Handbook of Research on Advanced Research Methodologies for a Digital Society 1799884732, 9781799884736

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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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