. (2012). Youth violence at school and the intersection of gender, race, and ethnicity. Journal of Criminal Justice, 40(1), 1–9. doi:10.1016/j.jcrimjus.2011.11.005 Pranggono, B., & Arabo, A. (2021). COVID‐19 pandemic cybersecurity issues. Internet Technology Letters, 4(2), e247. doi:10.1002/itl2.247 Reilly, I. (2018). F for Fake: Propaganda! Hoaxing! Hacking! Partisanship! and Activism! in the fake news ecology. Journal of American Culture, 41(2), 139–152. doi:10.1111/jacc.12834 Reusch, F. A. (2016, July). Remarks to the security of Web of Things [Paper presentation]. Digests F2F Meeting W3C, Web of Things. World Wide Web Consortium. Rey, J. P., Corrons, L., García, M., & Ramírez, A. (2017). Reportaje, WannaCry, la penúltima gran amenaza. Ctrl: Control & Strategies, (646), 38-41. Rid, T., & Buchanan, B. (2015). Attributing cyber attacks. The Journal of Strategic Studies, 38(1-2), 4–37. doi:10.1080/01402390.2014.977382 Rosenfeld, R. (1989). Robert Merton’s contributions to the sociology of deviance. Sociological Inquiry, 59(4), 453–466. doi:10.1111/j.1475-682X.1989.tb00120.x Salatino, A. A., Thanapalasingam, T., Mannocci, A., Osborne, F., & Motta, E. (2018). The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas. In Lecture Notes in Computer Science: Vol. 11137. The Semantic Web – ISWC 2018. Springer. doi:10.1007/978-3-030-00668-6_12 Saltzer, J. H., & Schroeder, M. D. (1975). The protection of information in computer systems. Proceedings of the IEEE, 63(9), 1278–1308. doi:10.1109/PROC.1975.9939 Sophos. (2018). The State of Endpoint Security Today. An independent study of 2,700 mid-sized. https:// www.sophos.com/en-us/medialibrary/Gated-Assets/white-papers/endpoint-survey-report.pdf?la=en Symantec. (2019). ISTR- Internet Security Threat Report, 24. https://docs.broadcom.com/doc/istr-242019-en Ting, D. S. W., Carin, L., Dzau, V., & Wong, T. Y. (2020). Digital technology and COVID-19. Nature Medicine, 26(4), 459–461. doi:10.103841591-020-0824-5 PMID:32284618 Trautman, L. J., & Ormerod, P. C. (2018). WannaCry, ransomware, and the emerging threat to corporations. Tennessee Law Review, 86, 503–550. doi:10.2139srn.3238293 Turner, A. B., McCombie, S., & Uhlmann, A. J. (2019). A target-centric intelligence approach to WannaCry 2.0. Journal of Money Laundering Control, 22(4), 646–655. doi:10.1108/JMLC-01-2019-0005

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Verble, J. (2014). The NSA and Edward Snowden: Surveillance in the 21st century. ACM Sigcas Computers and Society, 44(3), 14–20. doi:10.1145/2684097.2684101 Von Solms, R., & Van Niekerk, J. (2013). From information security to cyber security. Computers & Security, 38, 97–102. doi:10.1016/j.cose.2013.04.004 Waever, O. (2010). Towards a political sociology of security studies. Security Dialogue, 41(6), 649–658. doi:10.1177/0967010610388213 Walton, S., Wheeler, P. R., Zhang, Y., & Zhao, X. (2021). An Integrative Review and Analysis of Cybersecurity Research: Current State and Future Directions. Journal of Information Systems, 35(1), 155–186. doi:10.2308/ISYS-19-033 Wang, Z., Zhang, T., Yang, Y., & Qu, H. (2016, November). Comparison of Security Frameworks for Governmental Clouds between United States and European Union. In Proceedings of the 6th International Conference on Communication and Network Security (pp. 30-34). 10.1145/3017971.3017985 Woolgar, S. (1996). Technologies as cultural artefacts. In W. Dutton (Ed.), Information and Communication Technologies: Visions and Realities (pp. 87–102). Oxford University Press. World Economic Forum. (2021). The Global Risks Report 2021. WEF. http://www3.weforum.org/docs/ WEF_The_Global_Risks_Report_2021.pdf Zimba, A., & Chishimba, M. (2019). On the economic impact of crypto-ransomware attacks: The state of the art on enterprise systems. European Journal for Security Research, 4(1), 3–31. doi:10.100741125019-00039-8

ADDITIONAL READING Bisson, P., Martinelli, F., & Granadino, R. R. (2015). Cybersecurity Strategic Research Agenda-SRA. European Network and Information Security (NIS). Platform NISP-Working Group, 3, 1–201. Dunn Cavelty, M. (2018). Cybersecurity research meets science and technology studies. Politics and Governance, 6(2), 22–30. doi:10.17645/pag.v6i2.1385 Edgar, T. W., & Manz, D. O. (2017). Research methods for cyber security. Syngress. European Cyber Security Organization. (2018). European Cybersecurity Strategic Research and Innovation Agenda (SRIA) for a contractual Public-Private Partnership (cPPP). https://ecs-org.eu/documents/ ecs-cppp-sria.pdf Macnish, K., & van der Ham, J. (2020). Ethics in cybersecurity research and practice. Technology in Society, 63, 101382. doi:10.1016/j.techsoc.2020.101382 National Science & Technology Council. (2019). Federal Cybersecurity Research And Development Strategic Plan, https://www.nitrd.gov/pubs/Federal-Cybersecurity-RD-Strategic-Plan-2019.pdf

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Stevens, T. (2018). Global cybersecurity: New directions in theory and methods. Politics and Governance, 6(2), 1–4. doi:10.17645/pag.v6i2.1569

KEY TERMS AND DEFINITIONS Authentication: It is the situation in which it can be verified that a document has been prepared or that it belongs to whom the document claims to belong. Authentication occurs when the user can provide some way to verify that said person is who he claims to be; from that moment he is considered an authorized user. Availability: It is the capacity of a service, data, or a system, to be accessible and usable by authorized users (or processes) when they require it. Botnet: Software artifacts built by cybercriminals as basic infrastructures to support different variants of cyberattacks, allowing multiple remote computers to infect and control up to millions of computer computers without their owner’s knowledge, being controlled to launch denial of access attacks, theft of information, and bank credentials or means of payment and sending millions of messages with harmful content. CIDAN: The concepts of confidentiality, integrity, authenticity, availability, and non-repudiation are prevalent in the field of cybersecurity and appear as fundamental in any information security architecture, either in the area of current regulations related to the protection of personal data, such as in the application of codes of good practices or recommendations on information security management and prestigious international certifications such as that relating to the ISO 27000 family of standards. Confidentiality: The quality that a document or file must have so that it is only accessible in an understandable way or is read by the authorized person or system. DDoS: The use of malicious software to organize automated attacks through IoT devices, specifically denial of service attacks (DDoS), is currently another effective new formula of cyber-activism, in which a large volume of traffic is directed towards a specific service or website (normally governmental but not exclusively) to make it inoperative. Integrity: It is the quality of a document or file that has not been altered. That also makes it possible to verify that there has been no manipulation in the original document. Non-Repudiation: Or inalienable, it is a security service closely related to authentication, and that allows to prove the participation of the parties in a communication. For example, when someone has sent us a message if they are who they say they are. Security Controls: The principles of control of systems, devices, and connectivity were established early in the global standards, mainly through the COBIT guides or the ISO 27000 family standards. They imply the need to guarantee security through instruments for monitoring unwanted behavior that detects and prevents unwanted use by third parties. Social Engineering: It can be defined as the mechanism to obtain information or data of a sensitive nature. In essence, they are persuasive tactics that tend to use the goodwill and lack of caution of users. Threat: A threat is understood to be any action that tends to be harmful. It triggers a security incident that can eventually lead to material damage or immaterial loss of assets.

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Chapter 47

Composed Cognitive Maps: How Little Things Became Big in Crime Analysis Daniel Castro Aniyar Universidad Laica Eloy Alfaro de Manabí, Ecuador

ABSTRACT Composed cognitive maps are a tool based on grounded theory and on Lynch’s urban model of cognitive maps, which allow the transfer of information from ethnographic situations to general patterns, and to the so-called spatial dynamics. In criminological matters, they have been applied in the context of environmental and criminology of place to identify criminal situations, criminal patterns, and spatial dynamics of crime. The latter concept has allowed reliable diagnoses for the design of criminal policies. Their advantages are compared with traditional criminometric methods. It introduces a brief compilation of the existing literature on the subject. In a special way, this chapter shows how composed cognitive maps allowed the measurement of drug trafficking networks, police intelligence, and, above all, crime reduction.

INTRODUCTION: THE PROBLEM OF LITTLE THINGS Inter-American Development Bank promotes a “Prevention Protocol based on Evidence”, designed by Lawrence Sherman, which to date serves as the basis for the design of criminal policies in Latin America. The protocol established that: “A strong minority, a small proportion of all units of criminal conduct, causes the most damage to most types of crime”, and because of this, “efficiency in crime prevention may increase when resources are concentrated in strong minority units, identified by the use of patterns from past behavior” (Sherman 2012, p. 8). Also, the notion of crime routines in small territories as the primary step to (dis)organized crime, has become a common debate in crime policies studies (Mclean 2021; Brantigham & Brantigham 1992). This perspective is widely supported by an important criminological movement that maintains that, without disregarding the importance of other factors, most efficient criminal policies perceive commission DOI: 10.4018/978-1-7998-8473-6.ch047

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of crimes, above all, as a transaction between aggressors and victims in the spatial situation (Weisburd et al, 2012; Vozmediano Sanz & San Juan Guillén 2015; Clarke, 1995). To identify the patterns of such opportunities inside crime situations would allow to prevent crime in a more efficient way. Also, it has been related the efficiency of proximity police to the fight on terrorism. Early reports on suspicious activities and a healthy relation between proximity police and community seems to be one of the most important tools for preventing terrorism, as it was recently studied in Nigeria (Tarela 2018, pp. 107-112). Some of the literature calls this perspective “Policing Oriented to Problems” (or POP, in English) (Scott & Stuart 2012; Sherman, 2013; Eck & Weisburd 2015; Felson 1995). Prevention policies that recognize these opportunities and to act on them, which not necessarily use force but proximity presence and situation-oriented policies at small territories and hotspots, show greater possibilities of measurable efficiency that, for example, policies oriented exclusively towards repression-deterrence, the socioeconomic structure, school, community or family. This notion is still confirmed and warned by certain now a day criminology (EUCPN, 2021). This perspective shows the need for an instrument to closely observe the dynamics of crime that occur in specific spaces, so an approach from an instrument of victimization on that scale would generate valuable information for preventive use and police intelligence. The basic notion is to understand the crime phenomenon as a concrete and measured reality in observable exchanges. Quantitatively, small territories and hotspots relationship with greater crime statistics is not questioned yet, and it has been fundamental in a certain cutting-edge criminological debate represented by the “Law of Concentration of the Crime”. This notion shows, based on measurements in dozens of cities around the world, that about 4% of street segments, intersections and spaces would correspond to about 40% - 60% of the crimes committed in an urban conglomerate (Weisburd 2015). In this sense, microterritorial crimes are not a “minor” evil. Statistically expressed, they affect in an extended way to almost all the population of a country, a region and, above all, inside the corresponding urban conglomerates. Such proportions of context measurement imply the need of instruments dedicated to the microspace level and small territories through crime analysis (Torresano Melo & Calles López 2018; International Association Of Crime Analysis 2013). This angle is as important to policies design to urban prevention as fundamental for safety feeling or intelligence. But how can the situation, characteristically related only to small territories, be measured in order to use it in larger public policies? A problem related to building situation-oriented methods is the well-studied divorce between the micro and macro level. This is an issue that touches the multi-factoriality of crime. It is an assumed fact in criminology that crime is multifactorial, but its readings are so complex that they end up escaping the possibility of being easily measurable in prevention-oriented research inside a specific territory. Some more radical authors even proposed that police cannot be evaluated as a reduction of crime agent, because reduction crime factors are too hard to evaluate (Gabaldon 2007). Multi-factoriality puts the problem on transferring from the micro level to the macro level. This is not due to not recognizing a profitable relationship between the micro and macro levels (Short 1998) but because the methodology to project behavior from the micro to the macro level is not clear. Matsueda (2013, p.3), following Coleman, found that macro context can be suitable to understand micro context for crime or deviation, as micro-outputs could be suitable to understand macro crime or deviation outputs. But the lacking part is the relation between macro context and micro-outputs. In other words, policymakers do not know how to produce social change in small territories to help to improve the macro context of security, because they seem to be divorced. It is not possible to simply aggregate dyadic exchanges 868

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into a greater matrix. The same happens in economy where prices stablished by two persons cannot be compared to price dynamics at the structural level. The quantitative and qualitative methods may suffer, since this appreciation, from weak conceptual bases on which to project reliable information.

Common Techniques in Criminometry This sub-chapter will show cases of dysfunction of criminometric common techniques and analyze why these techniques are not enough to understand or to project crime situation. The departure problem of common techniques is that, even if security agents, as police, understand the importance of small territories, they do not translate situations into the computerized crime analysis systems. New crime analysis technology just uses 911 denounces or judicialized ones to perceive danger, disregarding situational description (Dammert 2018). An evidence of this is that crime analysis software packages are usually technically closed to this kind of additions. Traditional methods to understand crime dynamics are administrative denounces to Prosecutor’s office. Secondarily, the formal social control system can use police’s reports on specific crimes, and also victimization surveys (which, in most Latin American countries, were not applied or they expired). These three methods imply several problems, widely recognized by the same users. For example, police reports use to show poor understanding of the reporting process, delays in police responses, difficulties to arrive to the report centers, different kinds of bureaucratic pressures, among others (Castro Aniyar & Jacome, 2017a). On the other hand, large victimization surveys are not widely available and do not offer sufficient situational clues on specific or small territories (Castro Aniyar & Jácome, 2017a; 2017b; Damnert et al., 2010). Also, administrative denounces do not produce an integral picture of the criminal issue because they depend on conjunctural opportunities. This is the reason why black figures are so important in criminological debate (Van Kesteren, Van Dijk & Mayhew, 2014.p 51-52; Sullivan & Mcgloin, 2012; Damnert et al., 2010; Castro Aniyar & Jacome, 2017a). For the Ecuadorian case, it is possible to infer a black figure of robbery around 80% of the official data. It means, comparing victimization surveys and administrative denounces to prosecutor’s office or the police, 80% more Ecuadorians declared they were victimized but they did not denounce institutionally. And comparing Ecuador to other similar countries, as Paraguay or Colombia, Ecuador black figures seem to be smaller than theirs (Castro Aniyar & Jacome, 2017a). In other words, traditional surveys, reports and denounces clearly do not measure optimally crime. And their methods openly lack of a qualitative approach to identifying situation, although this notion gives actuality and memory to concepts as justice, conflict or peace (Bobrow, 2008; Gillmartin, 2000; Löschper, 2000; Loor et al., 2019). The “thick description”, a convened classical concept from anthropology, shows that a socio-cultural fact is always expressed symbolically and, for so, exists objectively in communication (Geertz, 1973). Therefore, by its nature, crime and deviation is not metaphysically hidden or occult in the history of signs, but always reflected in, for example, language, the use of space, social organization, social change, the significance of space, even if it is unconscious to the informant in a specific moment. However, crime situation is a kind of forgotten category in prevention policies. It is true that crime situation, as well as the set of opportunities, seen this way, not only exists inside the offender, victim and space triangle, but can also be detected using other instruments designed to collect specific information, as denounces and surveys. Buy to not observe that triangle from direct, systematic and scientific observation is a mistake. Intelligence police should agree. 869

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In the Ecuadorian case, the New Police Management Model (NMGP, in Spanish) and the model based on the so-called Peaceful Social Coexistence of the Ministry of the Interior, have reaped tangible achievements in the decrease in robberies and, above all, homicides / murders, as less, in the last 8 years (2009-2017). These achievements have been associated with models of police deconcentration, community participation in local security strategies, and the increase of the Units of Community Police (in Spanish, UPC) at small circuit level. NMGP taught that proximity and a more situational approach can produce positive results.

COMPOSED COGNITIVE MAPS: WHY THEY SUPPORT A MORE EFFECTIVE MODEL? Robert Kitchin has made a thoughtful effort to integrate behaviorist, cognitive psychology and human geography theories on cognitive mapping to help validate the relevance of cognition in transactions1. One of his integrative ideas explains that mapping constitutes a dual code. This phrase means a non-analogical dimension of a perceived spatial reality in contrast with a verbalized reality, which is, in turn, rebuilt, abstracted and analogic. However, the analogic narrative is the basic material of large surveys and other traditional criminometric methods, such as victimization surveys and administrative reports. In other words, verbalized formulas are an indirect expression of a kept image that is culturally shaped (Kitchin 1996, p 56-84; Friendly 2006; Downs, Roger & Stea 2005). Putting this in crime analysis perspective: if images are how people understand their victimization experience, what composed cognitive maps propose is to integrate image-related messages into criminometry. Also, Kevin Lynch, in its referential work on urbanism, The Image of the City (Lynch 1960; Universidad De Barcelona, 2018), introduced cognitive maps in order to understand how people navigate and understand the used and lived space. In this case, people were asked to draw the space they used and, in the drawing process, a previously non-verbalized set of memories showed useful information about the social relations attached to it, this means, to streets, referential points, pathways, gates, walls, open spaces, closed spaces, etc. When security issues were conversed between the researcher and the informant, during the drawing, information showed to be richer and not so defined by cultural or political conjunctural narratives. So, for Lynch descriptions, it was easy to see that the main tool of cognitive maps were not the maps themselves, but the researcher’s field journals. Lynch also observed that significance of space notion was radically important, but surpassed his urbanistic goals, so he just developed the formal construction of physical space inside the user imagination. From 1995 to 2015 several researching teams in South America, directly or indirectly directed by Prof. Castro Aniyar, proposed to introduce the idea of situation mapping through complexity-oriented methods (Gilmartin, 2000) to improve the comprehension of the relation between victim, space and offender, using the Lynch idea of significance of space. As suggested previously, this proposition was a response to common criminological methods that interpretate crime patterns only from police reports, surveys and administrative denounces. At the beginning of these researches, cognitive maps were applied to urbanism purposes in Venezuela (Castro Aniyar 2014). But the method clearly also showed its abilities for criminology in Venezuela (Castro Aniyar 2015), and criminology of place in Ecuador (Castro Aniyar 2019c). This latter experience renamed cognitive mapping to composed cognitive maps, because they mixed, through grounded theory, ethnographic observation and Lynch’s techniques to read the cognitive map drawing process. 870

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Weisburd wrote about them: “While the law of crime concentration is well supported by official data, we have little anthropological and qualitative data that helps us to enter the black box of mechanisms for these findings” (…) “this work has tremendous potential to strengthen crime prevention and public security more generally (…) Cognitive crime maps have tremendous potential for advancing basic and applied research on crime concentrations” (Weisburd 2020) Anthropologists use to observe two major problems that composed cognitive maps must face to give this anthropological quality to crime analysis. 1. Due to the experimental nature of situational theories in criminology or anthropology (Geertz 1973, p.23; Clarke 1995) it is not possible to automatically project the logic of a criminal opportunity from one situation to another. For this reason, it is important not to generalize crime policies in large territories if they cannot be previously tested by rigorous tools. It implies that criminal situation diagnoses also have to be made from small territories, where proximity police would have a better incidence because of its nature, but also have to be made with a pattern strategy, useful to project recurrencies into a larger level. This is the reason why grounded theory was integrated (Konecki 2011; Glaser & Strauss, 2009). It has the ability to build categories and validate them through saturation. So, information reliability depends on inner dynamics and not from presumed categories before the observation. In other words, categories and recurrencies come from the observed and saturated reality and not from pure hypotheses. 2. Another issue is that situation analysis must correspond to a much deeper interpretation of symbolic exchanges than, for example, surveys or administrative denounces. So it is not enough to have the way to transfer saturated categories if these informations cannot reflex situation independently of verbalized formulas (common in surveys or administrative denounces). Is important to offer what anthropology calls thick descriptions. The way composed cognitive maps use to solve this problem is to count on the ability of the drawing process to inspire new forms of memorization. While the witness or the victim of a crime draw their own experience, for example, of danger, security, hostility and risks, they are constructing again an allegory of the lived crime situation. So, the informant is surprised by the amount of information related or attached to the significance of specific space (Ruiz et al., 2006; Manzanero, 2006). This process was studied by neuropsychologist Ruth Loor (Loor et al, 2019) which observed how the method improved memorization of the lived facts, better than simple interviews.

HOW THIS METHOD WORKS? Composed cognitive maps are a victimization tool that produce better diagnoses from micro-territories to general dynamics of crime. These maps were designed to improve upon data collection methods by developing a richer and more complex understanding of the crime location. The maps, and interviews stemming from the maps, are used within a discourse analysis. They attempt to overcome the gap between the space/site/situation level, and cognitive, social, cultural interactions occurring at the crime site. Their promotors believe that the way offenders persistently use a space can be described by the maps 871

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their victims and witnesses build in their minds. In this way, victimization can be understood as a built situation between spaces, victims and community, which can express itself through patterns. In order to better illustrate the way they work, it is necessary to explain them technically. Please be aware that this not an exhaustive handbook, but only a very brief reference. Researchers must prepare themselves for several weeks, and need to have a specific background to achieve the expected results before applying the maps. 1. The victim or witness of crime, user of a space defined by the research is asked to draw a geographical map of the site. In the middle of this process a conversation about the drawing helps to collect information among victims and witnesses of crimes. The researcher asks to all users to make the map but will keep a specific and a deeper conversation with witness and victims. It means that witness and victims must not be selected previously. 2. It is required to ask about access points, hotspots, timespots, and other criminal categories produced from inside the field, like criminal dynamics, perceptions and feelings, by means of ethnographic discussion techniques and a semi-open thematic pattern. 3. Regarding the graphic representation of the space, always in relation to the conventional concept “map” or “sketch” (to achieve verifiable homogeneity of the data), it is requested that the places perceived dangerous be drawn in red, the hostile ones in green, the safe ones in blue, and the limits or borders of the studied space in yellow. 4. The data obtained by this mechanism is analyzed using grounded theory. It means that the categories of analysis must not be produced before the research but during and from the conversation. The pre-notions must surrender before the obtained information and categories in the field. 5. Since these maps focus on victims and witnesses, the composed cognitive maps can be identified as a victimization tool. So, they can be helped by categories produced by the world experience on victimization surveys. 6. In any case, situation-oriented policies, nor composed cognitive maps, do not need to substitute common techniques, but to complement them. This triangulation has been called the VDS (VictimDenounce-Situation) Trilemma, which teaches that two types of these criminometric methods cannot be effective without the third one. For more information, please refer to the related article (Castro Aniyar & Jacome, 2017a). 7. It is necessary to apply as much composed cognitive maps as needed to saturate the categories in the same way grounded theory suggests. Categories can explain crime dynamics only if they exist recurrently as social facts (indicated by a saturation factor) in the contextual space. In one hand, saturation gives more depth to categories not imagined before the research and, in the other hand, it allows the free observation of the researcher to have a certain statistical idea of the importance of a categories, observing its repetition form different angles. 8. Once all the collected information is described and discussed in detail to the entire researching team, and displayed on big blackboards or computerized systems, it is possible to see greater categories, even reuniting different spaces and territories outside the contextual or studied space. These new greater categories, outside the contextual space, have more interpretative power, and are called spatial dynamics of crime. So, the patterns can be observed, understood and used in prevention crime policies, from the individual basis (to intelligence purposes, for example), to contextual space (to understand crime opportunities in place) and to spatial dynamics of crime (which represent a much more consolidated set of patterns, useful to larger crime policies). 872

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9. The use of software protocols, specifically designed to composed cognitive maps and crime analysis altogether, is highly desirable. The purpose is to accelerate the analysis phase which, otherwise, would last several weeks. But the researchers have not found yet a way to interpretate information without a strong supervision of field analysts. Further research on software possibilities must be done. This composed cognitive map was made by a victim in a conflictive area in Quito’s downtown: Although is not clear on this map, the informant uses different colors to indicate the places she/he considers dangerous, hostile and safe, thus identifying her/his contribution to the recurrent understanding of crime patterns and spatial dynamics. The very process of drawing usually shows categories like hotspots, time spots, hostile spots, safe spots, crime triggers, inner otherness (including migration, families, limits, etc.), otherness before the city, crime modalities, social disorder types, urban (un) defensibility spots, antipode maps, among others. Analog narration is the type of material that large surveys collect, such as the traditional victimization surveys, administrative complaints, the presential or telematic police reports, or the Prosecutor’s Office reports. But verbalized formulas do not directly express a conserved image that originally comes from a cultural image, based on the idea that culture, like identity, also exists non-analogically. This idea suggests that, if images are the way people understand their experience of victimization in space (hotspots, micro-territories perceived as dangerous or hostile, for example), it is logical to assume that images themselves should be integrated into criminometric information, and also used as a mnemonic trigger of a complex experience, such as victimization.

What Happened When Composed Cognitive Maps Were Applied in Ecuador? There is an extensive literature showing how composed cognitive maps are associated or related to specific achievements and crime reduction experiences. Please refer to indicated sources or contact to the author to see and discuss the evidence in a detailed way. Official evidences are still growing in the moment this chapter is written. From 2014 to 2015 composed cognitive maps were applied in 11 of the most conflictive territories in Ecuador: inside two police sub-circuits in the North of Quito, two in the South of Quito, two in Quito’s downtown surroundings, two in South of Guayaquil, and other three areas along the Colombian-Ecuadorian Frontier. From 2009 to 2016 Ecuador was continuously reducing all its common crimes, mostly homicides (Pontón Cevallos et al., 2020), so the National Police and the Ministry of Interior wanted to know which were the relevant variables of this process. Composed cognitive maps, linked to other geo-referential and statistical tools, indicated that the most relevant variables in those 8 years were the consolidation of police proximity in small places, and the strategies for integrating citizenship into the proximity policies, such as situation-oriented strategies (Castro Aniyar et al., 2015). Also allowed to set spatial dynamics of crime in all the country, useful to larger crime policies. During this experience, composed cognitive maps also allowed to stablish the esteemed amount and the clandestine routes for domestic drug-dealing to Quito and Ambato cities, some important economic interplays at the Colombian border, and the prediction, three years before, of a terrorist attack against the main Police headquarter in San Lorenzo city (Castro Aniyar, 2019a). In attention to the possibility that composed cognitive maps only could define categories in residential neighborhoods, Professor Sonia Barcia established the relevance of its results in a different macro873

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Figure 1. Composed cognitive map (without the field journal) made by a victim in Toctiuco, Quito

economic context, a touristic site with a non-permanent population of victims or offenders. The Espacio Abierto high impact journal can be consulted with these results (Barcia et al. 2018). Afterwards, the Política Criminal high impact Journal published a final research on how composed cognitive maps made precise diagnosis that allowed the National Police to decrease common and sexual crimes, as the insecurity feelings, in an important touristic beach at the Ecuadorian coast (Castro Aniyar et al. 2020). The National Police, a researcher from the Ecuadorian Army and a local University researcher published a scientific article showing that composed cognitive maps were much more reliable to prevention 874

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and intelligence than epidemic maps, and other complex quantitative tools normally used in the region (Castro Aniyar et al. 2019). According to the fact that proximity police strategies were so useful to systematically reduce crime, and to the fact that composed cognitive maps were applied to accelerate situational diagnoses in high conflictive territories, it is possible to esteem that part of the crime decreasing, mostly homicides decreasing, between 2015 and 2020, are related to National Police situational action in that period. David System and OAID/DAID/DNAI of the National Police of Ecuador promoted and applied crime analysis with composed cognitive maps to strengthen the situational approach. But, in this sense, situational and proximity policing has been applying with little scientific feedback, and the whole process lacked of measurement strategies. So, National Police and some Majors decided to measure the impact of composed cognitive maps in specific territories in 2021, by before/after proceedings (Programa Aguas Turbulentas 2020) as it successfully happened in 2018-2019 in Murciélago Beach (Castro Aniyar et al. 2020), in Leonidas Proaño/La Fabril neighborhoods and in a Manta’s downtown area where prostitution became a concern. The results, once again, showed that the method allowed new kind of policies related to effective crime decreasing, with a little use of force or arrests. The results should be published between 2021 and 2022. In conclusion, the integrated efforts of the National Police, Sistema David, OAID/DAID/DNAI and the university researchers showed that the method represents important advantage to crime analysis, therefore, to crime reduction and intelligence

CONCLUSION: HOW THIS MODEL CAN BE EFFECTIVE TRANSNATIONALLY? As Van Dijk once said, while promoting victimization surveys, “I have never understood how it was possible to ignore the people who are exposed to crime” (The Stockholm Criminology Symposium 2012). Composed cognitive maps show, mainly, how important is to construct the situation angle of the victim and the witness of a crime, in order to achieve better comparability and efficiency as a whole. The need of integrating the situation angle was one of the International Crime Analysis VII Seminar conclusions in Quito (Dammert 2018). For doing so, what the maps innovated is to take advantage of its quantitative and qualitative nature to upload hyperthetic information about the social relations of crime since its territorial dynamics. In this way, the characteristic isolation of ethnographic studies into public policies (which generally lack of global, systemic or structural categories to be projected with) is avoided. The qualitative look of crime is integrated into one model, helping to explain the importance of proximity and situation in criminology of place, and the use of more accurate crime reduction policies. But it is important to clarify some specificness of the Ecuadorian context. The maps (“los mapas”, the way researchers call them) profited of a somehow optimal context because communitarian police strongly deconcentrated into the territory in the same moment that composed cognitive maps were applied. The geographic map of the Ecuadorian police service radically transformed from 2011 to 2012, dividing the 140 traditional administrative districts into 1134 circuits --about 5 km2 and 50,000 hab. approx. each one--, and, just for policing purposes, dividing those circuits into 1880 sub-circuits --about 1 km2, from 5,000 to 10,000 hab. approx. each one. The new territorial units served as operating units of 1829 headquarters of the Communitarian National Police to almost 15 and a half million people in 2012 (Policía Nacional Del Ecuador 2012; Cencepol 2014; INEC 2021). 875

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From 2009 to 2016, deconcentration showed a positive effect of proximity police on crime decrease among robbery and homicides rates. This decrease is visibly more pronounced and more consistent when the new deconcentration model was fully implemented, but also in the not fully implemented moment. The fully implemented model implied the expansion of the new headquarters, number of agents increasing (also better salaries) and the deepening of the transversal programs that began in 2012. Transversal programs placed crime policies in a situational context, profiting community organization and shared information. During the fully implemented model, the crime curves clearly precipitated from 2012 to 2014 (Castro Aniyar 2021). The homicide rate in Ecuador is a prove of the two moments of the deconcentration strategy effect, from 2009 to 2011, and from 2012 to 2015: Figure 2. Homicide rate in Ecuador (1980-2015) with descending curve since proximity and situation policies were implemented in 2009

(own, from DAID, 2016)

But even when the Ecuadorian New Management Police Model (NMGP in Spanish), the very frame where composed cognitive maps began to develop, ceased in 2017, composed cognitive maps could be measured in relation to specific territories, and showed an effect on crime decreasing in 2018, 2019, and 2020, for the moment, and according to the already published literature. The NMGP helped crime policies effectiveness producing deterrence proximity in small territories. This is an important factor for composed cognitive maps success or association to success. It implied coordination of strategies and information with policemen, and police units in small territories to the reduction of crime goal.

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Thanks to NMGP, composed cognitive maps helped the police, or any other agent, to develop professional criminometric techniques on territory as crime analysis computerization, geo referentiality and strategy design. In other words, even if police crime policies assumed the community participation, it had the possibility to use a scientific approach, and not habitual political leaderships and narratives. Because of this, the Ecuadorian crime reduction experience (Pontón Cevallos, et al., 2020), also depended on professionalization or the acquired social abilities to measure and acting inside the community context from 2012 to 2017. And this is good context, even if not measured, for composed cognitive maps. From this description it I possible to conclude that composed cognitive maps are not a model thought to the less developed countries, but to all crime situations. They could be more performing in a proximity and situation-oriented criminal polices context, but does not need to depend on it. It is possible to think that they have limits in countries where macro economical contexts instrumentalize crime, because these conjunctures produce structural opportunities to crime, and can make chaos convenient, like in Libya, Venezuela or Syria. These dynamics should scape from the small territory approach advantages, because they should end up to be insufficient. But, in general, the maps can be applied in any context where common crimes menace the life of people in public spaces. However, policymakers, should understand that the proximity model of police, oriented to give the policemen or other agents, an active role in the situational and crime opportunity context, can be a positive contextual trigger to the composed cognitive maps effectiveness. This method also drives to re-understand proximity policies through questions like: How is it possible to materially, objectively, and empirically understand what proximity means? Which is the role of the situation level, between strategies, policemen and people? The method effectiveness should deepen this debate. The Ecuadorian experience can fit, even unconsciously or unintentionally, into an imagined major system of crime justice, according to classic literature: the civil/mediation State model variant (DelmasMarty,1992, pp 137-151). From this perspective, in the studied period, Ecuadorian citizens provided intelligence data insights to professional Police and institutions, because they were somehow in contact and listening each other. But, in this case, people are not just informants, because the listening put in situational context the dynamics of crime. Composed cognitive maps were designed to describe crime situations restoring the place of thick description in small territories and situations, exactly there, where larger concepts acquire actuality. In this sense, crime policies relatively success is the result of objectively describing and understanding crime situation from inside the territories.

ACKNOWLEDGMENT This research was supported by the Senescyt.

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ADDITIONAL READING Castro-Aniyar, D. (2016). El lugar y la emoción: La condición social a través de la cultura y su territorio. Editorial Académica Española. Erostarbe, I. I. (2000). Memoria de testigos: Recuerdo de acciones e información descriptiva de un suceso. Revista Psicothema, 12(4), 574–578.

KEY TERMS AND DEFINITIONS Cognitive Maps: Also called cognitive mapping, are space sketches made from the imagination and memory of an informant. They are one of the most useful tools in subjective geography. Conjuncture: Can be understood as the next level to situation. When greater political issues determine social relations, interpretable patterns can be defined as a crossroad of steady dynamics and other political forces. This social relation level can be called conjuncture. Dynamic: While situational exchanges produce larger patterns, individuals, groups, or communities respond to steady practices in time. These practices can be called dynamics. Method: Between the technique and the epistemology, a method is a system of techniques and strategies scientifically and rigorously designed in order to answer a research or dissertational problem. Otherness: The cognitive construction of an informant placed in the role of I/Us in relation to the they/others. It is a useful category in anthropology to define identity. Situation: The level social relations where exchanges are produced by individuals, groups, or communities. System: Even if all kind of exchanges constitute itself a system, social sciences use to call like this a much larger exchange of patterns which define and determine the interpretable notion of nation, society, or civilization. This concept must be differentiated to the idea of structure, much steadier than the idea of system, and related to the interpretable notion of society from a historical, anthropological and/or macro-economical point of view.

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ENDNOTE 1



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It is important to say that this type of cognitive maps has nothing to do with pedagogic cognitive maps technique which are easy to find in the web.

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Chapter 48

Gender and Sexual Minority Research in the Digital Society Salvatore Monaco https://orcid.org/0000-0002-4218-6267 Free University of Bozen, Italy

ABSTRACT The chapter aims to deepen the theme of the participation of gender and sexual minorities in social research, with a specific focus on the new possibilities offered by the digital society. After defining the concept of “hidden populations” or “hard -to -reach populations,” the contribution focuses on the factors of greatest vulnerability of the LGBT population. Subsequently, the chapter aims to review the literature regarding the barriers to the sampling, recruitment, participation, and involvement of sexual and gender minorities, highlighting some strategies to overcome some of the main barriers, through a plurality of innovative procedures made possible by the so-called digital society.

INTRODUCTION Although making significant contributions to empirical and theoretical understanding of sexuality in society, qualitative, quantitative and mixed research methods often encounter problems in the stage of subject recruitment and sampling. This is particularly evident in social studies and researches involving subjects belonging to gender and sexual minorities. In fact, those who do not comply with “the heterosexual norm” or who shift away from the traditional gender binary belong to a class of individuals who are still the victims to hate crimes, prejudices and stereotypes (Savin-Williams, 2001; Ludlam et al., 2015; Nunn & Bolt, 2015). Thus, gay men, lesbian women and bisexual and transgender people often tend not to reveal themselves; in reality, they choose to hide or keep their true sexual identity as a secret, or even intend to escape from social research. This “masked” character makes the LGBT community invisible from a statistical point of view in many countries.

DOI: 10.4018/978-1-7998-8473-6.ch048

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 Gender and Sexual Minority Research in the Digital Society

From this critical angle, it is safe to argue that subjects belonging to sexual and gender minorities can be categorized into the sub-group sociologically termed as “hard-to-reach population” or “hidden population”. In the context of quantitative research, failure to obtain accurate research data capable of reflecting the size of the entire sub-population in question may result in several drawbacks, including threats to the external validity of the results and the possibility of generalization (Jonhson, 1990; Rogers, 2004; Meyer & Wilson, 2009). Similarly, also qualitative research sometimes requires meeting and interaction in person with this hidden population, but its members often escape or refuse. Social researchers continue to struggle to find and involve gay, lesbian, bisexual and transgender participants in their research, even if they have to renounce the use of probabilistic sampling or to restrict the field of their analysis only to the most reachable people. Traditionally, to deal with this situation, social researchers implement several strategies or sometimes a combination of them. For example, to recruit subjects belonging to “hidden populations” they resort to sampling of convenience, conducting recruitment and interviews in gathering places usually visited by the target population. Whilst this strategy facilitates the involvement of some members in the study, at the same time, it also omits those who are not present at the identified social venues and those who visit different venues in the period during which the research takes place (El-Khorazaty et al., 2007; Festinger et al., 2008; Corbisiero, 2010; Germino et al., 2011). A second strategy that social researchers often deploy is appeal to professionals who have direct contact with members of target populations for their assistance in the mediation between the researchers and the potential subjects (Booth, 1999; Derose et al., 2000; Hatchett, 2000; Benoit et al., 2005; Keyzer et al., 2005; Alvarez et al., 2006; Hoppitt et al., 2013). In this scenario, they could be NGOs and some counseling services, which, depending on the case, often play a central role in welcoming, supporting and listening to some vulnerable sub-groups of subjects. Consequently, they are able to produce fairly profiled lists of individuals” instead of “consequently, producing fairly profiled lists of individuals. A last solution sometimes adopted is snowball sampling. In this method, researchers come into contact with some target subjects and, after their involvement in the research, ask their help to identify and invite from their social circle more right potential subjects who share the same characteristics, thus making the number of the subjects taking part to the research grow “like a snowball” (Shedlin et al., 2011). The rise of the digital society and the increasingly pervasive diffusion of online communication channels is making it possible to partially overcome the existent barriers to the sampling, recruitment, participation, and involvement of sexual and gender minorities in social research. Thus, this chapter intends to deepen the theme of the LGBT population in social research, with a specific focus on the new possibilities offered by the digital society. After defining the concept of “hidden populations” or “hard- to- reach populations”, the contribution reviews some of the most innovative studies in this field, highlighting some strategies to overcome some of the main obstacles to reach this target. As it will be explained more in detail, a growing number of studies have successfully recruited hard –to- reach populations via social media, through a plurality of innovative recruitment procedures.

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BACKGROUND The concepts of “hidden populations” or “hard-to-reach populations” are very widespread in the scientific and sociological literature (Sydor, 2013). They refer to all the social groups made up of people who tend not to expose themselves and choose to keep their identity hidden. This is the defining features of social minorities, sub-groups who are subject to hate crimes, prejudice and stereotypes for one or more of their characteristics or for some of their behaviors. For example, in this category there are ethnic groups, lower socio-economic classes and, more generally, all socially disadvantaged groups (Lambert & Wiebel, 1990). Additionally, the identity of sexual and gender minorities is sometimes experienced as a “social stressor”. Thus, it is safe to argue that also the LGBT populations hope for concealing their sexual identity, which could be accounted for subcultural, intersectional and behavior-related reasons. For example, a part of the LGBT community which, even if they make public their sexual identity, can belong to other vulnerable categories (linked, for example, with their ethnicity, age or mental health condition) that prevent them from exposure of sexual identity (Meezan & Rauch, 2005; Sullivan & Losberg, 2003). Evidently, under these circumstances, the invisible nature of this hidden population turns into a stumbling block for researchers. Searching for an appropriate number of right subjects intended for the conduction of meaningful or representative social studies represents an arduous task for them (Sateren et al., 2002). Objectively, they lack a list of potential subjects belonging to the target population from which they can invite in a probabilistic sampling, but they sometimes also have to deal with strong resistance. In fact, those who think they belong to a sexual minority group or they are socially disadvantaged tend to live with discomfort. For this reason they avoid revealing themselves (to the general public). When it is possible, they “wear a mask”, pretending to be someone else, with a socially acceptable identity. Similarly, sometimes the socially disadvantaged people may feel suspicious of researchers, because of their failure to recognize the value of the scientific research or their fear of potential everyday inconvenience caused by their sexual identity exposed in the research (for example, stigmatization, mistreatment or exploitation of various forms). Even though some members of hidden populations recognize the value of social research, they may refuse to take part in studies regarding their own social group because they are afraid of being publicly exposed. This is particularly true with cases where the subjects are involved in conducts considered illegal or immoral. As the socio-cultural and territorial research on this topic underlines, discrimination linked with sexual identity is often handled by anti-discrimination legislation. However not only, not all countries declare homophobia, biphobia and transphobia as a crime, but also, in some contexts, even when the safeguards are present, the protection effort is often far from sufficient or well-defined . In addition, homosexuality, bisexuality and transgenderism are still often socially condemned. Even in some contexts where gender and sexual minorities are statutorily protected, homosexual, bisexual and transgender people cannot live a carefree life with their sexual identity due to the hostility derived from cultural and social context. In these territories, it often happens that people tend to conceal their sexual orientation or gender identity due to the fear of “emotional abuse” or “cold violence” in the family, workplace, and other areas of social life (Williams Institute, 2019). Another specific peculiarity that somehow derails the path of social research on gender and sexual minorities to success is represented by the presence of gender fluid people. Research on this issue sug887

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gests that this is especially true for young adults, who keep questioning about their sexual orientation and gender identity (Taylor & Keeter, 2010; Risman, 2018; Corbisiero & Monaco, 2021). Empirically, this sub-group of people feel that they belong only at certain moments of their life to a sexual and gender minority or that they are not really part of it, since they experience their sexual identity in the course of continuous experimentation. Similarly, some individuals belonging to sexual minorities reject prevailing categories of sexual identity (including LGBT) and therefore decide not to contribute to scientific research on the basis of a political position (Parks & Werkmeister-Rozas, 2006). Finally, it is safe to point out that the coming-out paths are often slow or difficult. In this sense, not all people feel equally comfortable coming out to people whom they are not familiar with, such as social researchers (Walby, 2010). In this complex and uncertain scenario, the diffusion of the Internet-based communication tools has contributed to the partial solution of some aforementioned problems .

SOME DIGITAL SOLUTIONS As claimed by Cabiria (2008), in the cyber space, the “Netizens” who belong to social minorities and share the same characteristics have over time the opportunity to build safe spaces in which they can virtually meet and tell their experiences without feeling the pressure coming from social and cultural oppression and discrimination. This process involves several marginalized categories, including the LGBT community (Döring, 2009). In other words, in the digital society, in which Internet access has become increasingly widespread, most people who belong to sexual and gender minorities take part in groups and online spaces in which they have social relationships with other individuals with whom they share sexual identity (Collister, 2014; Nardi, 2015). The birth and diffusion of these spaces certainly represents a possible resource for social researchers, They can avail themselves of the new opportunities offered by the web to expand the number of subjects to be included in their studies. In order to make more reader-friendly the use of new possible digital solutions in the involvement of the LGBT population in social research, the following pages are dedicated to the elaboration of some of the main studies in this respect. As it has been anticipated, one of the main difficulties social researchers encounter is recruiting a large number of appropriate subjects in the research. Even if the web does not allow researchers to build statistically representative samples, they can come into interaction with a larger number of people online than in the real life. Furthermore, researchers can attempt to communicate with different people online, segmenting the desired sample based on other socio-demographic variables in addition to their sexual identity (for example, area of residence, ​​ age or socio-economic status). An emerging strategy for recruiting the LGBT subjects in social research is the application of social networking platforms (such as Facebook, Instagram, and Twitter) (Ramo & Prochaska, 2012; Ramo et al., 2014; Loxtonet al., 2015; Rait, Prochaska & Rubinstein, 2015). In fact, social media platforms nowadays represent a real digitalized global village which hosts most of the world population. This kind of strategy comes in two forms. One possibility is that researchers use the web to inform users of their scientific initiatives, creating personalized communications to spread on the main channels or groups frequented by LGBT people. People interested in participating in the study or in obtaining more information about it can then contact the researchers in private. A second possible strategy consists in one-on-one contact with target users when the social network offers the possibility to send direct messages. 888

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Regarding the first strategy, it is important to point out that social media advertisements are particularly useful when research is aimed at studying specific LGBT sub-populations that are particularly difficult to reach. In this sense, a widespread targeted communication can allow researchers to reach the target people directly and attract their attention. In this aspect, a review of studies conducted in the early 2000s to recruit vulnerable populations (UyBico, Pavel & Gross, 2007) highlighted that the sponsorship of research through advertisements and mailing lists has proved to be very effective in many cases. On several occasions, viewing the advertisement has encouraged different subjects to take part in the social research, allowing to reach very high numbers of participants, especially in clinical trials. However, the authors stressed that the highest response rates were recorded especially when clear and direct communication messages were spread, overcoming linguistic and cultural barriers. In this sense, it is important to emphasize that in the case of sexual and gender minorities using inclusive and non-discriminatory language is essential to fully respect all identities and also to show empathy (Corbisiero, Maturi & Ruspini, 2016; Marotta & Monaco, 2016). As for the second strategy, the web hosts numerous online social groups with full or limited access in which subjects share one or more of their personal characteristics. Researchers who want to exploit these virtual spaces can request access, not only releasing messages on the bulletin boards presenting themselves and their study, asking target people to take part in the research, but also writing private messages to people they intend to involve in the study. This type of approach has been successfully used in the United States in research focusing on adult gay men (Hernandez-Romieu et al., 2014; Wilkerson et al., 2016), gay and bisexual youth (Prescott et al., 2008), and HIV-positive LGBT adults (Thériaultet al., 2012; Yuan et al., 2014). In a more or less similar way, the researchers searched online for groups and pages frequented by the target and, once the enrollment is done, they presented their study and specific research objectives, asking people to contribute to the study. Recently a group of American scholars (Guillory et al., 2008) has conducted a quantitative research aimed at evaluating the effectiveness of a public intervention education campaign of alcohol drinking for young LGBT people. In this specific research, the working group decided to adopt the methodologies of both traditional and online sampling, with the further aim of comparing the data collected from different sampling methods. With regards to the online sampling, researchers used different communication channels (online social media, specific websites for LGBT, online radio and online TV) and distributed the online questionnaire to the subjects who requested it. On the contrary, for traditional sampling, they recruited participants during LGBT festivals and initiatives at their main gathering places, such as bars and discos, and asked them to answer structured questions face to face. The comparison between the two groups of respondents showed differences in terms of efficiency: online recruitment resulted in a greater number of responses in less time across a more heterogeneous sample. However, the participants who took part in the traditional face-to-face survey were more cautious in filling out the questionnaire and the rate of questionnaires left incomplete was lower. In time of COVID-19, the research group of the Free University of Bozen engaged in the national interest research project COPING (“Constructions Of Parenting on INsecure Grounds. What role for social work?”) recruited 50 LGBT+ parents online to study through a series of qualitative interviews on how parents belonging to sexual and gender minorities are responsible for caring for and bringing up children, which represents parenthood and their challenges in the contemporary society (Monaco & Nothdurfter, 2021). Since the study is based on the theoretical-methodological approach of the grounded theory, the researchers exploited the potential of the Internet in proceeding with the theoretical sampling. As the 889

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research progressed, they recruited LGBT+ parents with different profiles, in accordance with their research needs, through personalized communications disseminated through the main communication channels, but also with the help of associations of category and, when possible, through direct contacts with parents who make public their profile on social networks. At a later stage, the researchers also used the same recruitment strategies to involve social workers who have had direct experience with LGBT+ parents in their qualitative research. To create a participatory research, researchers decided to involve the interviewees also in the phase following the formal data collection, discussing with them the first results from the analysis of the interviews through some online focus groups. The literature on the subject (Matthews & Cramer, 2008) has for some time already highlighted that the use of video calls through webcams and microphones contributes to a valid social research, without losing valuable information from the setting, especially in the context of qualitative research. The Internet can also be considered as an alternative territory for conducting alternative forms of netnographic research, also called online ethnography or virtual ethnography (Hine, 2000). It is an ethnographic research that validates online virtual spaces as a field of observation (Kozinets, 2009) and which involves the observation of the participants and, at times, also the interaction with the members of the community (McKenna, Myers & Newman, 2017; Kozinets, 2015). A case in point is McKenna and Chughtai’s study (2020) which studied the behavior of LGBT members within the online role-playing platform WoW, from January, 2010 to November, 2012. While WoW is essentially a mission game for killing monsters and fighting enemies, it is also used a lot by politically committed people. They meet online to play, but also to communicate horizontally online with people who share the same ideologies. The LGBT movement on WoW was founded in October, 2006 with the aim to provide a safe and inclusive place accessible by members of any sexual orientation or gender identity. The WoW LGBT movement has also triggered the birth of a specific website equipped with discussion forums. Through a participatory observation activity, the researchers noted the importance of online space in the combat against the oppression of heteronormative society. In other words, this nethnographic research has shown that WoW is more than a game environment, but it is a virtual tool that facilitates an escape from the social stigma for the LGBT community. At the same time, this research has highlighted that social movement activists, regardless of their motivations, are exploring new ways to express themselves or raise their concerns locally or globally through the use of modern technology. Another study, which combined netnographic research approach with online recruitment for quantitative insight, was conducted in Italy by Monaco in 2018. The author analyzed the profile of some users of GRINDR, that is a gay dating app, to detect the main relational dynamics within the Italian gay community. At a later stage, he individually contacted some of the users and ask them to fill out a questionnaire based on online virtual exploration.

FUTURE RESEARCH DIRECTIONS The previously cited examples allow to argue that online recruitment strategies can be a possible solution to the problem of insufficient engagement of LGBT individuals in social research. However, to make the maximum utilization of these opportunities, there are two points worthy of notice: firstly, researchers must possess the specific skills and knowledge to navigate easily on the net, 890

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implementing the most suitable strategies to optimize their results. In other words, researchers must be equipped with the necessary skills to seize this opportunity and must behave in full compliance with the netiquette that these virtual spaces require. In fact, it is important to underline that these virtual communities are born with the objectives of socialization and comparison amongst people who have little to do with social research. This is a fundamental point that social researchers need to keep in mind before taking any action. Secondly, they must be even more attentive to the communication strategies, since online research requires the researcher to communicate in a more immediate and direct manner. Furthermore, given that personal interaction in many cases is absent or limited, researchers must excel at instilling a sense of trust in potential participants, adopting all possible precautions (for example, they should make personal contacts available, so that people interested in the study can use them to request further information or have some clarifications). This solution also serves to mitigate the distrust that subjects sometimes have towards social research. Among the advantages of online auto-compilation we find the convenience regarding the places or times of data collection and the reduction of compilation errors. However, even though researchers in many cases manage to reach a higher number of people to involve in studies, they need to pay more attention to the quality of the data they collect. Furthermore, especially in research involving self-filling in questionnaires, researchers must verify that the answers are reliable. To this end, not only statistical tests of significance can be carried out in the analysis phase, but researchers should implement strategies in the construction phase of the survey tools. For example, one of the strategies to detect if the interviewee answers in an honest way is to ask sensitive questions along with general questions whose response rates are already known. Knowing the proportion of the general question, the analysts can then confront these answers and, verify if the responses to sensitive questions are honest, and, consequently, if the survey produces accurate results. Furthermore, to make the research more accessible, the researchers could consider using simultaneous translation services when doing the questionnaire surveys, in order to involve people who do not speak the local language in the research. Another strategy to increase the number of participants could be to provide rewards or incentives for the participants in the study. In this case, researchers must first verify whether and to what extent resorting to incentives does not affect the ethics of the study (Popay, 2008). Also, regarding online data analysis, researchers must take into consideration that people do not always write on the Internet what they really think or would do in everyday life. This discrepancy may come from the fact that they feel more protected sitting in front of the screen than being in front of a researcher. Finally, it is important to specify that online recruiting strategies inevitably exclude those who lack Internet connection or who are “Internet illiterates”. For this reason, a combination of traditional and digital methods is always recommended. In this aspect, some studies have shown that research participants needed guidance from researchers, particularly for the correct compilation of online questionnaires (Elam & Fenton, 2003; Chang et al., 2004; Flory & Ezekiel, 2004).

CONCLUSION For some time now, the literature on how to develop a solid methodological framework has highlighted a growing demand for methodological innovation, especially when social research focuses on vulnerable 891

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or hard-to-reach populations (Taylor & Coffey, 2008). Thus, in contemporary society, an understanding of the strengths of different online recruitment methods could be useful to globally increase future knowledge in the social research on sexual identities and personal relationships. Starting from these considerations, it is safe to argue that the strategies that researchers decide to implement have to be designed and adapted according to the research methodology, the type of research they intend to conduct and the characteristics of target audience. In some cases, for example, conducting targeted communication campaigns is more appropriate than contacting people directly by resorting to their public personal profiles on social networks. This is especially true when the subject of the study concerns a sensitive topic. In other examples, however, it is important not only to focus on the construction of a quantitatively significant sample, but also to variegate the internal structure, so that the various groups are represented in the overall study sample. In this case, researchers should envision building a non-probabilistic sampling plan by quotes. Indeed, it is important to ensure that results can be split within LGBT respondents, since experiences and profiles are different. Even though LGBT people belong to sexual and gender minorities, the experience of a gay man may not be the same as that of a lesbian woman or a transgender, bisexual person. Likewise, it is increasingly necessary to pay attention to multidimensionality of situations, considering also generational, ethnic or socio-economic differences, conducting data analysis also in an intersectional perspective. Furthermore, given that the LGBT population is very vulnerable, each research could represent a starting point but not an end in itself. In other words, researchers who intend to carry out other studies on one or multiple segments of the LGBT community should learn to develop relationships with the participants they involve, activating forms of exchange and sharing of results even once the study has ended. This could incentivize the participation of subjects in subsequent follow-up studies. Technology-enhanced research projects also suffer from a number of ethical issues. In particular, researchers must pay particular attention to respecting the regulations regarding privacy and data protection; they must instill in the participants a sense of security, motivation and trust. Similarly, in the case of netnographic studies, researchers may use data that are present on the Internet for other purposes than the research they are conducting. This is a recently hotly-debated point within the scientific community (Lombi, 2015). Furthermore, researchers must keep in mind that, despite the widespread diffusion of the Internet, there may still be problems related to the use of digital technologies by people with disabilities, digital illiterate, and people living in geographically isolated areas with underdevelopment of Internet. In conclusion, the positive aspect is that researchers begin to divert their attention towards innovation, considering in a conscious way what are the possible practices that must be adopted in the field of empirical research, but also the critical aspect that can affect their researches, to make way for a more digital, inclusive society.

FUNDING This research received no specific grant from any funding agency in the public, commercial, or not-forprofit sectors.

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ACKNOWLEDGMENT Part of the information applied in this article originates from the project ‘COPING. Constructions of parenting on insecure grounds’ funded by the Italian Ministry of Education, University and Research. I am grateful to Prof. Urban Nothdurfter for his valuable support. I am also greatly indebted to the staffs of Osservatorio LGBT attached to the University of Naples Federico II, under the coordination of Prof. Fabio Corbisiero, and to Guido Guarino and Steven Ding who have patiently read the paper.

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Taylor, C., & Coffey, A. (2008). Innovation in qualitative research methods: Possibilities and challenges. Cardiff University School of Social Sciences. Taylor, P., & Keeter, S. (Eds.). (2010). Millennials: A Portrait of Generation Next. Confident, Connected, Open to Change. Pew Research Center. Thériault, N., Bi, P., Hiller, J. E., & Nor, M. (2012). Use of web 2.0 to recruit Australian gay men to an online HIV/AIDS survey. Journal of Medical Internet Research, 14(6), e149. doi:10.2196/jmir.1819 PMID:23128646 UyBico, S. J., Pavel, S., & Gross, C. P. (2007). Recruiting vulnerable populations into research: A systematic review of recruitment interventions. Journal of General Internal Medicine, 22(6), 852–863. doi:10.100711606-007-0126-3 PMID:17375358 Walby, K. (2010). Interviews as encounters: Issues of sexuality and reflexivity when men interview men about commercial same-sex relations. Qualitative Research, 10(6), 639–657. doi:10.1177/1468794110380525 Wilkerson, J. M., Patankar, P., Rawat, S. M., Simon, R. B., Shukla, K. M., Rhoton, J., & ... . (2016). Recruitment strategies of Indian men who have sex with men in the State of Maharashtra into an online survey. International Journal of Sexual Health, 28(3), 221–227. doi:10.1080/19317611.2016.119307 9 PMID:27668029 Williams Institute. (2019). Social Acceptance of LGBT People in 174 Countries, 1981 to 2017. Williams Institute. Yuan, P., Bare, M. G., Johnson, M. O., & Saberi, P. (2014). Using online social media for recruitment of human immunodeficiency virus-positive participants: A cross-sectional survey. Journal of Medical Internet Research, 16(5), e117. doi:10.2196/jmir.3229 PMID:24784982

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Chapter 49

Secondary Analysis for Digitized Data:

Its Utility in Finding and Selecting Indicators of Social Well-Being Gennaro Iorio University of Salerno, Italy Marco Palmieri Sapienza University of Rome, Italy Geraldina Roberti University of L’Aquila, Italy

ABSTRACT Secondary analysis for quantitative data is a social research method traditionally employed for statistical analysis of administrative data. In the new digital society, this old research method that pre-existed the emergence of the new digital environment has been digitized to carry out its valuable activity in doing science. In this chapter, the secondary analysis for digitized data is illustrated. Thanks to the growing availability of datasets digitized on the web, the scholars of social well-being use the secondary analysis to inquiry this phenomenon through a cross-national perspective. The authors present the empirical study of World Love Index, in which the utility of the secondary analysis in finding and selecting valid indicators of social well-being is experienced.

INTRODUCTION1 In the 1990s the scholars of communication studies considered Internet to be virtual reality, something different from the real world, in which the people could take refuge to redefine own identity, community and corporeity; this idea is related to the obsolete notion of web, as theater of fake identities and deviant behaviors (Jurgenson, 2012). Recently, the idea of virtual cyberspace, according to which the internet DOI: 10.4018/978-1-7998-8473-6.ch049

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exists as a separate place, detached from the reality, has been abandoned. There is no longer distinction between the real and the virtual world. The online actions performed by people have offline consequences and vice versa; nowadays, these two aspects intersect each other in everyday life (Rogers, 2009). To study social phenomena in a society in which the digital character is perceived as an essential dimension that constitutes it from within, new research methods have been designed and employed. Methodological research programs produced, and are still producing, ad hoc research methods that can be classified into two groups: the ‘digital methods’ and the ‘digitized methods’. The digital methods are born in the digital world and therefore they are digital native (e.g., sentiment analysis); digital methods are fitted for analyzing digital data, created within the digital environment (e.g., posts on Facebook or on Twitter). Digital data are produced by spontaneous activity of people in the digital setting; digital behaviors leave digital traces that are stored and can be analyzed. Digital data are persistent and easily searchable (Natale and Airoldi, 2017). The digitized methods, on the contrary, are traditional social research methods, imported into the digital era to which they pre-existed (e.g., the web survey); the digitized methods are fitted for analyzing digitized data, created offline, and then imported and stored online (e.g., survey responses). Digitized data are produced by digitalization process, which led to the transition from paper and pencil data to digitized data; previously recorded on paper, data have been progressively digitized, increasing the availability of digitized empirical documentation that can be analyzed by software on computer (Rogers, 2015; Natale and Airoldi, 2017). In this chapter, the digitized methods of social research are accounted; methods born in the pre-digital age and then adapted to the new digital environment. In particular, the secondary data analysis method is illustrated, a traditional research method applied to digitized data. This digitized method is selected because it is considered very useful in finding valid indicators of conceptual dimensions that are so general as not to suggest direct forms of empirical operationalization, such as the concept of social well-being. The authors of the chapter present the World Love Index, an index based on the secondary data analyses. The World Love Index is the result of the empirical operationalization of agape (Boltanski, 1990) as Social Love concept, closely related to the concept of social well-being (Nussbaum, 2013), as an important factor of cohesiveness in contemporary society as well as a key element in promoting solidarity and recognition in pluralist societies (Iorio, 2014). This paper describes the procedure applied by the research group in finding and selecting valid indicators of Social Love conceptual dimensions; the World Love Index is a valid example of how to design a transnational index from secondary sources, using digitized data.

SECONDARY ANALYSIS FOR DIGITIZED DATA “Secondary data analysis concerns the analysis of previously collected, available and systematically organized data, having individual or aggregate unit of analysis, coming from one or more statistical sources, with the aim of answering a defined research question regardless of the purposes for which the data are originally collected” (Biolcati-Rinaldi and Vezzoni, 2012, p. 16). The peculiarity of this research method is analyzing already existing data which have previously been collected by an/other researcher/s (Hyman, 1972; Corbetta, 2003). This brings many advantages in doing social research: “savings in relation to resources, in terms of time, money and personnel. To begin with, using data collected by someone else means that the data is available relatively quickly. The 899

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researcher does not have to go through the long and costly processes of obtaining funding, designing, and implementing their own survey, or paying for a sampling frame, conducting fieldwork, data preparation and data cleaning. The main cost in undertaking secondary analysis is that of finding the data” (Devine 2003, p. 285). Many data sources are available for secondary analysis, traditionally connected to the statistical analysis of administrative data; in the new digital era, secondary analysis concerns other types of data sources, useful for conducting the most varied analysis. The great development in the field of information and computer technology has favored the use of secondary analyses, simplifying the storage and the search of digitized data. A wide variety of data sources exists, but a detailed classification is unrealistic. Here, the typology elaborated by Hakim (1982) and re-elaborated by Biolcati-Rinaldi and Vezzoni (2012) is presented (Table 1). Table 1. Typology of secondary data sources Cross-sectional

Longitudinal

Non institutional

Data sources from cross-sectional and noninstitutional studies

Data sources from longitudinal and non-institutional studies

Institutional

Data sources from cross-sectional and institutional studies

Data sources from longitudinal and institutional studies

Source: (Biolcati-Rinaldi and Vezzoni, 2012)

This typology is made with two fundamenta divisionis. The first criterion refers to the difference between cross-sectional and longitudinal research designs. How many times data are collected within the same study? The cross-sectional study collects primary data only once in t1 and no more again; the longitudinal study collects primary data at t1 and it is replicated over time (t2, t3, and so on). The second criterion relates to institutional or non-institutional scope of the primary study. Does a specific legislation rule the data collection and the purposes of the study? The institutional study collects data respecting specific legislative frameworks; non-institutional studies are free of legislative limitations. The types of secondary data sources are the following. Data sources from cross-sectional and non-institutional studies. This type of data sources comes from isolated studies, conducted only once by research institutes or academic research groups, often outside a more general research line. It is a very heterogeneous data source both in terms of the topics covered and the methodological approaches employed. These studies do not take place within any legislative framework that clearly defines the scientific objectives and how the research must be conducted. Nevertheless, it would serve an important public function to share data and knowledge, on the condition that the primary researchers make their data available to the scientific community, as well as clearly documenting the research strategies adopted. But this practice is uncommon. Data sources from longitudinal and non-institutional studies. This type of data sources comes from studies designed by large networks of universities and research institutes. These studies are mostly international surveys conducted in different areas of the world, with longitudinal and comparative approaches, in which the questionnaire does not change, except slightly, from one wave to the next. These studies collect data of individual properties, related to lifestyles, values and attitudes of people; the research objective is to understand how these properties change in space and time. The datasets take shape from

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very complex research designs, where the covered topics are homogeneous over the time, as well as the methodological approaches used; nevertheless, data collection does not take place within any legislative framework. The produced datasets are usually shared with the rest of the scientific community for secondary analysis, to the extent that they constitute datasets for a global community. EVS (European Values Survey), ESS (European Social Survey), and WVS (World Values Survey) are well-known examples of longitudinal and non-institutional data sources. Data sources from longitudinal and institutional studies. This type includes data sources that are regulated by specific legislation and produced by research activities that are carried out by national statistical institutes or other public institutions, whose purpose is to provide the legislator with empirical evidence necessary to implement public policies. The legislator clearly defines the purpose of the study, which is financed by public research funds. These studies have a national reference, with longitudinal research design. One example is “Multi-purpose Survey System” carried out by Italian Statistical Institute, which includes longitudinal studies on multiple topics that concern the entire Italian population (daily life, leisure time, citizens’ safety, family consumption, etc.). This type of data sources also includes datasets produced by studies conducted by supranational institutions, like the European Commission, which entrusts European Statistical Institute with longitudinal studies, like the European Union Statistics on Income and Living Conditions, to inquiry the financial and living conditions of European citizens. In addition, this type of data sources is composed of longitudinal administrative data, related to data collected for administrative purposes over time (like the national census). Data sources from cross-sectional and institutional studies. This type of data sources is just logically possible but empirically not real. Cross sectional studies do not require specific laws to be implemented, even if they can be considered useful by the legislator for addressing programs of public policies. Secondary data analysis relies on data systematically organized in data matrix and ready to be analyzed. The most common data matrix in social research is cases and variables matrix, made up of bundles of row vectors, which refer to the cases of the study, and bundles of column vectors, which refer to the variables of the matrix. The intersection of row vectors with column vectors composes the cells of the matrix, in which the data are registered; the set of data systematically organized creates the dataset. Secondary data analysis is related to the availability of already existing datasets. The unit of analysis is either individual (e.g., in case of survey research) or aggregated (e.g., in case of ecological analysis). Secondary analysis is employed to work on variables coming from aggregated properties (data are collected at individual level and aggregated at territorial level) or among variables coming from global properties (data are collected at territorial level). In the simplest case, the secondary researcher analyses primary data collected by one previous research, but secondary data analysis can be performed on datasets made through the integration of several primary data sources, whose data are systematically organized in two or more datasets. This is a peculiar aspect of secondary analysis, to such an extent that the data integration is necessary to design complex research strategies, able to offer answers to research questions that would otherwise remain unanswered. Starting from this criterion, Biolcati and Vezzoni (2012) classify the types of research strategies can be implemented thanks to the secondary analysis (Table 2). Traditional secondary analysis. By adopting a single data source, the most traditional way of doing secondary research is implemented, using old data to answer new research questions. The researcher starts from a new research question, which stems from personal research interests, and searches for data sources already available and systematically organized in data matrix, in which to find variables that operationalize properties relevant to the secondary research. The traditional secondary analysis has theo901

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Table 2. Typology of secondary research strategies Secondary Research Strategies One data source

     • Traditional secondary analysis      • Replicated analysis      • Parallel analysis

Two or more data sources

     • Cumulative analysis                o Repeated analysis                o Comparative analysis                o Multilevel analysis

Source: (Biolcati-Rinaldi and Vezzoni, 2012)

retical purposes, with the aim of answering substantive sociological research questions, controlling the empirical solidity of already formalized theoretical propositions or creating new ones within a specific empirical domain. In addition, a very interesting application of this strategy is to clarify the definition of a sociological concept with a high level of generality, identifying the conceptual dimensions that compose it, the best available indicators, and thus creating a synthetic index that recomposes the semantic unity of the general concept, fragmented into the indicators that have made possible its operationalization. Replicated analysis. By adopting a single data source, the replicated analysis can be planned with the aim of replicating in toto the research design already implemented in the primary research. The research question and the collected data do not change from the primary to the secondary study. Thus, why should a replicated research design be carried out? Some scholars highlight the issue of replicability in social research: “The only way to understand and evaluate an empirical analysis fully is to know the exact process by which the data were generated, and the analysis produced” (King, 1995, p. 444). Therefore, a replicated research design has methodological purposes, to control the quality of the studies conducted by other members of the scientific community (Freese, 2007). Parallel analysis. Additionally, by adopting a single data source, it is possible to design the parallel analysis. The purpose is to inquiry the research question already investigated by another previous research – as well as the replicated analysis – but using a dataset other than those used in the primary research. Parallel analysis does not replicate the old research design in toto; it replicates the same research question, using a parallel dataset. In this case, the research design has the methodological purpose to control the reliability of the data collection instruments (e.g., the questionnaire and the questions administered) as well as the quality of the estimates produced by the primary research (Godwin, 2012). Cumulative analysis. By adopting two or more data sources, the cumulative research is designed to integrate several datasets into a single dataset. The researcher needs two or more old data sources to make one brand new dataset capable of answering new research questions. More data sources can be integrated for several reasons. Firstly, the secondary researcher needs to increase the number of cases; to achieve this goal, the rows of the primary data matrices are integrated into the rows of secondary data matrix. Or it may happen that the secondary researcher needs to increase the number of variables; to achieve this goal, the columns of the primary data matrices are integrated into the columns of secondary data matrix. Integrating the variables and the cases of the primary data matrices, the cumulative analysis increases the columns and the rows of secondary data matrix, offering itself as a rich empirical base for meeting the researcher’s cognitive needs. When two or more data sources are integrated into a single dataset, new research designs take shape: repeated analysis, comparative analysis. multilevel analysis.

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Repeated analysis. Repeated analysis is part of longitudinal analysis, in which the same research design implemented at t1 is replicated in toto at t2 and then at t3 and so on. In this case, the old research question, originating the study at t1, is repeated in subsequent waves (t2 and t3), to control whether the statistical distributions observed in t1 change in t2 and t3, and under which conditions these changes take shape. To make this possible, data are collected on the same variables (with the same operational definitions) over the time, and the sampling designs of the succeeding waves do not change or are considered equivalent. Repeated analysis investigates the longitudinal changes at aggregate level; panel analysis investigates the longitudinal changes at individual level (Dunkan and Kalton, 1987). Comparative analysis. One of the main advantages of secondary analysis is to explore the same research question in different spatial contexts. This opportunity is given by the comparative analysis, studying a social phenomenon in different spatial contexts, and comparing the emerged results. This research strategy emphasizes the changes that have been taken place over the space. Its greatest utility is to inquiry the same research question in new populations. The ‘population’ refers to the territorial dimension (national, regional, or even more local) that is relevant to the study. To make this research strategy possible, data are collected on the same variables (with the same operational definitions) over the space, and the sampling designs do not change or are considered equivalent over the space. Multilevel analysis. Multi-level analysis requires two or more data sources, integrating the columns of primary data matrices. This research approach is having success because it meets the need to have variables at different level, coming from different datasets. Multilevel analysis refers to the possibility of putting in relation individual variables, related to properties of individuals, with contextual variables, related to properties of the context (Coleman 1986). This research strategy is useful to answer research questions that connect the micro with the macro level of social phenomena. To make it possible, the sampling designs of the cumulated studies do not change or are considered equivalent (Snijders and Bosker, 1999). Repeated analysis, comparative analysis and multilevel analysis are very specific cumulative research designs, in which data integration requests the respect of rigid methodological criteria. When the secondary researcher integrates different data sources to increase the number of cases (e.g., comparative analysis), the selected primary datasets must have the same variables, with the same operational definitions. When the secondary researcher integrates two or more data sources to increase the number of variables (e.g., repeated and multi-level analysis), the selected primary datasets must have cases sampled by a common, at least similar, sampling design. Nevertheless, not all cumulative analyses are classified either as repeated, comparative or multilevel analysis; indeed, two or more datasets can be integrated in many ways, especially when both the rows and columns of the primary matrices are integrated. In this case, problems of data comparability can occur. Sometimes, the secondary researcher decides to add cases from datasets that have variables operationalized without common standards; sometimes, the secondary researcher decides to add variables, coming from different datasets, whose cases are selected with different sampling designs. Some statistical solutions (e.g., data weighting techniques) are created by scholars to tackle the problems of data comparability (Goodwin, 2012). In any case, the secondary researcher must take the responsibility, in the eyes of the scientific community, for integrating datasets made with different standards, explaining why this choice is taken: for example, the cognitive needs of the secondary analyst justify the data integration from primary datasets created with different methodological standards. In addition, secondary analysis shows other problems in doing research. The main problem concerns the existence of data on the research topics; it is possible that data useful to answer new research questions 903

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do not exist at all. In case they exist, the researcher locates the data sources, deciding whether already existing datasets are suitable to study specific objects with new research questions. Since secondary analysis uses data for purposes other than that for which they were originally collected, understanding the existing datasets is crucial. “Having accessed a dataset, the secondary analyst must spend time examining and learning to understand the data. This is greatly facilitated when the original primary researcher has provided comprehensive and accurate documentation of the data” (Devine 2003, p. 286). The decision taken by the researcher is based on the dataset/s accessibility and the quality of dataset/s. The first problem concerns the accessibility to dataset/s for secondary analysis: dataset/s can be accessible to all for free or with fee, which usually is reasonable, saving time and money which would otherwise have been invested in data collection. In addition, the researcher must assess the quality of the dataset/s, relying on the choices made by the primary researchers during the construction and the collection of the primary data. This aspect is important when data are collected with survey questions, when many errors risk of undermining the quality of collected data (Pitrone, 2009). When the access to already existing datasets is open, or the requested fee is reasonable, and the collected data are considered having good quality, the researcher evaluates the conformity of the primary dataset/s to the requirements of the secondary research design. This is the moment in which the researcher selects the variables for secondary analysis. This is a crucial decision, influenced by the operationalization process of concepts in secondary analysis, which is different to how it works in primary analysis. The operationalization process of theoretical concepts in primary analysis is represented through a diamond structure (Figure 1): 1. the definition of the general concept is given (C); 2. the meanings attributed to the general concept are grouped into semantic dimensions (D1, D2, Dn), each one is composed by a set of the meanings with a strong reciprocal semantic similarity; 3. valid indicators are established for each of the semantic dimensions (I11, I12, Inn); 4. the indicators are operationalized into variables (V11, V12, Vnn); 5. the supervariables – indexes with an intermediate level of generality – are created (SV1, SV2, SVn); 6. the final index is ready (I), and it represents the translation of the general concept (C) on the empirical level. The operationalization process of theoretical concepts in secondary analysis is different: 1. the definition of the general concept is given (C); 2. the meanings attributed to the general concept are grouped into semantic dimensions (D1, D2, Dn); 3. the data sources and the variables are theoretically selected and imported from the primary to secondary matrix (V11, V12, Vnn); 4. the secondary analyst assesses the empirical validity of the selected indicators inside the secondary data matrix (I11, I12, Inn); 5. the supervariables – indexes with an intermediate level of generality – are created (SV1, SV2, SVn); 6. the final index is ready (I), and it represents the translation of the general concept (C) on the empirical level. At the beginning of conceptualization and specification of the general concept (bullet points 1. and 2.), primary and secondary analysis do not differ, when a detailed definition of the general concept is 904

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Figure 1. The diamond structure of the operationalization process Source: (Nobile, 2008).

given, and the conceptual dimensions are isolated and interpreted trough theoretical reflections; they are equivalent also at the end of the operationalization process (bullet points 5. and 6.), when the index recomposes the semantic unity of the general concept fragmented into a plurality of indicators. The differences lie in the middle of the process (bullet points 3. and 4.), when the operationalization decisions taken by the secondary researcher are constantly put to the proof of the available data. Are the selected variables considered valid indicators of the general concept? The selection of the variables for secondary analysis and their validity as good indicators are not two independent phases; they interact and influence mutually, making the operationalization of general concept in secondary analysis like a circular process. Indeed, the secondary researcher cannot establish the final validity of the indicators during the selection of variables, if not generically and basically, anchoring the supposed indicators to the conceptual dimensions identified at point 2. Since the secondary analysis relies on

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already existing data, the researcher must limit her/himself to select the variables among the available datasets, choosing variables related to the indicators which she/he intends to consider for the secondary analysis. Even if the selection of the variables from already existing datasets (at point 3.) is carried out by theoretical considerations (e.g., content validity), the final assessment on considering the selected variables as valid indicators of the general concept must be given inside the secondary data matrix (at point 4.); this is due to the fact the secondary researcher is pushed to accept the operational definitions designed by others for the variables she/he selects, not to lose the indicators of own interest. In case the assessment of empirical validity of selected indicators is negative, the variables are deselected, and this choice brings to give up the related indicators. At this point, the only solution is to come back to the point 3. to identify other data sources and other variables to select, whose operationalization fits the needs of the secondary research. Furthermore, the selection of the variables and the assessment on the validity of indicators are part of a more general process of progressive adaptation to available data, which takes place within a more general process of selection of the statistical data sources to use for the secondary analysis. Summing up, the operationalization of theoretical concepts is not a linear process, from theory to variables, as in the case of primary analysis. In secondary analysis the operationalization of concepts is similar to a circular process: the process ends when the researcher’s final decisions are compatible with the data at his/her disposal (Capuana, 2007, pp. 16-17). Secondary researchers usually select many variables from secondary data sources, that are candidates for being considered indicators for the secondary analysis. A so large amount of data must be balanced with theoretical and empirical strategies capable of orienting the researcher to the choice of a few and selected indicators, able to best express the relationship of semantic representation that connects the indicators to the general concept and its dimensions. The authors of this chapter show that the secondary analysis is very useful to best synthesize information organized in large secondary datasets. The few selected indicators receive solid empirical evidence of their validity.

SOCIAL WELL-BEING, RELATIONALITY AND SOCIAL CAPITAL It is becoming increasingly clear that today individuals’ social well-being is based on a number of factors that do not relate just to the economic sphere; social sciences have adopted a multidimensional approach that assesses citizens’ quality of life on the basis of indicators not exclusively linked to income and consumptions (McGillivray, 2007). According to this perspective, economic growth would no longer be an end in itself, but a means of achieving a higher level of well-being. As Neumayer points out (2004), we understand well-being as the fulfillment of human preferences: the better human preferences are satisfied the greater is well-being. In this sense, income or material wealth by far are not the only relevant items creating well-being. Similarly, the actual welfare of a nation cannot be deduced from its national income or from the Gross Domestic Product (GDP). There is now a well-established understanding that the dimensions of individual and collective well-being are closely linked to social, environmental and sustainable development issues. These areas have a substantial impact on the quality of life of individuals, highlighting how the economic and social policies implemented by different countries need a more elaborated and complex set of indicators than GDP alone. The Nobel Laureate in economics Joseph Stiglitz himself (2009) denounced the pitfalls of GDP fetishism, an attitude characterized by an excessive focus on this parameter in the overall assessment of prosperity of a State and its citizens. As Stiglitz (2019) warned, 906

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GDP does not provide a good measure of living standards, and this means that the way we assess economic performance and social progress is fundamentally wrong. GDP is a synthesis measure created to give sense of macroeconomic performance, at national and supranational level, by bringing together a range of measurable and classifiable economic data, and not a tool capable of assessing social and environmental factors that are not easily quantifiable. In an attempt to overcome the limitations of GDP, in 1990 the United Nations developed the Human Development Index (HDI), an indicator structured around three elements: the longevity (i.e. life expectancy) of individuals, their level of literacy (expressed in years of education) and GDP per capita. But even this measure does not seem able to capture central aspects of social well-being such as the quality of relationships between individuals, the possibility of living in a healthy and habitable environment and the sustainability of political and social choices. The Istanbul Declaration of 2007, signed by the European Commission, the Organization for Economic Cooperation and Development, the Organization of the Islamic Conference, the United Nations, the United Nations Development Programme and the World Bank, suggested the need to move beyond conventional economic measures such as GDP per capita to assess the social progress of nations2. Identifying the most relevant dimensions for individuals’ well-being should provide a guide to the economic and social policies of each nation, since it would allow the real priorities of political action to be focused, pushing the public debate to address the needs of citizens and their living conditions. In this respect, the indicators used to measure social progress can influence economic policy choices and guide cultural, political and governance processes. Moreover, enabling politics to identify the objectives and evaluate the results of its action through the use of more complex and sophisticated tools would make the work of public institutions more adequate to the complexity of the present, improving decision-making processes and renewing the deeper meaning of political action. In addition, when considering the elements that influence a nation’s welfare, the issue of the sustainability of the current level of development for the years to come – i.e. the capacity to pass on the same standard of well-being to future generations – must also be taken into account. While well-being has an orientation towards the present, sustainability requires an effort to look to the future3, entailing the ability to provide non-declining well-being over time4; in effect, the issue of sustainability raises questions, and consequently requires policies, that are different and not always related to choices made to benefit the current well-being of a nation. The question of the sustainability of current welfare level was addressed by the Commission on the Measurement of Economic Performance and Social Progress (generally referred to as the Stiglitz-SenFitoussi Commission) formed in February 2008 by the French Government to analyze how a nation’s wealth and social progress could be measured without relying on the uni-dimensional measure of Gross Domestic Product (GDP). The Commission places the individual and the satisfaction of his/her needs at the foreground, proposing well-being and the quality of life as a result of material and immaterial living standards and broad and articulated social relations. The Final Report, published in September 2009, is structured in three sections: the first one analyses the limits of GDP as a measure of the economic and social well-being of a community; the second one highlights the multidimensionality of the concept of quality of life, stressing the need to use not only economic indicators in order to analyze it; and the third one addresses the issue of sustainable development, so as to combine the focus on economic growth with the policy-maker’s responsibility towards future generations5. The authors define sustainability as the ability to secure for future generations standards of well-being at least equal to our own by transferring to them an adequate amount of the assets on which that well-being depends: it is therefore no longer just 907

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a question of managing the present in the most responsible way, but for politics the ability to anticipate the future and to make more responsible choices towards the young must also come into play. As Giannetti et al. (2015) point out, challenge to redefine the welfare metrics entails efforts to develop environmentally oriented indicators and socially oriented measures; using GDP as an indicator of overall well-being is ambiguous and dangerous, since it ignores economic inequality and fails to assess changes in human and social capital. GDP, as an economic measure, takes into account neither the quality of economic growth nor the issue of the distribution of the benefits arising from that growth. Scholars go further, adding: “beyond a given point, increments in GDP are counterbalanced by the losses related with increasing income inequality, lack of leisure activities, and natural resource consumption. Additional increases in economic well-being may lead to adverse results such as the lessening of people’s healthy relationships, knowledge, contact with nature, and many other dimensions of individual wellbeing” (Giannetti et al. 2015, p. 12). In these situations, what has been called the “Easterlin Paradox” or “Paradox of Happiness” in economics occurs, i.e. a phenomenon that highlights the non-linearity of the relationship between income and happiness6. As the US economist has shown (Easterlin, 2001), in the western societies, above a certain income level, happiness does not increase significantly with additional income, and economic gains beyond a threshold no longer correlate with increases in personal well-being. To sum up: for low-income levels there is a positive correlation between income and happiness, the higher the income, the higher the happiness; for high-income levels, the correlation disappears: higher income, broadly unchanged happiness7. Easterlin assumes that the overall effect of income contributes to wellbeing only for those with rather limited income: after exceeding a certain threshold of material wellbeing (the critical point), the pursuit of wealth has harmful consequences, since the effort to increase income leads to negative effects on the so-called relational goods, that is, on the quality and quantity of the social relationships that the individual can build. The social actors are investing more and more in the consumption of positional goods, to the detriment of engagement in the domains of emotional and family life, on which their happiness depends to a large extent. It is as if individuals have a tendency to overestimate the effect of material goods on their wellbeing and instead underestimate the importance of relational goods for their happiness and quality of life. According to the sociologist Donati (2019), relational goods consist of social relations produced and enjoyed together by those who participate in them, with no individual subject being able to appropriate it singlehandedly. They are intangible goods, that are born and disappear with the relationship itself. In a nutshell, in this interpretation the relationship is not a means of obtaining other goods but is the good itself: family, friendship, emotional, social relationships fall into this category. Donati (2010) conceives the society in the very making of social relations in specific contexts, i.e. in the concrete configurations (social forms) that relations between subject-agents take on in a given space-time, thus constituting the social capital. One of the distinctive element social capital is therefore its relational nature. Scholars pointed out that, although social capital is enjoyed at the individual level, higher social capital at the community level can contribute to greater individual wellbeing (Auriemma et al., 2020), since individual satisfaction coming from social networks depends not only on an individual’s own engagement but on other individuals’ involvement (Portela et al., 2013). Thinking precisely about the loss of centrality of social capital in the United States, Putnam (2000) points out that the relationships capable of generating social capital are based on values that transcend the dimension of individual utility; the sharing of a universe of values commits social actors through norms of reciprocity that increase collective civicness and thus interest in the public good. In fact, it is the dimension of social capital that makes it possible for individuals to cooperate on the basis of a shared 908

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normative scheme, which encourages engagement and generates interpersonal trust8. Social capital is made up of values, informal norms and bonds of solidarity, supported by trust, which guide the actions of individuals and may or may not be formalized into associations. In this perspective, social capital, as a component of the culture of a specific community, increases the efficiency of social organization in pursuing shared goals and contributes to enhancing the well-being of citizens. All these considerations bring to light the link between well-being and social capital; for this reason, the definition of socially oriented indicators to measure the quality of life of individuals and their happiness must take into account the importance of social capital in determining satisfactory living conditions. If happiness is conceived as the perception of an overall balance between economic prosperity, relationships with others, respect for the environment and cultural growth, then the link between wellbeing, social capital and sustainability becomes evident, leading scholars to reflect more carefully on the interconnections between these dimensions. Several studies (see, among others, Wandersman and Florin, 2000; Cicognani et al., 2008) highlight the positive effects of trust, social participation and the feeling of belonging to a community – key elements in the definition of the concept of social capital – on the overall well-being of individuals; as Berkman et al. (2000) point out, socially oriented behaviors and enhancement of the relational dimension promote social well-being, implementing the quality of life of individuals. The results of a research carried out by Taylor et al. (2017) on a sample of adult population in South Australia show that the higher level of social capital corresponds with the good well-being category, while worse measures of social capital indicate lower levels of well-being. As researchers write: “well-being and social capital are two dissociable but connected measurable attributes of individuals and communities. Understanding the role of social capital in building and strengthening well-being at the population level is an important consideration when aiming for best possible experience and functioning of the population” (Taylor et al., 2017, p.1). Similarly, Guillen et al. (2011) also found a significant link between different types of informal participation and happiness (and thus well-being)9, whereas the belonging to more formal or political networks seems to show little influence on it. Finally, Bjørnskov (2003) states that, at least in advances economies, the role of social capital on well-being appears to be more important than income to the extent that, while efforts to generate income may not directly contribute to happiness, investments in social capital do. This means that involvement in activities of a social nature (e.g., civic participation, volunteering, participation in associative activities) pushes individuals to build a feeling of belonging and identification with their immediate social environment and enhances social well-being. Indeed, Putnam (2000) himself had explained how such values were at the basis of his concept of social capital, highlighting precisely the role of these dimensions in building a sense of citizenship and civic engagement. It is also by analyzing these processes that the authors of the chapter have constructed an Index of Social Love (Iorio, forthcoming). The Index of Social Love moves in the direction of overcoming GDP as the only measure for assessing the level of well-being of a community, since it configures loving action as a social action, a practice through which the subject gives to others without conditionality or reciprocity, in a social dimension that transcends individual interest and strengthens well-being. As Palmieri et al. (2021) write: “love is fundamental to sociality, as it represents the first stage of recognition, a prerequisite for every subject to participate in the public life of a community: only starting from the experience of love, the spheres of law and solidarity can be founded. According to critical theory, love allows one to experience the other and expresses a type of action that breaks with the logic of accounting, which has colonized every sphere of human action”. Loving action is based precisely on behaviors that do not take into account what is given and what is received (Boltanski, 1990), and that find no other explanation 909

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than the choice to commit oneself to the good of others. It is precisely through this form of love that it is possible to identify an unprecedented model of sociality, placing at the basis of the social bond the ability to generate benefits for another who is unknown, who is not physically or emotionally close, but who shares with the subject the same excess feeling of proximity. In this sense, sharing such an attitude plays a central role in strengthening the social capital of subjects (and therefore of communities); Social Love (SL) allows, in a way, to lubricate social networks, facilitating interaction and exchange, if not the creation of a new model of community and is very important for justice (Nussbaum, 2013). As practical knowledge to be spent within the social relations in which the subject is engaged, Social Love reveals itself through everyday action, at the very moment in which the social actor accepts to take charge of the other by transcending the symmetry of the relationship. But it is a modality of interaction that must be interpreted from a social perspective, as a conduct, that is, that reflects on the public action of individuals and not only on their private dimension, thus affecting precisely the level of social well-being that we are analyzing. If, as we have shown, those with higher amounts of social capital also report higher levels of well-being, it can also be assumed that the degree of Social Love that characterizes an individual also influences his or her level of well-being, considering SL as a key element in the promotion of solidarity and recognition which is part of the social progress and welfare of a society (Palmieri et al., 2021).

SECONDARY DATA ANALYSIS IN THE MAKING OF WORLD LOVE INDEX The World Love Index is a secondary analysis project, based on digitized data sources, created to operationalize the concept of Social Love. To create the secondary matrix of World Love Index, two data sources of world-surveys are selected and integrated, because their datasets have individual unit of analysis, have indicators of Social Love, and have a global extension: Gallup World Poll (GWP)10 and World Values Survey (WVS)11, which are non-institutional and longitudinal surveys. The answers given by thousands of respondents, from 2010 to 2014, to the questionnaires of GWP and 6th wave of WVS are aggregated at national level, composing the secondary matrix with 55 countries. The World Love Index is a cumulative research design, which integrates the rows and the columns of two primary datasets into the new secondary data matrix. The rows of the primary datasets are integrated to investigate the Social Love phenomenon by a cross-national point of view, comparing the results observed in many countries around the world. The columns of the primary datasets are integrated to satisfy the research’s cognitive needs, that is to propose the first quantitative operationalization of Social Love concept, identifying its main conceptual dimensions and the most valid indicators. As stated above, the operationalization of theoretical concepts in secondary analysis is a circular process, when the selection of data sources and variables, as well as the assessment on the validity of the related indicators, must be made through theoretical considerations and empirical procedures at the same time. These two tools are interconnected, and they influence each other because they are part of a more general process of progressive adaptation to the data sources available for the secondary analyst in that moment. This is exactly what happened during the operationalization of Social Love concept. Some experts in studying the Social Love phenomena were asked to assess the capability of the variables, imported from the primary datasets (GWP 2010-2014 and VWS 6), to be valid indicators of Social Love, trough the strategy of content validation (Marradi 2007). Have the selected primary data sources some variables that can be considered valid indicators of the dimensions of Social Love? The

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experts validated fifteen indicators with theoretical criteria, anchoring the supposed indicators to the conceptual dimensions of Social Love. The table 3 shows the list of the validated indicators, attributed to the four dimensions of Social Love, theorized by the experts. Overaboundance: it is the typicality of Social Love. Love is social when it is characterized by the overabundance, that is giving more than the situation demands or more than what one has received according to a given measure. Social Love occurs when individuals refuse to keep count and show unconditional behaviors, breaking the logic of ‘do ut des’. The overabounding action breaks the interpersonal expectations and overcomes any antecedent with the action; the social actor does something unexpected by the others, doing more than the context requires. Volunteering free time to no-profit organizations, donating money to charity, being member of humanitarian or/and environmental organizations are examples of overabounding actions. The priority for ​​the benefit of others: the overaboundance can be negative if it is not doing good to another person. Social Love needs an objective criterion to be recognized: the overabounding action must be oriented to benefit the other in priority, doing good for the other, not waiting reciprocity. For this reason, Social Love must be considered an overabounding action which benefits the other in priority; Social Love does good to others. Indicators of giving priority for the benefit of others are participating in a demonstration for environmental causes, giving money to an ecological organization, considering people living in poverty and need the most serious problem for the world, as well as considering environmental pollution the most serious problem for the world as a whole. Recognition of others: Social Love considers the other as irreducible and singular person, without judging the other whoever he/she is. Considering the other as a unique and unrepeatable subject brings out the value of tolerance and respect for others, as well as the value of unselfishness. Examples of love of this kind can be welcoming the idea of ​​the other even if divergent from mine, considering the diversity of the other person as an added value for me and society, to make a more human and less impersonal society. Universalism: Social Love is an action oriented to all people and goes beyond the in-group’s logic (partner, family members, friends, etc.). Examples of Social Love are helping an unknown person or someone from another country or culture, even helping an ungrateful person, trusting people regardless their nationality or religion. Indeed, nobody has the expectation that you will do something for these people. The act of helping them is overabounding in terms of time or resources made available for them. Although it is difficult to translate these semantic dimensions into operational terms, as it is mostly linked to the sphere of the tacit (Montesperelli, 2014), the operationalization of Social Love has taken shape, using indicators of behaviors, opinions and feelings considered proxies of social love. The results of the World Love Index, as a composite index, are reported on www.worldloveindex.net. In the following step, the research group was looking for empirical confirmation for the theoretical and conceptual evaluations made by the experts of Social Love, trying to answer the following questions: 1. Do the conceptual dimensions of World Love Index matrix match the conceptual dimensions theorized by the expert of Social Love? If not, which are the main conceptual dimensions that compose our secondary data matrix? 2. Can the variables of the World Love Index matrix, firstly theoretically validated, be considered valid indicators of Social Love also from the empirical point of view?

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Table 3. The list of indicators of Social Love validated theoretically The Overaboundance Dimension 1. Have you done any of the following in the past month? How about volunteered your time to an organization? (Yes/No)     from GWP 2. Have you done any of the following in the past month? How about donated money to a charity? (Yes/No)     from GWP 3. Could you tell me whether you are an active member, an inactive member or not a member of ‘humanitarian or charitable organization’? (Active member/Inactive member/Not a member)     from WVS 4. Could you tell me whether you are an active member, an inactive member or not a member of ‘environmental organization’? (Active member/Inactive member/Not a member)     from WVS The Priority of Others’ Benefit Dimension 5. During the past two years have you given money to an ecological organization? (Yes/No)     from WVS 6. Please indicate if you consider ‘people living in poverty and need’ the most serious problem for the world as a whole? (Mentioned/Not mentioned)     from WVS 7. Please indicate if you consider ‘environmental pollution’ the most serious problem for the world as a whole? (Mentioned/Not mentioned)     from WVS 8. During the past two years have you participated in a demonstration for some environmental     cause? (Yes/No)     from WVS The Recognition of Others Dimension 9. Here is a list of qualities that children can be encouraged to learn at home. Do you consider ‘tolerance and respect for other people’ to be especially important? (Mentioned/Not mentioned)     from WVS 10. Here is a list of qualities that children can be encouraged to learn at home. Do you consider ‘unselfishness’ to be especially important? (Mentioned/Not mentioned)     from WVS 11. Would ‘the progress toward a less impersonal and more humane society’ be the most important thing for you? (The first and the second important thing)     from WVS The Universalism Dimension 12. Would you say that most people can be trusted or that you need to be very careful in dealing with people? (Most people can be trusted/You need to be very carefully)     from WVS 13. Have you done any of the following in the past month? Have you helped a stranger or someone you did not know who needed help in the past month? (Yes/No)     from GWP 14. Could you tell me whether you trust people of another religion? (Completely/Somewhat/Not trust/Not trust at all)     from WVS 15. On this list are various groups of people. Could you mention ‘immigrants’ as group of people that you would not like to have as neighbors? (Mentioned/Not mentioned)     from WVS

To reach these objectives, our strategy requested to employ the ‘two-stages’ factor analysis on the secondary matrix of World Love Index (Di Franco and Marradi, 2013).

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In the first stage the factor analysis is used for exploring the multidimensionality of datasets and to identify the conceptual dimensions that shape the structure of data, through statistical quantification of the relations between the variables imported from the primary datasets. If a strong empirical relationship exists, it is likely that this is due to the presence of one or more general concepts, or conceptual dimensions, that semantically justify the statistical findings. In the first stage, the multidimensionality of the secondary matrix of World Love Index, composed by the fifteen variables validated by the experts in advance, is explored: the central conceptual dimensions are recognized and interpreted, making it possible to have a comparison between the dimensions interpreted theoretically by the experts and the main dimensions that statistically constitute the secondary matrix of World Love Index. The second stage of the factor analysis is crucial for selecting the indicators. In this case, the variables that are statistically associated with the dimensions interpreted in the first stage are subjected to a careful selection and refinement process, to identify the best indicators to be employed for the construction of World Love Index. At the end of the process, these indicators are used for constructing the synthetic index, which represents the final phase of the operationalization process, where the contribution of each indicator is carefully weighted12. The table 4 shows the final list of the indicators validated statistically and attributed to the two central dimensions of the secondary matrix of World Love Index. Table 4. The final list of indicators of Social Love validated statistically and attributed to the central dimensions of World Love Index The Overaboundance 1. Have you done any of the following in the past month? How about donated money to a charity? (Yes/No)     from GWP 2. During the past two years have you given money to an ecological organization? (Yes/No)     from WVS 3. Have you done any of the following in the past month? How about volunteered your time to an organization? (Yes/No)     from GWP 4. Have you done any of the following in the past month? Have you helped a stranger or someone you did not know who needed help in the past month? (Yes/No) from GWP Type of Beneficiary of the Overabounding Action 5. Please indicate if you consider ‘people living in poverty and need’ the most serious problem for the world as a whole? (Mentioned/Not mentioned)     from WVS 6. Please indicate if you consider ‘environmental pollution’ the most serious problem for the world as a whole? (Mentioned/Not mentioned)     from WVS

By means of ‘two stages’ factor analysis employed on the matrix of World Love Index, two conceptual dimensions are isolated and interpreted: the dimension of the ‘overaboundance’, and the dimension of ‘type of beneficiary of the overabounding action’ (which is related the concept of the priority of other’s benefit). The overabounding action occurs when the individual gives to the other (unknown and universal) more than expected by the others. This constitutes the semantic core of Social Love, that is giving without conditionality, without reciprocity. But it is not a generic action; it is a gesture of love to

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the others. Indeed, the love is social when it generates benefits for the recipients of the overabounding action, giving priority for the benefit of the other. But who is the other? The second dimension clarifies whom or what receives benefits by the making of the overabounding action. Sometimes, the ‘other’ are the people in need, the poor in condition of extreme fragility; donating money, time and general support are overabounding actions aimed to alleviate the condition of extreme poverty that afflicts many people. Sometimes, the ‘other’ is the environment, in its purity and naturalness, threatened by pollution due to the human activity; the overabounding action is primarily directed to the environmental protection and the fight against pollution and climate change. Thanks to this strategy, that mixes theoretical argumentations and empirical procedures, it is emerged that two central dimensions of Social Love, theorized by the experts, are present in the secondary matrix of the World Love Index: the overaboundance and the priority of other’s benefit. The secondary matrix of World Love Index has no trace of the other dimensions theorized by the experts (the recognition of others and the universalism). This approach allowed to have empirical validation of the theoretical choices made by the experts about the indicators of Social Love. Four indicators of the overabounding action are validated empirically: donating money to charity organizations is the most important indicator, followed, in order of importance, by donating environmental organizations, by volunteering own time, and helping strangers in need. In addition, two indicators (considering people living in poverty and need to be the most serious problem for the world as a whole and considering ‘the environmental pollution’ to be the most serious problem for the world as a whole are employed and considered equally important) are empirically validated to recognize Social Love as an action oriented to benefit the people in need or to benefit the natural environment in priority.

CONCLUSION The secondary analysis for quantitative data is a traditional social research method re-discovered by social researchers in the new digital setting, due to the increasing number of digitized data sources, in which large datasets are available. These datasets, composed of data designed and collected for purposes other than those of the secondary analysis, are useful for implementing innovative studies, to answer new research questions that would otherwise remain unanswered. In the recent years, one of the most frequent uses of secondary analysis for digitized data has concerned the study of social well-being through a cross-national perspective, to construct indices that are alternatives to GDP, adopted as the prevailing measure of national progress. Thanks to the availability of a growing number of large datasets, the secondary analyst inquiries this phenomenon from a global point of view, making it possible to compare the social well-being of a country with that of other countries placed in different geographical and cultural contexts. In such a situation of wide availability of data, the challenge for scholars engaged in constructing indices of social well-being has shifted to the careful selection of indicators capable of operationalizing the multidimensionality of the concept, in which the economic dimension is only part of a much more complex set of semantic facets related to the quality of relationships between people, as well as to the quality of the environment in which they live, the quality of their personal and interpersonal activities, and so on. Thus, the secondary analysis is employed not only to construct synthetic indices, that recompose the semantic unity of the general concept fragmented into multiple indicators; it also offers valuable tools 914

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for identifying and selecting valid indicators of the dimensions of social well-being that are being investigated by the researcher. The selection of indicators used to assess the quality of life of a community is a crucial moment, to which due attention should be paid by the secondary analyst, because the dimensions of social well-being are so general as not to suggest direct forms of empirical operationalization. In this chapter the authors present the choices made during the construction of World Love Index, result of the empirical operationalization of agape as Social Love concept. In this research experience, the secondary analysis has been very useful for identifying and selecting the best valid indicators of the dimensions of Social Love, initially selected on the basis of theoretical criteria. The comparison between the theoretical validation performed by the experts and the empirical validation performed by statistical procedures shows that only part of the conceptual dimensions theorized by the experts are present in the secondary dataset of World Love Index; additionally, only part of the indicators initially validated theoretically by the experts received statistical validation. The World Love Index shows how the empirical operationalization of theoretical concepts works in secondary analysis: it is not a linear process, but it is like a circular process, because the decisions of the secondary analyst have to deal with the decisions taken by the primary researchers in the construction and collection of the primary data. This affects especially the selection of the indicators. The first validation of indicators lies on theoretical and conceptual considerations; secondly, this validation requests to be controlled empirically, due to the atypical character of the operationalization process of the concepts in the secondary analysis. After carefully comparing the theoretical and empirical contribution to the selection of the indicators, the secondary researcher has a well-founded awareness that the variables selected from the primary datasets are also valid indicators in the secondary dataset, able to offer a solid empirical basis to answer new research questions using old data.

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This chapter is a joint work by the three authors. However, in line with standard academic practice, we indicate that Marco Palmieri wrote the introduction, paragraphs “Secondary analysis for digitized data”, “Secondary data analysis in the making of World Love Index” and the conclusion; Geraldina Roberti wrote the paragraph “Social well-being, relationality and social capital”. The rest of the paper is a joint work. All authors have read and agreed to the published version of the manuscript. For more information see https://www.oecd.org/newsroom/38883774.pdf Neumayer (2004) defines sustainability a future-oriented concept. Back in the mid-1990s, when examining the relationship between welfare, economic development and sustainability, Max-Neef (1995, p. 117) pointed to a limit beyond which these three dimensions tended to diverge: “for every society there seems to be a period in which economic growth (as conventionally measured) brings about an improvement in the quality of life, but only up to a point - the threshold point - beyond which, if there is more economic growth, quality of life may begin to deteriorate”. See https://ec.europa.eu/eurostat/documents/8131721/8131772/Stiglitz-Sen-Fitoussi-Commissionreport.pdf According to Easterlin (2004), although those with a higher income may reasonably experience a higher level of happiness during their lifetime than those with a lower socio-economic status,

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8



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there is no evidence that perceived happiness increases when income rises. On this topic see also Easterlin (1995; 2017) and Easterlin et al. (2010). For an exhaustive review on the different interpretations of the “Easterlin paradox” see Clark et al. (2008). In this respect, in Italy it is Cartocci (2007), among others, who insists on bringing values back into the construct of social capital, highlighting how cooperation between subjects is the result of individual actions inspired by a principle of gratuitousness, rather than by a criterion of mere cost/ benefit assessment. As the scholars state, “[…] informal participation refers to the number of interactions that an individual has with relatives, friends and work colleagues in an informal setting, while formal participation refers to the number of interactions resulting from involvement in established organizations in society” (Guillen et al., 2011, p. 334). www.gallup.com/analytics/318875/global-research.aspx https://www.worldvaluessurvey.org/wvs.jsp The choices and the output related to the factor analysis conducted on the matrix of World Love Index are illustrated in Iorio G., Sociologia do Amor. Agape na vida social, Ateliê de Humanidades Editoria (forthcoming).

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Zappavigna, M. (2011). Ambient affiliation: A linguistic perspective on Twitter. New Media & Society, 13(5), 788–806. doi:10.1177/1461444810385097 Zendle, D., Meyer, R., & Ballou, N. (2020). The changing face of desktop video game monetisation: An exploration of exposure to loot boxes, pay to win, and cosmetic microtransactions in the most-played Steam games of 2010-2019. PLoS One, 15(5), e0232780. Zhang, G., & Zhu, A. X. (2018). The representativeness and spatial bias of volunteered geographic information: A review. Annals of GIS, 24(3), 151–162. doi:10.1080/19475683.2018.1501607 Zhang, S., & Feick, R. (2016). Understanding public opinions from geosocial media. ISPRS International Journal of Geo-Information, 5(6), 74. doi:10.3390/ijgi5060074 Zhao, W. X., Jiang, J., He, J., Song, Y., Achanauparp, P., Lim, E. P., & Li, X. (2011). Topical keyphrase extraction from twitter. Proceedings of the 49th annual meeting of the association for computational linguistics, Human language technologies, 379-388. Zhao, N., & Cao, G. (2017). Quantifying and visualizing language diversity of Hong Kong using Twitter, Environment and Planning A. Economy and Space, 49(2), 2698–2701. Zhao, Y., Dong, S., & Li, L. (2014). Sentiment analysis on news comments based on supervised learning method. International Journal of Multimedia and Ubiquitous Engineering, 9(7), 333–346. doi:10.14257/ijmue.2014.9.7.28 Zhong, Z., Balagué, C., & Benamar, L. (2017, May). Conséquences de l’appropriation des objets connectés par les consommateurs: une étude sur les usages effectifs quotidiens de la montre connectée [Paper presentation]. The 33th Congress of the Association Française du Marketing (AFM), Tours, France. Zhou, X., Xu, C., & Kimmons, B. (2015). Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform. Computers, Environment and Urban Systems, 54, 144–153. doi:10.1016/j.compenvurbsys.2015.07.006 Zhou, X., & Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys, 53(5), 1–40. doi:10.1145/3395046 Zimba, A., & Chishimba, M. (2019). On the economic impact of crypto-ransomware attacks: The state of the art on enterprise systems. European Journal for Security Research, 4(1), 3–31. doi:10.100741125-019-00039-8 Zimmerman, D. H., & Wieder, D. L. (1977). The diary Diary-Interview Method. Urban Life, 5(4), 479–498. doi:10.1177/089124167700500406 Zineldin, M. (2005). Quality and customer relationship management (CRM) as competitive strategy in the Swedish banking industry. The TQM Magazine, 17(4), 329–344. doi:10.1108/09544780310487749 Zinn, J. (2007). Risk, Social Change and Morals. Conceptual Approaches of Sociological Risk Theories. Working paper 2007/2017. Social Contexts and Responses to Risk Network. Available at: https://www.kent.ac.uk/scarr/ Zittrain, J. L. (2014). Reflections on Internet culture. Journal of Visual Culture, 13(3), 388–394. Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. Public Affairs. Zuboff, S. (2019). The age of surveillance capitalism: the fight for a human future at the new frontier of power. PublicAffairs. Zupi, M. (2017). Social GIS per l’analisi dei comportamenti e delle abitudini in ambito urbano. GIS Day Calabria 2017.

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About the Contributors

Gabriella Punziano, PhD in Sociology and Social Research, is Assistant Professor in Sociology and Methodology, Department of Social Sciences, University of Naples Federico II where she teaches New data and new methods for communication analysis and Software for qualitative and quantitative analysis. Her research interests include: the methodology of social research, the new analytical frontiers and the challenges introduced by new data, mixed and integrated and digital perspectives; social policies and welfare regimes in relation to social inclusion, territorial cohesion and community integration; the analysis of public, institutional and political communication phenomena through innovative content analysis techniques; risk communication analysis on social and digital platforms; socioeconomic effects, individual behavior, risk governance, communication of science, and the role of expertise in the Covid-19 era. Angela Delli Paoli graduated cum laude in 2005 with a master degree in Communication and a thesis in Social Research Methods at the University of Salerno (Italy). She received her Ph.D. in Entrepreneurship and Innovation in 2010 from the Second University of Naples (Italy). She was visiting researcher at the London Business School (UK) in 2008. She holded a Postdoctoral Research Fellow position at the University of Salerno (Italy) from 2010 to 2017. She was Adjunct Professor of “Social Research Methods” and “Sociology” at the University of Salerno and of “Research Methods in Scientific Research” at the Second University of Naples. Now, she is Assistant Professor at the Department of Humanities, Philosophy and Education (University of Salerno). Author of monographic studies, several essays, book chapters and articles published in national and international academic journals, she has also teaching experience at both undergraduate, postgraduate and master levels. She participated as senior researcher and expert evaluator in several European (Horizon 2020 interim evaluation, Eurofound expert groups), national (INVALSI; Youth Department, etc.), and regional expert groups. She acts also as independent evaluator for the European Commission General Directorate for Research and Innovation and the European Commission Research Executive Agency (REA). Her current research interests are qualitative and quantitative social research methods; epistemology, online research methods, big data, netnography. *** Suania Acampa is a PhD student in Social Sciences and Statistics at the University of Naples Federico II, her research interests are digital sociology, digital methods, methods for information and communication analysis.  

About the Contributors

Felice Addeo, PhD in Communication Science, is Associate Professor in Social Research Methods at the University of Salerno, Department of Political and Communication Science, where he holds the chairs of “Social Research Methods” and “Communication and Market Research Methods”. He is a methodologist with a broad and consolidated experience in Social Science Research. His epistemological perspective is focused on the integration among the different social research methods: this has characterized his research, which ranges across different disciplinary fields (gender violence, social cohesion, immigration, corporate communication, evaluation of sustainable development policies, business economics, marketing, game studies, etc.). His scientific and research activity has also been aimed at deepening epistemological and technical aspects of recent methodological approaches, such as Mixed Methods, Online Research Methods and Netnography. Since 2017, He is the director of the Summer School in Social Research Methods. He is also Independent Expert Reviewer for the European Commission General Directorate for Research and Innovation and Independent Evaluator for the European Commission Research Executive Agency. His current research interests are social research methods; epistemology, quantitative research, qualitative research and mixed methods, online research methods, netnography, sustainable development, social cohesion, migration studies. Mete Akcaoglu, Ph.D., is an associate professor of Instructional Technology at Georgia Southern University, Leadership, Technology, and Human Development Department. His research focuses on designing innovative and technology-rich learning environments to teach young children important higher-order thinking skills. More detail on his past and current research and teaching can be found on http://meteakcaoglu.com. Alhassan Yakubu Alhassan is a PhD Research Fellow at the Department of Sociology and Social Work at the University of Agder, Norway. His research interest is on Digitalisation, Social Network Analysis, and Urban Development. Alhassan holds a Masters in Sociology from the University of Saskatchewan in Canada and a Masters in Global Development and Planning from the University of Agder. Prior to his position as a PhD Research Fellow, he worked as Teaching Assistant in Research Methods and Statistics at the university of Saskatchewan. He has also worked as an online tutor at the department of global development and planning and a teaching and research assistant at the Department of Sociology and Social Work at the Kwame Nkrumah university of Science and Technology in Ghana where he obtained his BA(Hons) Sociology and Social Work. Enrica Amaturo is a Full Professor of Sociology (sector SPS/07) at the Department of Social Sciences, University of Naples Federico II, where she teaches Methodology of Social Research in the bechelors in Sociology and Critical Epistemology in the Master in Digital Sociology and Web Analysis, Master of which she is President. She is Coordinator of the PhD in Social Sciences and Statistics. She currently directs, together with Gabriella Punziano, the series of Applied Social Sciences of Libreriauniversitaria.it. Biagio Aragona is associate professor in Sociology at the Department of Social Sciences University of Naples Federico II. Member of the Board of the Italian Sociological Association and managing editor of the AIS Journal of Sociology – SOCIOLOGIA ITALIANA. He teaches “Digital methods and big data” and “Advanced methods for quantitative research” and has been Principal Investigator for the research project B-DATA, Big Data Assemblages: Techniques and Actors. Visiting researcher at the Web Science Institute of the University of Southampton (UK) and at Universidad de Salamanca (ES) and member of the PH.D. Committee in Social Sciences and Statistics of the University of Naples Federico II. clxxi

About the Contributors

Gianluca Attademo, PhD, is a researcher in moral philosophy. His research develops around the themes of German Jewish symbiosis, anti-Semitism, biotechnology and the governance of regenerative medicine. He teaches Bioethics (M-FIL / 03) at the universities of Naples and Salerno and taught History of Modern and Contemporary Philosophy at the Pontifical Theological Faculty of Southern Italy. As a bioethics expert he has been a member of research ethics committees since 2010 and is member of the Scientific Advisory Board of the Laboratory “Ethics, Bioethics and citizenship” (EBC), University of Naples Federico II. Eugenio Bagnini, Ph.D. in Sociology and Social Research, is adjunct professor of Sociology of Sport and Communication at Department of Life Quality Studies, Alma Mater Studiorum - University of Bologna. Since 2018 he is collaborating with SportComLab-Center for Studies and Research on Sports Communication of the same University. His field of study concerns the sociology of cultural and communicative processes, with particular interest in fitness and well-being with high technological innovation; his research interests concern Human Technology Interactions, Digital Culture and Sociology, Communication Studies and, finally, Body, Sport and Wellness. He recently published: 2020, A corpo “libero”. Bodybuilding, fitness and wellness practices between rationality and morality, in “ERACLE. Journal of Sport and Social Sciences “, vol. 3, pp. 63 76. Félix A. Barrio has a PhD from the University of Salamanca and an MSc in Software and Systems Engineering of the UNED University of Madrid. He is a tenured professor of “Social and Industrial Cybersecurity” at the Isabel I University (Spain) as well as Deputy Director of the Spanish National Institute of Cybersecurity. He has been director of the Cybersecurity Hub of the Monterrey Institute of Technology and Higher Studies. Maria Giovanna Brandano is an assistant professor in Applied Economics at the Gran Sasso Science Institute Italy. She holds a PhD from the University of Sassari (Italy). She taught Tourism Economics at the Free University of Bozen, University of Sassari and in the GSSI PhD school. Her research interests are tourism externalities, tourism taxation, wine economics, cultural heritage economics and tourism in inner peripheries. Adelina Calvo-Salvador, with a doctorate in Pedagogy (University of Oviedo, Spain), is a senior lecturer in the Department of Education at the University of Cantabria, Spain, in the area of Didactics and School Organization. Her research interests include socio-educational inclusion/exclusion mediated by technology, global education, student voice, gender and education and school improvement. She is a member of ANGEL (Academic Network on Global Education and Learning). Jessica Camargo Molano is an art critic. In the past 10 years she worked for important national and international newspapers, particularly in the field of digital media criticism: cinema and television. Her research focuses on the way in which the concepts of avant-garde and technological innovation are combined in artistic experimentation. She is a PhD student at International Telematic University “UniNettuno”. At present she is an assistant lecturer of Sociology of Experimental Audio- visual media; Sociology of electronic arts; Sociology of multimedia entertainment; Theories and techniques of digital media at the University of Salerno (Italy).

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About the Contributors

Claudia Cantale is a researcher (RTDa) in Sociology of Cultural and Communicative Processes. Since 2009, she has worked in the field of communication and promotion of cultural heritage and scientific museums. In 2018 she obtained her PhD in Cultural Heritage with a research related to the communication and cultural heritage according to the perspectives of Digital Humanities. She is currently engaged a PON research project “AIM - Attraction and International Mobility” 2014-2020 in the field of cultural studies and digital sociology. She is a member of the interdisciplinary research centre CINUM – Centro di Informatica Umanistica. Viviana Capozza is a PhD student in methodology of social sciences, Co.Ris department (Sapienza University of Rome). Main research interests: usability evaluation, social impact assessment, program evaluation, methodology of social research. Daniel Castro Aniyar is a Sociologist (LUZ), Formation in Cultural Anthropology (Université de Montreal), Master in Social Anthropology (EHESS Paris), Master in Public Policies (Complutensis University of Madrid), PhD on Peace and Conflict (Complutensis University of Madrid) Criminology advisor for several Latin American governments, and police departments. Researcher on crime policies, criminometrics, critical criminology and epistemology of sciences. Was titular Professor of qualitative methods at the University of Zulia (Venezuela) for 20 years. Actually, professor of Criminology at the ULEAM (Ecuador). Wrote 11 books and more than 25 scientific articles, many of them at high impact indexed journals. Casa de las Americas (House of the Americas) awarded. Invited Lecturer at Université de Grenoble, the Union of South American Nations (UNASUR), the Hebrew University of Jerusalem, the University of Stockholm, Sweden. House of the Americas in Havana, the University of Wilhemstad, the High Studies School in Social Sciences at Paris (EHESS), and the Complutensis University of Madrid, among other places. Maria Carmela Catone is a visiting professor at the Department of Sociology of the University of Barcelona (Spain), where she teaches Scientific Methodology and Techniques of Social Research. Her research topics relate to both traditional and emerging issues concerning social research methodology and the practices used in the teaching of research methods through e-learning and ICT systems. Michela Cavagnuolo, PhD student in methodology of social sciences, Co.Ris department (Sapienza University of Rome). Main research interests: digital methods, evaluation and migration. Jacopo Cavalaglio Camargo Molano is a software engineer in Tetrapak Packaging Solution S.P.A. In 2013 he took a bachelor’s degree in Industrial Engineering at the University of of Perugia; in 2016 he took a master degree in Mechatronics Engineering at the University of Modena and Reggio Emilia, where he continued his studies in Industrial Innovation Engineering as a PHD student, at the end of which he presented a thesis entitled “A condition based monitoring framework for independent carts system.” Over the last few years he has focused his research on condition monitoring for mechatronic systems, he has published his works in International Journals and at present he continues his research on the use of AI in condition monitoring. Giulia Ciancimino graduated in Economics for Development, now collaborating to scientific and research activities as well as communication for CNR’s Social Transformation, Evaluation and Methods (MUSA) team. Currently a member of the Observatory for Ongoing Social Changes-COVID-19 (OSC clxxiii

About the Contributors

COVID-19). Mainly addressing the analysis of behaviours and attitudes within the population, with a specific focus on socioeconomic and cultural factors. Jitka Cirklová, M.A., Ph.D., is a sociologist at the University of Finance and Administration in Prague, Deputy Head of Department of Marketing Communication. She completed her Ph.D. in Sociology at the Charles University in Prague and her M.A. studies at the Hebrew University in Jerusalem, Israel. Jitka currently teaches graduate courses in Sociology, Sociological Research, and Consumer Culture. The focus of her research work is in the Sociology of culture, identity and transformation of lifestyles, and intergeneration changes of value models. Jitka is also responsible for the Internal research projects that involve students participations https://consumesustainablyvsfs.wordpress.com. Focus of research projects is on value models and business practices of people inspired by sustainability that suggest different preferences and choices these people are making while forming the identities within a community. Her interest in the theoretical, methodological, and empirical issues of qualitative research is of a prolonged character. She concentrates continually on using the emic perspective of the active participants fortified by the content, narrative, and visual analysis by using the innovative methodology of Engaged Photography and Netnography. She contributes to the research area by exploring the further understanding of adaptation processes and cultural accommodation of new concepts, values, and ideas in our communities. Marianna Coppola is a clinical psychologist and PhD student in communication sciences at the Department of Political and Communication Sciences at the University of Salerno. Her research fields concern new media, gender studies, LGBT studies and the hikikomori effect. Noemi Crescentini, graduated in Sociology and Public, Politics and Social Communication, is currently a PhD student in Social science and statistics at University of Naples Federico II. Her research interests regard research methods for social science, sociology of science and STS studies. Francesca De Chiara is Researcher at Fondazione Bruno Kessler, where she is working on studying, developing and promoting data sharing at the institutional and community level. The latest projects focus on analyzing the impact, the social and economic value of open data, the reuse of the public sector information. As Fellow at the GovLab at New York University, she led the joint project Open Data 200 Italy. She holds a PhD in Sociology and Social Research. She has been Research Fellow in Development Sociology at Cornell University and visiting doctoral researcher at the Warwick Business School. She coordinated and designed three editions of the Civic Tech School; training sessions, hackathons, and data challenges for local and national governments. Ciro Clemente De Falco has a PhD in Social Sciences and Statistics, carries out research at the Department of Social Sciences of the University of Naples Federico II where he collaborates with the chair of Methodology of social research. Luca Di Censi is scientific advisor for research activities related to social impact evaluation at Human Foundation. He collaborates, at national and international level, with public research institutions, universities and social organizations in study areas such as welfare, addictions and extreme poverty. His latest works cover topics such as: EU family policies, second-generation immigrants, emerging urban poverty and cost analysis of the justice system related to the drugs phenomenon. clxxiv

About the Contributors

Irina Dimitrova is doing her licentiate in Business Administration. Her research focuses on the consumer behavioural issues, primary in the banking industry. She has published scholarly articles in form of book chapter and conference paper. She is a part of The Centre for research on Economic Relations (CER) and the subject of Business Administration at Mid-Sweden University. Hossam Mohamed Elhamy has been an assistant professor at the College of Communication and Media Sciences, Zayed University, UAE since 2019. He has worked at different Arab universities, including Ahlia University, Bahrain; Modern University for Technology and Information, Ain Shams University, and Akhbar Elyoum Academy, Egypt. He has published several books, research papers, and articles in media and mass communication and participated in several international conferences in the field. His research interests include media discourse analysis, narrative analysis, and media semiotics. Mariapaola Faggiano is a PhD in Methodology of Social Sciences and Researcher in Sociology (SPS/07) at the Department of Communication and Social Research (CoRiS) of Sapienza University of Rome. She is the Scientific Head of the Communication and Social Research Laboratory (CorisLab) at the same Department and she has been carrying out its training offer since 2011 (https://web.uniroma1. it/corislab/home). She is a member of the CoRiS Electoral Sociology Observatory and of the Board of the Methodology Section of the Italian Sociology Association (AIS). She is part of the didacticscientific council of the II level inter-university Master in Methodology and Advanced Techniques of Social Research (MeTARS - Dep. CoRiS); she is also a member of the Board of Professors of the PhD in Communication, Social Research and Marketing (Dep. CoRiS). She has published numerous works and articles in national and international journals on methodological and sociological topics (youth lifestyles, social participation, political online communication, social representation of diversity and intercultural coexistence, data collection and analysis techniques). Cristiano Felaco, PhD in Methodology of Social Sciences, is Assistant Professor at the Department of Social Sciences, University of Naples Federico II. His main scientific interests ranging around digital methods, social media, datification processes and algorithms, with particular attention to youth studies. Marco Ferracci earned his Master’s degree in Sociology and Social Research at the University of Florence. He is currently a PhD student in Social Science and Statistics at the University of Naples Federico II. His research interests are economic sociology, mixed methods and methods for social research. Aquilina Fueyo Gutiérrez is a full‐time senior lecturer of Educational Technology at the University of Oviedo (Spain). Since 1988 she has developed educational activities as a university professor in the subjects of Educational Technology, Communication and Media Education, Educational, Computing Education, New Technologies for Education, and New Technologies for Education Innovation. She has taken part in official Master’s Degrees and Doctorate Programs about Social Media, Communication and Education. She has a very long experience in the use of e-learning platforms and in the direction of projects based in Information and communication technologies. She has been an active researcher in the areas of Media education and Digital competence, Virtual Learning and Development Education. She has published a lot of articles in indexed journals and chapters in books, and various books on the aforementioned subjects.

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About the Contributors

Giuseppe Giordano is associate Professor of Statistics at the Department of Political and Social Studies (DISPS) of the University of Salerno (Italy). Scientific Director of the Didactic Laboratory of Data Analysis at DiSPS. The main research interests are in the area of Statistics and its applications in the fields of Social Sciences, Economics, Marketing and Medicine. The methodological interests concern the development in R environment of techniques and models for the analysis of data inherent to the study of preferences (Conjoint Analysis), relational or flow data (Social Network Analysis), Models for actuarial data and demographic structures (Mortality Data); Complex Networks (Neuroscience; Recommendation systems, Semantic Networks). He collaborates with CNR-IRISS on research projects concerning the analysis of the Italian port system and the application of SNA to the cultural and religious heritage of the Unesco site “Centro Storico di Napoli”. The results of the research activities have been published in international scientific journals. Giulia Giorgi holds a MA in Linguistics and is currently a PhD Candidate in Digital Sociology at the University of Milan and University of Turin. Her dissertation investigates the role of memes in the creation of a shared generational imaginary across different platforms. Her research interests include figurative speech, multimodal metaphors, youth culture(s) and digital methods. Edmondo Grassi is an adjunct professor of Sociology of cultural and communicative processes at the San Raffaele Telematic University of Rome. He is a PhD in Theoretical and Applied Social Research. He deals with ethical changes produced by the use of technologies, communication, postmodern identity and the thought of complexity. Charles B. Hodges, Ph.D., is a Professor of Instructional Technology at Georgia Southern University in Statesboro, GA. He was formerly a faculty member at Virginia Tech. Dr. Hodges earned his B.S. and M.S. degrees in Mathematics from Fairmont State University and West Virginia University respectively, followed by a Ph.D. focusing on Instructional Design and Technology from the School of Education at Virginia Tech. He is Editor-in-Chief of the AECT journal, TechTrends and he is the co-editor of the book Emerging Research, Practice, and Policy on Computational Thinking (2017) published by Springer, as well as editor of the book Self-Efficacy in Instructional Technology Contexts (2018) also published by Springer. Ludovico Iovino is currently Assistant Professor at the GSSI in the Computer Science scientific area. His interests include Software Engineering, Model-Driven Engineering (MDE), Model Transformations, Metamodel Evolution, code generation, and software quality evaluation. He has participated in different academic projects related to Technology Transfer, Model Repositories, model migration tools and Eclipse Plugins. Lucas John Jensen is an Associate Professor of Instructional Technology at Georgia Southern University. He received M.Ed.’s in Social Science Education and Instructional Design and Development and a Ph.D. in Learning, Design, and Technology from the University of Georgia. He has taught courses and conducted professional development courses and parent workshops on Instructional Technology, social media, and digital citizenship issues. His research focuses on video game design and educational social media use.

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About the Contributors

Alessandro Laruffa is a PhD candidate in History of Europe at “Sapienza” University of Rome, where he holds a master’s degree in Political Science. He has collaborated with the Vrije Universiteit Brussels and with the Institute of History of Mediterranean Europe - CNR. He is currently involved in the historiography of the History of Europe in contemporary age and he is manager of a digital humanities project at the “Istituto Storico Italiano per l’Età Moderna e Contemporanea” in Rome. Patrizio Lodetti has a Ph.D. in Sociology and Methodology of Social Research. In recent years he has expanded and refined methodological techniques related to digital methods. His research interests are oriented towards the analysis of new media and computational sociology. Alessia Maccaro, PhD, is a research fellow (WIRL COFUND - Marie Sklodowska Curie Actions) at the Institute of Advanced Study of the University of Warwick (UK). Her research focuses on hermeneutics of the religious, questions of moral philosophy emerging from the use of new technologies for health and bioethics for LRSs. She has authored many scientific articles, books chapters and books about moral philosophy and bioethics and is Teaching Fellow in Moral Philosophy (M-FIL03) and Philosophy and Theory of Languages at University of Naples Federico II - Medical School. In 2020 she achieved the National Academic Qualification as Associate Professor of Moral Philosophy (ASN M-FIL 03). She is member of different professional bodies related to bioethics and she is member of the Scientific Advisory Board of the Laboratory “Ethics, Bioethics and citizenship” (EBC), University of Naples Federico II. Giuseppe Maiello (1962) is an Associate professor in Netnography and Digital anthropology at the University of Finance and Administration. He is the author of four monographs and several studies published in Czech and international scientific journals and books. Emiliana Mangone is Associate Professor of Sociology of Culture and Communication at University of Salerno (Italy), Department of Political and Communication Sciences. She is a Director of “Narratives and Social Changes-International Research Group” (2020-2026) and she was also a director of the International Centre for Studies and Research “Mediterranean Knowledge” (2015-2020). Her main investigative interests are in the field of cultural and institutional systems, with particular attention to the social representations, relational processes, and knowledge as key elements to the human act, and in migration studies. She recently published: Beyond the Dichotomy between Altruism and Egoism. Society, Relationship, and Responsibility, Information Age Publishing, 2020; Social and Cultural Dynamics. Revisiting the Work of Pitirim A. Sorokin, Springer, 2018; (with G. Masullo & M. Gallego, eds), Gender and Sexuality in the Migration Trajectories. Studies between the Northern and Southern Mediterranean Shores, Information Age Publishing, 2018. For further information see personal page: https://emilianamangone.com/. Daniele Mantegazzi is assistant professor in Economic Geography at the University of Groningen (The Netherlands), where he teaches courses in Real Estate and Land Supply, Economic Geography, Spatial Econometrics and Real Estate Research. He holds a PhD in economics from the Università della Svizzera italiana (Lugano, Switzerland) and was previously a post-doc researcher at the GSSI. His research interests include topics in the area of regional economics, economic and political geography, as well as formal and informal institutions, with a particular interest on spatial spillovers.

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About the Contributors

Giuseppe Masullo is Associate Professor of General Sociology and directs the International Lab for innovative social research (ILIS), an interdisciplinary research center which promotes theoretical, epistemological and methodological advances in the field of social sciences through constant dialogue with national and international scholars. His research interests concern social representations, health and disease, cultural dynamics in care relationships; disadvantages deriving from intertwining conditions of psychological and social vulnerability, such as that of migrant women employed in care work, and of LGBT migrants. Other areas of research concern the study of the young condition, with a particular focus on cultural models and lifestyles in relation to the dimensions of health, body and sexuality. Alfredo Matrella, PhD student in methodology of social sciences, Co.Ris department (Sapienza University of Rome). Main research interests: digital methods, methodology of social research, social organization, mixed approaches and evaluative research. Sergio Mauceri is a PhD in Methodology of Social Sciences, is Associate Professor in Methodology of Social Research (Sapienza University of Rome, Department of Communication and Social Research). He is member of the National Board of the Methodology Section (Italian Sociology Association - AIS). He is author (with G. Gobo) of an international book on survey research: Constructing survey data. An interactional approach (Sage: 2014). He is also author of numerous articles on methodological and sociological topics, published in national and international scientific journals. Salvatore Monaco is a Postdoctoral Researcher in Sociology at the Faculty of Education, Free University of Bozen - Bolzano, where he teaches “Placing gender and sexuality”. He achieved the PhD degree in “Social Sciences and Statistics” at the Department of Social Sciences, University of Naples Federico II. He is currently a researcher within the PRIN project: Constructions of Parenthood on Insecure Grounds (COPING). He is a researcher of Osservatorio LGBT and OUT (Osservatorio Universitario sul Turismo) of University of Naples Federico II since several years. His research interests concern social exclusion, tourism, urban contexts, technologies and new media, with particular attention to issues related to identities, genders, sexual orientations, generations. Maurizio Napolitano is head of the unit Digital Commons Lab at Fondazione Bruno Kessler - Digital Society Center open data and geospatial technologist. Jacopo Nocerino, bachelor’s degree in political sciences and International Relations, graduating in Digital Sociology and Web Analysis at the Department of Social Sciences, University of Naples Federico II. His main interests range around social phenomena and how they intertwine with social media platforms, digitalization of the society with particular attention to how digital is affecting modern societies. Peter Öhman is Professor of Business Administration. His research focuses on the relationship and behavioural issues, primary in private and public accounting and auditing, but also in the banking and property industries. Professor Öhman has published a significant numbers of scholarly articles in leading academic journals including Contemporary Accounting Research. He is the director of The Centre for research on Economic Relations (CER) and the head of the subject of Business Administration at Mid-Sweden University.

clxxviii

About the Contributors

Giuseppe Padricelli is currently a PhD student in Social science and statistics at University of Naples Federico II. His research interests regard the ethnographic approaches tied to new methods for the social research and the collective action in digital scenario, mainly focused on the relation between the organizational dynamics of social movements and new media sphere. Jessica Parola, bachelor’s degree in Sociology, graduating in Digital Sociology and Web Analysis at the Department of Social Sciences, University of Naples Federico II. Her main scientific interests ranging around digitalizzation of social and cultural phenomena with particular attention to how digital platforms and artificial intelligence affecting subjectivities and social processes. Raquel Poy is Associate Professor at the Faculty of Education in the University of León (Spain). She has developed her career mainly at the University of Salamanca and the Autonomous University of Madrid. She has been visiting professor in universities such as Montpellier, Évora, or Tec of Monterrey. Her main lines of research focus on contemporary problems in education, including cyberbullying or the training of experts in engineering and cybersecurity. She is a member of the Research Institute of Applied Sciences in Cybersecurity of Leon. She has conducted recent research on “Assessment of the Recent Postgraduates in Cybersecurity on Barriers and Required Skills for Their Early Career.” Ilaria Primerano is research fellow in Social Statistics at the Department of Political and Social Studies, University of Salerno (italy). Her research interests concern the analysis of complex data structures through the use of Multidimensional Data Analysis and Social Network Analysis with applications in economic and social fields. She is author of articles in national and international journals. Riccardo Pronzato is a research and teaching assistant at the Department of Communication, Arts and Media at IULM University (Milan, Italy), where is currently conducting a PhD research within the doctoral program Communication, Markets and Society. Previously, he obtained a summa cum laude master’s degree (MSc) in Sociology and Social Research from the University of Trento (Trento, Italy). Currently, his major research interests cover digital sociology, critical algorithm studies, everyday life, as well as socio- narrative approaches. He has already been accepted as an author to several conferences and got published in both academic journals and books. Massimo Ragnedda (PhD) is a Senior Lecturer in Mass Communication at Northumbria University, Newcastle, UK where he conducts research on the digital divide and social media. He is the co-vice chair of the Digital Divide Working Group (IAMCR) and co-convenor of NINSO (Northumbria Internet and Society Research Group). He has authored fourteen books and more than 70 articles and chapters in English, Italian, Spanish, Russian, and Portuguese. His books include: Enhancing Digital Equity. Connecting the digital underclass (Palgrave, 2020); Digital Capital. A Bourdieusian Perspective on the Digital Divide (with Maria Laura Ruiu), Emerald Publishing, 2020; Digital Inclusion. An International Comparative Analysis (co-edited with Bruce Mutsvairo), Lexington Books 2018; Theorizing the Digital Divide (co-edited with G Muschert), Routledge (2017); The third Digital Divide: a Weberian approach to Digital Inequalities (2017), Routledge; The Digital Divide: The Internet and Social Inequality in International Perspective (co-edited with G Muschert) (2013), Routledge.

clxxix

About the Contributors

Elisabetta Risi is Research Fellow at IULM University (Milan). Previously, he obtained a PhD in Information Society from the Bicocca University of Milan. She has been teaching sociology and communication sciences for several years and she carries out research at the Communication, Arts and Media Department at IULM University. Her current mayor research interests cover the role of platforms in daily life, the issues of contemporary work, as well as digital and qualitative methods. She has participated and organized workshops and conferences in various universities and is the author of numerous papers on the relationship between communication practices, subjectivity and social change, as well as on current forms of job insecurity. Carlos Rodríguez-Hoyos has a degree in Education and Doctorate awarded by the University of Oviedo. He has been a lecturer in the Department of Education at the University of Cantabria since 2009. His lines of research include: the analysis of e-learning from a teaching perspective, ICTs and the dynamics of educational and social inclusion and exclusion. He has taken part in several national and international conferences, and has published various articleS. He has also collaborated on articles in several books. Giovanna Russo, PhD in Sociology and Political Sciences, currently is Senior assistant professor (fixed-term) in Sociology of Culture and Communication (qualified Associate professor) at the Department of Educationl studies “G. M. Bertin”, Alma Mater Studiorum - University of Bologna. Her main research interests are: wellbeing culture, practices of sport and physical activities, wellness consumptions, leisure time and migration. Last publications: 2020, Fitness in Italy: body culture, well-being and active lifestyles (with A. Borgogni, S. Digennaro), in Scheeder J, Helsen K. (eds), The Rise and Size of the Fitness Industry in Europe. Fit for the future?, Palgrave Macmillan, Cham; 2018 (ed.), Charting the wellness society in Europe. Social transformations in sport, health and consumption (FrancoAngeli, Milan); 2019, Integration by Sport Activities: Resource or Only a Paradox?, “Journal of Mediterranean Knowledge JMK, 4 (1). Barbara Saracino (PhD in Methodology and Social Research) is Senior Assistant Professor of Sociology at the Department of Political and Social Sciences of the University of Bologna. Her main research interests deal with Methodology and Sociology of Science. She is expert in investigation techniques and analysis of both quantitative and qualitative data. She is member of the steering committee of the research centre Observa Science in Society and also coordinator of Science in Society Monitor. Monica C. Scarano is an assistant professor in marketing at the Catholic University of Lille. She works in the field of consumer behavior and consumer culture theory. Her thesis dealt with customer network dynamics and modification, using a multimethod approach focused on product and brand circulation. Rosa Sorrentino, graduated in Social Science and Communication, is currently a PhD Student in Social Science and Statistics at University of Naples “Federico II”. Antonio Tintori is a sociologist, Ph.D in Economic geography, Italian CNR researcher and teacher of Methodology of Social Sciences at Sapienza, University of Rome. His key competences include coordination and participation in national and international research projects, quantitative-qualitative analysis of attitudes and behaviours of the population in psychosocial and economic fields, definition of indicators of deviance and well-being. He is the author of numerous scientific publications and books. clxxx

About the Contributors

Roberta Tofani, bachelor’s degree in Sociology, graduating in Digital Sociology and Web Analysis at the Department of Social Sciences, University of Naples Federico II. Her main scientific interest lies in the transformation of digital society, with a focus on the digitization of social, cultural and communicative practices. Sara Tomasiello graduated in ‘Professional educators and continuing education experts’ in 2020 at the Universita’ degli Studi di Salerno presenting her dissertation on an in-depth research on gender study, specifically around the topic of misogyny. She is now enrolled in a Masters in ‘Clinical Pedagogy’ at the Centro Psicopedagogico Kromata in Brescia. Currently she is an educator in a Psychiatry community working with people affected by mental disabilities. She provides support and expertise for social reintegration while working on educational projects; she also has experience in high school environments, supporting teenagers with social and mental difficulties as well as young people affected by disabilities. Domenico Trezza, PhD in Social Sciences and Statistics, is a research fellow at the Department of Social Sciences, University “Federico II” of Naples. Lucia Velotti, Ph.D., is an Assistant Professor in Emergency Management and Disaster Science at John Jay College of Criminal Justice, Department of Security, Fire and Emergency Management, City University of New York (CUNY), New York City, NY. Dr. Velotti’s s experience in the field of disaster and emergency management is both national and international. Dr. Velotti has carried out several field studies in Haiti, Japan, the Netherlands, Italy, and Oklahoma, Alabama, North Carolina in the U.S following disasters concerning earthquakes, tsunamis, tornadoes, and floods. Dr. Velotti has been awarded several grants from the National Science Foundation (NSF), the Disaster Prevention Research Institute (DPRI), Kyoto, Japan, and the City University of New York. Dr. Velotti graduated from the University of Delaware, where she was a research assistant at the world-renown Disaster Research Center (DRC). Dr. Velotti is the co-lead of the Federal Emergency Management (FEMA) Special Interest Group (SIG) on Leadership and Service Learning and co-chair of the International Research Society on Public Management (IRSPM) panel on Emergency Services. Dr. Velotti is also a member of the following interdisciplinary research groups within the Converge Covid - 19: Risk Communication in Concurrent Disasters and Bridging Needs with Research through Action-Oriented Community Design funded by the National Science Foundation (NSF). Amanda Vettini’s key academic interests are higher education, pedagogy and research methods. She has worked in a range of research settings including academic, Government and for a private research company. She has also taught Sociology and social research methods at two universities in Edinburgh, the University of Edinburgh, and Edinburgh Napier University. Maria Prosperina Vitale is associate professor in Social Statistics at the Department of Political and Social Studies, University of Salerno (Italy), where she teaches Statistics, Sampling Techniques and Social Network Analysis. She has her own primary research interests in survey data analysis and social network analysis, where she has deepened the study of the structure of a social network through multidimensional data analysis techniques.

clxxxi

About the Contributors

Gloria Ziglioli is a young Italian social researcher, currently into the PhD program at the Department of Sociology and Social Social Work at University of Agder (Norway). Since 2017 she has been studying the changes regarding the elderly care policies and services in the modern welfare states, along the active ageing paradigm and the welfare technology/eHealth trajectories. She is now researching the governance dynamics of the local innovative partnership which involve public, for-profit and non-profit actors for developing and sustaining digital care services. She has recently published for an Italian journal, and she has presented her works in different conferences (ESPAnet). The research interest around online research, netnography and other digital method is recent, but quite significant.

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clxxxiii

Index

A access-to-data 65, 71-72, 75-76 Adolescents 101, 132, 140-141, 144, 147, 244, 295311, 314, 329, 896 Advocates 76, 727, 730-731, 734-736 Agency 3, 15, 21, 61, 145, 159, 251, 268-269, 295-296, 311, 349, 357, 425, 493, 533, 577, 595, 597-600, 607, 629, 658, 685, 798, 845, 847, 857, 859, 862, 882, 892 algorithm awareness 265 Algorithms 4, 9, 36, 43, 151, 162-163, 166-169, 171172, 176-179, 181-187, 189-193, 206, 217-218, 232, 265-269, 271-275, 277-279, 326, 340, 346, 348-350, 352, 354-356, 358, 364, 366, 402, 488489, 493-494, 500-504, 508, 532, 538, 829 Amazon Rekognition 167 API closure 149, 154 art institutions 800-801, 811, 813, 818 Artificial Intelligence 43, 131, 161-163, 166-169, 171-175, 177, 194, 217-224, 293, 365, 369, 533, 845, 857 Association of European Historians 746-747, 752753, 757, 759 audio-diary technique 375-378, 386, 389 Authentication 146, 854, 856-857, 866 auto-ethnography 112, 260, 265-266, 268, 270-273 Availability 30, 47, 57, 74-75, 118, 151, 177-178, 180, 183, 233, 267, 348, 355, 369, 401, 488, 502, 527, 573, 674, 690, 693, 749, 811, 840-842, 849, 854, 856, 858, 866, 898-899, 901, 914 Avatar 91, 241, 278, 617, 794, 798-799

B Bank Customer Online Communities 287, 294 bank customers 280-281, 284-289, 291, 294 banking industry 280-281, 285-287, 289, 291, 294 Beta writer 161, 168-169

Big Data 1-2, 4, 6-10, 20, 24, 27-32, 35-40, 42-45, 47-48, 50-53, 55, 65-66, 77, 79-80, 85, 127, 129130, 151-153, 155, 157, 159, 161-163, 173-174, 177, 192-193, 211, 218, 221, 224, 239, 241, 246, 276-277, 279, 283, 326, 340-342, 352, 358, 369, 488-489, 491, 493-494, 500-506, 508, 544, 571572, 574, 582-583, 585-587, 686-691, 700-705, 707, 740, 763, 856-857, 895 bisexual people 831, 885 Blog 39, 51-52, 84-85, 109, 205, 210, 369, 373, 524, 615, 748, 808, 811, 817 Botnet 77, 866 British newspaper 727

C cancel culture 708-713, 715-716, 718-725 Celtism 615 CIDAN 854, 866 Citizenship 14, 17, 54, 121, 205, 342, 425, 629, 873, 909 classical ethnography 45, 248, 262 Climate Change 73, 662, 727-744, 914 close reading 749, 755, 763 cognitive maps 867, 870-877, 879-880, 882-884 collaboration networks 526 collaborative ethnography 304, 311-312, 317 collaborative mapping 526, 528 Communities 19, 33-34, 37, 39, 85, 89, 91, 113, 117-118, 128, 136, 165, 196, 199-200, 203-212, 214, 228-232, 234, 236-238, 244, 247, 250-252, 258-259, 261-263, 267, 284, 286-287, 289-291, 294, 296, 298, 318, 321, 327, 340, 342, 350, 361, 365-366, 368-369, 371, 376, 406-407, 410, 412, 414, 419-421, 497, 508, 510, 513, 522-526, 528, 531-534, 539, 542, 544, 551, 553, 565, 573-575, 577, 583-584, 587, 594, 615-617, 623-624, 629630, 663, 666, 711-712, 748, 761, 783, 805, 810, 817, 826, 883, 891, 896, 909-910 Comparative Approach 176  Volume I: 1-441; Volume II: 442-919

Index

Confidentiality 32, 55-56, 137, 238-239, 298, 300, 840-842, 845, 854, 866 conjuncture 883 Connectivity 362, 366, 587, 725, 829, 844, 857-858, 866 Consumer Culture 425, 800, 818 Content Analysis 8, 29, 128, 152, 155, 193, 208, 252, 286, 319, 324, 326, 329, 342, 346-358, 388, 399, 401, 421, 424, 488, 494, 501, 503, 506, 515, 576, 593, 603, 664, 738, 740, 764, 769-770, 786 Content Management System 746, 750, 763 COVID-19 26-27, 36-40, 72-73, 78, 83-85, 105, 107, 109, 112-118, 125-131, 144, 184, 194, 231, 281, 285, 318-321, 326, 330, 333, 335, 337-338, 358, 375, 377-378, 385-387, 389-392, 395, 398, 401402, 524, 572, 575-576, 578, 580, 583-585, 587, 633, 658-659, 664-665, 667-668, 679, 685, 687, 698, 702, 706, 788, 814, 841, 862, 864, 889, 895 COVID-19 vaccine 572, 583, 664, 667-668 Creative and Participatory Methods 295 crime opportunity 867, 877 Crime Prevention 867, 871, 878-879, 882 crime situation 867, 869, 871, 877 Criminology of Place 867, 870, 875, 883 Criminometry 867, 869-870 critical infrastructures 840, 854, 857 critical pedagogy 265, 270, 272, 278 crowdsourcing 531-532, 544, 546 Cyber Ethnography 34, 196, 204, 282 cybercrime 840, 842, 845, 851-852, 854, 860, 862-863 cybersecurity 840-846, 848-852, 854-866

D data access 67, 69-72, 75-76, 79-82, 84-85, 149-150, 156-157 Data Driven Approach 149, 628 Data Goodness 176-177 DDoS 866 Delphi 33, 38, 390, 392-403 deniers 710, 727-729, 734-736 digital banking 280-281, 284-286, 288-290, 293-294 digital diaries 380 digital ethnography 24, 29, 34, 37, 40-41, 43-45, 47, 51-52, 76, 153, 196, 203-209, 211, 215-216, 234, 243, 245-246, 248, 251-252, 255, 258-264, 268, 274, 276, 278, 282, 295-297, 306, 311-316, 404, 408, 421, 424-425 Digital Governance 65 digital history 746-749, 759-760, 762-763 Digital Humanities 50, 79, 313, 319, 506, 703, 746748, 751, 754, 759-763 clxxxiv

Digital Media 12, 29, 74, 85, 210, 240-241, 245, 266, 273, 275, 309, 319, 341, 392, 405, 407, 506, 524, 570, 665-666, 724, 759, 800, 818 Digital Methods 1, 4-7, 10, 19-22, 24, 26, 31, 37, 40, 42-44, 46, 48, 52, 66, 76-77, 79, 116, 127-129, 149-152, 155, 159-160, 176-177, 196, 209, 212, 215, 231, 234, 246, 248, 251-252, 254, 264, 267, 275, 279, 318-320, 323, 325, 338-340, 342-343, 346-347, 351-352, 358, 407, 409, 425, 507, 510, 515, 521-522, 586, 589, 593, 607, 613, 628, 633, 658, 662, 720, 749, 764, 891, 899, 918 digital platform social data 346-350, 353-355, 358 digital research 4, 7, 11-12, 17-20, 42-43, 65-66, 75, 80, 125, 149-151, 153, 156-157, 204, 244, 249, 355, 421 digital research methods 11-12, 17-20 Digital Scenario 118, 248-251, 253-254, 261-262, 346-352, 355, 490 Digital Society 24-26, 32-33, 37, 42, 53, 151, 249, 346, 357, 390, 392, 395, 402, 404-405, 488, 571-573, 836, 841, 885-886, 888, 898 Digital Sociology 3, 9, 40, 80, 151-153, 159-160, 227, 246, 269, 277, 319, 341-342, 346, 358, 424, 506, 574, 586, 613, 663 Digital Traces 45, 47, 177, 207, 210, 239, 241, 247, 515, 522, 571, 574, 585, 689, 899 digital visual cartographies 295-296, 299, 307-311, 317 Digitalisation 90, 280-281, 285, 289, 402, 749 digitized methods 898-899 Discourse Analysis 230, 566, 627-628, 633-635, 652, 655, 660, 662, 723, 871 distant reading 749, 755, 758, 763 Dynamic 28, 152, 234-235, 251, 259, 352, 370, 490, 506, 551, 572, 592, 600, 692, 705, 765, 767, 800, 810, 814, 883

E Egocentric 365-366 Empirical Research 11-12, 15, 23, 57, 127, 364, 386, 573, 769, 892 empirics 11, 13, 15, 18 Epistemology 1-3, 6-7, 38-39, 66, 193, 212, 243, 277, 357, 393, 407, 422, 762, 883 Ethics 20, 34, 36, 48, 50, 54-57, 59-64, 67, 112, 123, 224-225, 236, 238-239, 243-246, 258, 262, 298, 316, 340, 600, 633, 722-723, 859, 865, 891, 896 ethics of disaster research 112 Ethnography 19, 24, 29, 33-34, 37-38, 40-45, 47, 5152, 76, 153, 196-200, 203-209, 211-216, 227, 229-230, 232-234, 242-246, 248-253, 255, 257-

Index

264, 266-268, 272, 274-279, 281-283, 290-291, 294-297, 304, 306-307, 311-317, 338, 388, 404, 408, 421, 424-425, 494, 508, 576, 593, 614, 796, 805, 890, 894 ethnography in evolution 248 exosystem 820, 824, 839 Extimacy 299, 317

F F2P 789-790, 798 face detection system 161-162, 168 Facebook 6, 23, 25, 34, 67-70, 72-73, 75, 77-85, 92-93, 95, 101, 110, 117, 150, 152-158, 160, 168, 204, 206-210, 231, 235, 247, 250, 252, 275, 290, 293, 314, 329, 340, 348, 361, 369, 371, 386, 410-412, 414-416, 490, 506, 511-512, 535, 544, 565, 568, 574-575, 584, 615, 617-621, 624, 631, 662-663, 667, 669, 722-723, 764, 769, 771, 796, 813, 815, 828, 830, 832-837, 888, 894, 896, 899 Fake news propagation 571 Financial service research 280 fitness 115, 404-412, 415-417, 420-423, 426 food economy 589-590, 595, 599-600, 602, 607, 609 Forum 203, 205, 209-210, 231, 235, 244, 282, 294, 607, 612-613, 615, 620-621, 665, 704, 761, 786, 843, 861, 865 freemium games 788 Future Scenarios 125, 355, 390, 392-395, 589, 592, 600, 607, 612

G Game Studies 788, 796, 798 gay men 885, 889, 897 gender minority 885, 888 gender roles 764, 768, 775 gender studies 783, 788 gender violence 764, 767-771, 780, 783 geodata 526, 531-532 Geo-located photo 686 Geo-tagged Twitter 488, 505, 508 governance of the science 54 Grounded Theory 53, 213, 378, 388-389, 867, 870872, 880-881, 889 guilds 790, 792-793, 798

H Heterostereotypes 790, 798 hidden population 885-887, 895

history of Europe 746, 752-753, 755, 757 history of historiography 746

I ICTs 196, 203 Identity 12, 25, 34, 42, 141-142, 203-205, 208, 210, 218-219, 230, 239, 243, 249-250, 252, 276, 296, 312, 314-316, 320, 322, 327-329, 337, 361, 391, 398, 416, 524, 566, 568, 572-573, 586, 615-618, 622-628, 659-660, 662, 669, 712, 718, 723, 739, 748, 753, 755, 757, 766-769, 780, 789, 792-794, 797, 800-804, 809-812, 814, 816, 818, 821-824, 826, 830-832, 834-837, 839, 862, 873, 883, 885, 887-888, 890, 898 Imagined Futures 402, 589-591, 593, 612, 614 IMPACT system 161-162, 165 impersonalisation 281, 285, 289-290, 294 Indicators 28, 75, 96, 98, 107, 139, 162, 351-352, 366, 400, 415, 492, 543-544, 584, 689, 703, 898-899, 902, 904-917 Industry 4.0 840 informal learning 510 in-game marketing 788 Instagram 6, 25, 34, 68, 82, 154, 207, 209-210, 252, 278, 301, 328-329, 361, 369, 410, 414-416, 424, 490, 511-512, 566-569, 575, 584, 618, 627-628, 633, 636, 658-660, 662, 667, 690-691, 812-813, 828, 830, 888, 894 Integrity 54-56, 58-60, 62-64, 82, 134, 239, 840-842, 854, 866, 881 international documents 58 Internet Studies 27, 78, 297, 346-347, 531 intertextuality 627, 634, 636, 647-648, 650-651, 661 Irony 189, 627-628, 634-636, 644-647, 652, 655, 657-658, 661-663 issue network 65-66, 69-71, 75-76 Italian Paganism 615

L lesbian women 835, 885 LGBT 371, 714, 718, 820-821, 834-835, 885-890, 892-895, 897 Likert Scale Questions 148 Lithium-Ion Batteries 161, 168-169, 173-174 Loot Box 798

M Machine Learning 85, 128, 161-163, 168-169, 171clxxxv

Index

174, 176-181, 183, 185, 190, 192, 194, 218, 278, 349, 355, 358, 493, 503, 507, 533, 538, 544, 547, 582-583, 684, 704 macrosystem 820, 824 Making-Meaning 708 manual coding 627-628, 636, 645, 649, 656 Mapping 66, 206, 228, 233-235, 309, 312-313, 321, 325, 330, 362, 369, 504-505, 508, 526-529, 531, 533-534, 541-542, 544-546, 662, 703, 752, 759, 762, 867, 870, 879, 881, 883 Maps 8, 125, 308-309, 322, 351, 526-528, 530, 535536, 544-546, 698-699, 742, 750-751, 758, 839, 863, 867, 870-877, 879-880, 882-884 memes 522, 627-636, 638-639, 641-642, 644-653, 655-663, 721, 724 mesosystem 820, 824, 839 Method 1-6, 8, 13, 18-19, 27, 32-35, 38, 42, 45, 61, 90, 106, 116, 122-123, 129, 131-134, 140, 144145, 153, 155, 169, 171, 183, 194, 197, 202, 211-212, 219-220, 228, 231-234, 236, 241, 243, 246, 248-249, 251-254, 258, 260-262, 265-268, 270, 272, 274-278, 280-284, 286, 288-290, 294296, 299, 301, 303, 305, 311, 314, 317, 320, 339, 347, 350, 356, 364-366, 372, 375-378, 382-383, 386-390, 392-393, 395-402, 407-409, 421, 423, 503, 515, 532, 551, 554, 566, 569-570, 577, 581, 612, 619, 670, 688, 690, 709, 712, 728-729, 754, 757, 769, 796, 802, 849, 870-871, 875, 877, 883, 886, 898-899, 914 Methodological Challenges 149, 196, 274, 503, 523, 590, 893 Methodology 9, 17-18, 24, 38, 68, 102, 106-107, 109, 118, 127, 129, 140, 145, 160, 197, 205, 212, 221, 232, 234, 236, 246-247, 254, 261, 274-275, 296-297, 313-314, 316, 357-358, 360-362, 371, 380, 382, 384, 392-394, 404, 424, 488, 490, 494, 502, 506, 592, 614, 633, 636, 644, 658, 688, 691, 749-750, 752-754, 759, 763, 768, 786, 868, 880, 892, 894 microsystem 820, 825, 839 microtransactions 790-791, 797-798 Minorities 161-162, 165, 210, 711, 714, 717-718, 885-889, 892 mirror method 295-296, 299, 301, 317 Mixed Methods Research 23, 88, 96, 102, 104, 350, 357-358, 373, 423-425 MMORPG 789-790, 798 moral panic framework 727-728, 734 Moral Panics 341, 727-730, 732, 734-735, 738-739, 741-743 multilevel survey 88, 105 clxxxvi

N Narration 196, 210, 219, 272, 318-321, 325, 339, 343, 387, 415, 422, 623, 639, 653, 744, 759, 873 Narratives 18, 22, 208, 210-211, 217, 219-220, 222, 228-229, 231, 241, 265-266, 268-270, 272-273, 300, 308, 310, 320, 323, 326, 336, 339, 341, 375376, 573, 589-590, 593-595, 599-600, 607, 609, 611, 614-615, 727-729, 732-736, 747, 752-753, 800, 810, 814, 818, 870, 877 Netnography 19, 22, 27, 32-37, 39, 42-44, 196, 204, 212-214, 227-234, 236, 241-245, 247, 251, 255, 258-264, 280-284, 286-294, 424, 615, 788-789, 796-797, 799, 894-895 Network Analysis 18, 29, 84, 127, 227-228, 231-235, 242-243, 246-247, 251, 267, 293, 324, 360-366, 369-373, 544, 571-572, 574-577, 582-588, 593, 601, 613-614, 660, 746, 754-755, 760, 763 Networking online 227, 243 News Media 82, 155, 727, 738, 740, 743 Nodes 39, 182, 214, 231, 233, 323-324, 332-333, 336, 365-369, 528, 534, 574, 577, 582, 754-755, 826, 842 non-repudiation 854, 866

O Omeka 746, 750-752, 754, 758-763 Online Communities 33-34, 37, 196, 204, 206, 208, 212, 228-230, 232, 236-238, 244, 247, 250, 258259, 261, 263, 284, 286-287, 289, 291, 294, 321, 342, 368-369, 414, 420-421, 534, 573, 575, 615, 617, 630, 783 Online Ethnography 204, 207, 211, 216, 227, 234, 890 Online Platforms 65-72, 74-76, 78-80, 82-83, 85, 137, 208, 268, 512 Online Research 88, 111, 145, 150, 214, 227, 232-233, 236, 239, 242-244, 246, 288, 613, 891 Online Social Networks 106, 231, 244, 360-361, 367, 369, 371, 574, 584-585 online social spaces 204, 211, 227, 232, 234, 236-238, 240, 247 online surveys 58, 91, 105, 109, 131-133, 135-139, 142, 145-148 Openstreetmap 309, 504, 526-528, 531-533, 535-542, 544-547, 706 Opinion Analysis 178, 664-665, 670, 676-677, 681 OS 133, 136-141, 144, 148 OSM 526-529, 531-538, 541-544, 547 Otherness 873, 883 Ottoman Empire 789-790

Index

P P2W 788, 790-791, 796, 798 paganism 615, 618-621, 626 Panel 33, 88, 94-96, 102-104, 106, 108, 134, 145-146, 390, 392-393, 396-401, 536, 599, 607, 628, 634, 642, 687, 701, 705-706, 903 Partecipatory Culture 318 participant field notes 295-296, 299, 303-307, 317 Person 55, 114, 116, 168, 217-224, 308, 315, 317, 361, 365, 369, 375-377, 382-383, 385-386, 407, 411, 413, 549, 561-563, 617, 639, 642, 655, 668, 725, 793, 808, 810, 823-825, 833, 836, 866, 886, 892, 911 Pilot Study 88, 100-102, 104-105, 135, 504, 686, 688, 691 plugin 750-751, 755, 758, 763 Police 61, 167, 258, 491, 851-852, 858, 861, 867-871, 873-879, 882 political agenda 199, 390 political correctness 708-714, 717, 720-725 pop culture 627, 629 post-API 65-66, 77-78, 80, 149, 160, 267, 277 Post-API Era 149 Principles 22, 54-58, 60-64, 182, 196, 207-208, 214, 228, 239, 246, 258, 263, 278, 295, 297-298, 354, 388, 397, 399, 401, 406, 548-549, 551-552, 554, 559, 620-622, 767, 789, 795, 799, 828, 843-844, 866, 917 Processing 18, 26, 31, 43, 48, 81, 102, 130, 134, 163, 167, 169, 171, 176-179, 191, 193, 241, 269, 319, 321, 328, 330, 333, 335-336, 338, 349, 352, 531, 536, 586-587, 703, 747, 749, 760, 763, 856 production of knowledge 30 professional development 510-511, 514, 522-525 professional learning networks 510-511, 514, 522, 524 Prosumption 800, 810-811, 817-818 Public Values 10, 23, 65, 76, 80, 279, 508

Q Qualitative 11, 18-19, 21, 26, 31-33, 35-36, 38-39, 42-45, 47, 51, 57, 63, 75-76, 94, 96-98, 104, 108, 127-128, 135, 144, 146-147, 150, 152, 156, 159, 196, 208, 211-216, 221-222, 227-229, 232, 234236, 241-244, 247, 250, 261-263, 268-271, 273, 275-279, 282-283, 286, 288-297, 303, 311-316, 325-326, 342, 346, 348, 351-354, 356-358, 360, 364-366, 369, 371, 375-377, 388-390, 396, 398399, 407-410, 416, 420-421, 423-425, 491, 494, 515, 530, 547, 566-567, 576, 589-590, 592-593,

607, 612, 616, 627-628, 633, 659, 661, 748, 788, 796, 821, 832, 854, 869, 871, 875, 880, 885-886, 889-890, 893, 895, 897 qualitative internet inquiry 227 Qualitative Methodologies 390 Qualitative Methods 196, 213, 228, 234, 243, 282-283, 286, 290, 312, 315, 348, 366, 388, 408, 420, 424, 494, 515, 628, 869 Qualitative Research 26, 38, 43, 51, 57, 63, 104, 146, 212-213, 215-216, 232, 241-244, 262-263, 268, 270, 275-278, 283, 288, 290, 294, 296-297, 311316, 326, 357-358, 360, 377, 388-389, 408, 416, 423, 425, 576, 612, 659, 661, 854, 880, 886, 890, 895, 897 Quantitative Research 21, 27, 57, 102, 140, 289, 296297, 326, 338, 353, 360, 886, 889

R Recruitment 378, 885-886, 889-890, 892, 894-897 Reflexivity 14, 17, 54, 59, 61, 152, 260, 262, 277, 295, 304, 307, 316-317, 388, 721, 773, 775, 794, 897 Regulations 36, 54, 56, 151, 168, 620, 710, 855, 866, 892 Research Methods 2, 7, 11-12, 17-20, 24, 64, 81, 108-109, 126-127, 147, 150-152, 177, 206, 214, 242-244, 270, 276, 280-281, 283, 287-289, 291, 299, 314-315, 348, 357, 371, 408, 425, 522, 567, 613, 804, 840, 854, 860, 865, 885, 897, 899, 917 Response Rates 102-103, 107-108, 131-132, 138-142, 144-148, 281, 889, 891 Responsibility 16, 54-55, 57-60, 62, 76, 79, 123, 223, 239, 320, 382, 396, 420, 735-736, 740, 849, 903, 907 RPG 788-789, 791-795, 797-799

S Sampling 30-33, 43, 90, 100, 102, 109, 116-119, 137, 209, 240, 261, 274, 290, 351-353, 378, 684, 832, 885-887, 889, 892, 894-896, 900, 903 scenario planning 391-392 secondary data sources 898, 900, 906 Security Controls 866 Selfie 423, 660, 662, 800-801, 810-811, 813, 817-818 semiotic analysis 548, 551, 556-557, 560, 563, 566567, 569, 592 Semiotics 276, 548-557, 560-561, 563-570, 804 Semiotics Resources 554, 570 Sentiment Analysis 29, 152, 176-183, 190, 192-194, 664, 670, 676, 685, 899 sexual market 820, 830, 833, 839 clxxxvii

Index

sexual minority 885, 887 Sharia 795, 798 Situation 48, 50, 62, 66, 94-95, 104, 114-116, 118, 120-126, 132, 137-138, 162, 164-166, 184, 187, 191, 200, 206, 208, 237, 267, 320, 327, 330, 339, 376, 384, 391, 551-552, 572, 617, 634, 639, 641642, 644, 646-647, 653-655, 667, 671, 684, 702, 708, 729, 783, 794, 812, 844, 866-872, 875-878, 883, 886, 911, 914 small data 27, 31-32, 37, 39, 42-43, 45, 47-48, 76, 127, 157, 241, 244, 318, 325-326, 340, 354, 356 Smartphone and Apps 404 Social Big Data 571-572, 574, 582-583, 587, 703 Social Capital 365-367, 370, 372-373, 624, 807, 826, 906, 908-910, 915, 917-919 social distancing 26, 84, 115, 118, 321, 390, 402 social engineering 840, 843, 848, 851, 858, 866 Social Impact 118, 318, 375, 825, 843 Social Love 898-899, 909-915 Social Media 5-6, 25, 28, 40, 44, 66-68, 70, 76-77, 7981, 84-85, 93, 117, 126, 134, 137, 150-152, 154, 157-160, 177, 191-193, 196, 203-204, 206-209, 212, 215, 228, 232-235, 237, 241, 245-247, 250, 258, 260, 263, 266, 272-273, 275, 277-278, 280283, 285-288, 294, 299-300, 309, 311, 313-314, 316-317, 321, 348, 353, 355, 358, 360-362, 366367, 369, 405-410, 412-415, 417-424, 488, 490, 503-508, 510-513, 515, 523, 525, 545, 548-549, 555, 558, 564-569, 571-578, 580-587, 593, 615616, 631, 633, 635, 650, 659-662, 664-665, 689, 704-705, 708-712, 720-725, 764, 785, 791, 796, 800-801, 809-814, 817-821, 828-834, 837-838, 844, 858, 886, 888-889, 895-897, 917 Social Media Semiotics 548 Social Network 25, 82, 101, 112, 150, 154, 156, 159, 173-174, 227-228, 231-235, 237, 242-243, 246247, 252, 263, 267, 293, 299, 301-303, 306, 318, 322, 324, 326-327, 338, 341, 360-367, 369-373, 375, 383, 535, 544, 569, 571-572, 575-577, 581588, 624, 688, 760, 796, 830, 888 Social Network Analysis 227-228, 231-235, 242-243, 246-247, 267, 293, 360-365, 369-373, 544, 571572, 577, 582-588, 760 Social networks mining 686 Social Research 1-2, 4-7, 9, 15, 18, 22, 24-30, 32-33, 35-37, 40, 42-44, 54-55, 63-66, 68-69, 76-77, 80-81, 88, 93, 97, 105, 107, 110-112, 116-118, 126-129, 144, 149-153, 157, 159-160, 162-163, 171-172, 177, 193, 212-213, 215, 227-231, 236237, 241, 244-245, 247, 249, 251, 263, 270, 274, 276-278, 282, 289-290, 307, 312, 314-316, clxxxviii

318, 321, 342-343, 347-349, 355-358, 361, 369, 376-377, 388, 390, 422-424, 489, 503, 527, 543, 572-574, 582, 586, 613, 625, 633, 769, 840-841, 843, 845-846, 848-851, 853-857, 885-892, 894, 898-899, 901-902, 914, 916-917 social research in the pandemic era 112 Social Research Methods 2, 24, 81, 152, 314, 348, 357, 840, 854, 899 Social Semiotics 276, 548-556, 560-561, 563, 565-570 social well-being 898-899, 906-907, 909-910, 914916, 918 Sociological Imagination 10-11, 15-17, 21-22, 585 sociological space 488, 492, 498, 502 Sociology 1-3, 5, 7-9, 11-23, 39-40, 42, 46, 53, 64, 80, 110, 147, 151-153, 157, 159-160, 162, 174, 204, 213-216, 227, 243-246, 263, 269-270, 276-278, 319, 340-343, 346, 358, 360, 362-364, 370-371, 373-376, 389, 420, 424, 489, 503-506, 508, 574, 586, 613, 625, 663, 739, 741-744, 765, 817, 821, 824-825, 844-845, 858, 861, 863-865, 915-917 startup 589-590, 592-595, 599-601, 608-609, 611-613 Stereotypes 241, 300, 320, 394, 764, 783, 788, 794795, 798, 809, 811, 814, 885, 887 Stereotyping 800, 810-812, 819 System 12, 14-15, 17-19, 22, 27, 31, 55, 59-60, 71, 78, 81-82, 88-89, 91, 96-98, 102, 110, 114, 121, 123, 128, 158, 161-163, 165-169, 173-174, 178, 197-198, 214, 223, 231, 244, 258, 281, 285, 320, 323, 336, 338, 349, 353-355, 361, 364-365, 369, 391, 394, 425, 505, 508, 512, 527, 533, 540, 543, 549-552, 557-558, 560, 562-563, 566, 584, 589, 592, 595, 601, 621, 623, 630, 665, 668, 684, 689, 692, 704, 746, 750-752, 763, 765-768, 777, 780, 783, 790, 812, 824-826, 829, 832, 839, 847-848, 858, 860, 866, 869, 875, 877, 883, 901

T Technology 1-2, 5, 7, 24, 26, 43, 55, 66, 85, 88, 91, 94, 106, 128, 136-137, 144, 153, 172-175, 193, 212, 217-218, 220, 223-224, 241, 243-244, 246, 250, 291-293, 296, 316, 320-321, 342, 357-358, 363, 365, 376-377, 383, 387, 390, 392, 404, 406409, 411, 422-423, 425, 507, 510, 512-514, 516, 518-519, 521-525, 529, 568-569, 612, 625, 659, 662, 689, 706, 723, 747, 749, 786, 805, 808-809, 811, 818, 827-828, 841-844, 849, 855, 857-865, 869, 890, 895-896, 900 Text Analysis 177-178, 190, 193-194, 525, 664-665, 669, 747 Textual Analysis 185, 708-709, 713

Index

the desire of knowledge 112 Theory 4, 7, 9-18, 21-23, 30, 38, 42, 51, 53, 146, 159, 193, 197, 213, 216, 275-277, 290-292, 311, 314315, 326, 341-343, 361-362, 364, 370-373, 378, 388-389, 394-395, 402, 408, 492, 502-504, 528, 545, 550-551, 566-567, 569-570, 578, 583, 612, 630-632, 658, 663, 705, 724, 737-738, 741-743, 803-804, 807, 814-815, 817-818, 821, 824-825, 836, 841, 854-855, 859-861, 863-864, 866-867, 870-872, 879-881, 889, 906, 909, 916-917 Threat 1, 17, 75, 113-115, 152, 711, 728, 730, 735-736, 843-844, 848, 851, 858, 862-864, 866 Ties 232, 234, 321, 361, 365-367, 370, 493, 558, 597-598, 803 Tourism Research 283, 686, 689-690, 703-706 tourist behaviors 686-688, 690-691, 695, 700-702 transgender 820-823, 831-839, 885-887, 892 transgender people 820-821, 823, 831-838, 885, 887 transposition of method 248 transsexual 823, 831, 834, 839 Twitter 6, 25, 34, 67, 69, 72-74, 77, 79-83, 85, 92, 152, 154-155, 158-160, 176, 193-194, 208-209, 244245, 247, 250, 263, 278, 301, 343, 348, 361, 488, 490-491, 493-494, 496, 503-516, 521-525, 535, 571-572, 574-580, 582-587, 589, 593, 607, 612, 615, 618-620, 631, 664-666, 668-669, 680-681, 708-709, 711-713, 715-716, 720-722, 725, 740, 813, 815, 830, 888, 899

U user experience 284-285, 287, 294

V

292-294 VGI 526-529, 532-534, 544, 689 videography 234 Virtual 25, 28, 34, 36, 39, 43-44, 89, 91, 95, 101, 115, 129, 147, 151-152, 160, 203-204, 212, 214-215, 227, 229, 232, 237, 242-243, 245-246, 248, 250-254, 258-259, 261-264, 268, 275, 277-278, 280, 282, 284-285, 287-291, 294, 296-297, 299, 301, 306, 309, 313-314, 321, 339-340, 348, 358, 361, 369, 374, 387, 392, 401-402, 405-406, 409410, 414-415, 418, 420, 423-424, 524, 545, 574, 578, 615-618, 661, 723, 725, 751, 755, 790-791, 793-794, 796-798, 814, 819, 821, 825-833, 837, 844-845, 889-891, 893-895, 898-899, 918 Virtual Ethnography 204, 214, 242-243, 248, 263, 268, 277, 297, 313, 890, 894 virtual observations 280, 282, 289, 294 visual social semiotics 548-549, 555-556, 560-561, 566, 568, 570

W Wattpad 318-319, 321-329, 334-335, 338-342 Web Scraping 4, 66, 81, 156-158, 228, 231, 267, 322, 489 Web Society 42, 45, 47, 50-52, 405, 420, 423, 820, 826, 832, 836-837 web survey on the social network 112 web surveys 33, 89-91, 100, 103, 105, 107, 109, 112, 116-117, 119, 131, 134, 146, 410 Wellbeing 404, 908, 917-918 Wicca 615, 618-621, 623, 625-626 World Love Index 898-899, 910-915, 917-919 w-readers 318

value co-creation 243, 280-281, 285-286, 288-290,

clxxxix