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Collaboration Between Social Sciences and Computer Science: Toward a Cross-Disciplinary Methodology for Studying Big Social Data from Online Communities

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Second International Handbook of Internet Research

Abstract

Online communities are now extremely numerous. Most of them being multifaceted, dynamic, and rapidly evolving, they are of the utmost interest for social science researchers. One of the special characteristics of these communities is the production of numerical traces generated by communications between members, inside their communities or through social networks. These traces, captured and stored by software managing their dissemination, represent a massive amount of data. Based on their volume, velocity, variety, and veracity, they must be handled in the context of the big data phenomenon. These novel constraints generate scientific, epistemological, and ethical problems related to the limited understanding researchers have of the algorithms utilized by software tools, their possibilities and limitations, error rates, and biases. As a consequence, social science researchers interested in mining all these data often depend on data analysts who lack any social science background. Collaboration between social sciences and computer science is hence critical to meet these challenges, and to propose a cross-disciplinary methodology combining the contributions of both fields towards the study of online communities. Using online communities of video game players as an example, this contribution puts the emphasis on identifying the challenges associated with the study of online communities, and proposes a methodology combining computer science and social science approaches. First, we present research questions, categorizations, and classifications related to identity, communication, and social dynamics by linking them to data mining and automated processing techniques. We then study how to integrate social science models into computer tools, and link qualitative methods with big data analysis in order to overcome errors in the interpretation of results related to data decontextualization. Finally, we formalize ethical concerns of social science researchers regarding limitations of software tools. This chapter hence demonstrates the scientific, epistemological, and ethical advantages of combining accepted methods from computer science and social sciences in order to propose a cross-disciplinary methodology for research on online communities.

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References

  • Ackland R, Zhu J (2015) Social network analysis. In: Halfpenny P, Procter R (eds) Innovations in digital research methods. Sage, London, pp 221–244

    Google Scholar 

  • Ampofo L, Collister S, O’Loughlin B, Chadwick A (2015) Text mining and social media: when quantitative meets qualitative and software meets people. In: Halfpenny P, Procter R (eds) Innovations in digital research methods. Sage, London, pp 161–192

    Google Scholar 

  • Anderson C (2008) The end of theory: the data deluge makes scientific method obsolete. Wired. https://www.wired.com/2008/06/pb-theory/. Accessed 10 Oct 2017

  • Anderson RJ, Jirotka M (2015) Ethical praxis in digital social research. In: Halfpenny P, Procter R (eds) Innovations in digital research methods. Sage, London, pp 271–296

    Google Scholar 

  • Andrejevic M (2007) iSpy: surveillance and power in the interactive era. University of Kansas Press, Lawrence

    Google Scholar 

  • Artstein R (2017) Inter-annotator agreement. In: Ide N, Pustejovsky J (eds) Handbook of linguistic annotation. Springer, Dordrecht, pp 297–313

    Chapter  Google Scholar 

  • Baeza-Yates R, Ribeiro-Neto B (1999) Modern information retrieval. ACM Press, New York

    Google Scholar 

  • Barry A, Born G, Weszkalnys G (2008) Logics of interdisciplinarity. Econ Soc 37(1):20–49

    Article  Google Scholar 

  • Beck K et al (2001) Manifesto for agile software development. Agile manifesto. http://agilemanifesto.org. Accessed 10 Oct 2017

  • Bennett CJ, Haggerty KD, Lyon D, Steeves V (2014) Transparent lives: surveillance in Canada. Athabasca University Press, Edmonton

    Book  Google Scholar 

  • Berendt B, Büchler M, Rockwell G (2015) Is it research or is it spying? Thinking-through ethics in Big Data AI and other knowledge sciences. Künstl Intell 29(2):223–232

    Article  Google Scholar 

  • Boellstorff T (2013) Making big data, in theory. First Monday, 18.10

    Google Scholar 

  • Bollier D (2010) The promise and peril of big data. The Aspen Institute. http://www.aspeninstitute.org/sites/default/files/content/docs/pubs/The_Promise_and_Peril_of_Big_Data.pdf. Accessed 10 Oct 2017

  • Borgatti SP, Halgin DS (2011) On network theory. Organ Sci 22(5):1168–1181

    Article  Google Scholar 

  • Borgman C L (2015) Big Data, little data, no data: Scholarship in the networked world. Cambridge: The MIT Press

    Google Scholar 

  • Boyd D, Crawford K (2012) Critical questions for big data. Inf Commun Soc 15(5):662–679

    Article  Google Scholar 

  • Brin S, Page L (2012) Reprint of: the anatomy of a large-scale hypertextual web search engine. Comput Netw 56(18):3825–3833

    Article  Google Scholar 

  • Brown I, Marsden CT (2013) Regulating code. Good governance and better regulation in the information age. MIT Press, Cambridge

    Book  Google Scholar 

  • Burrows R, Savage M (2014) After the crisis? Big Data and the methodological challenges of empirical sociology. Big Data Soc April–June:1–6

    Google Scholar 

  • Burt RS, Minor MJ (1983) Applied network analysis: a methodological introduction. Sage, London

    Google Scholar 

  • Carletta J (1996) Assessing agreement on classification tasks: the kappa statistic. Comput Linguist 22(2):249–254

    Google Scholar 

  • Chessell M (2014) Ethics for big data and analytics. IBM Big Data Hub. http://www.ibmbigdatahub.com/sites/default/files/whitepapers_reports_file/TCG%20Study%20Report%20-%20Ethics%20for%20BD%26A.pdf. Accessed 10 Oct 2017

  • Crampton J et al (2013) Beyond the geotag: situating ‘Big Data’ and leveraging the potential of the geoweb. Cartogr Geogr Inf Sci 40(2):130–139

    Article  Google Scholar 

  • Dalton C, Thatcher J (2014) What does a critical data studies look like, and why do we care? Seven points for a critical approach to ‘Big Data’. Society and Space. http://societyandspace.org/2014/05/12/what-does-a-critical-data-studies-look-like-and-why-do-we-care-craig-dalton-and-jim-thatcher/. Accessed 10 Oct 2017

  • Davis K, Patterson D (2012) The ethics of big data: balancing risk and innovation. O’Reilly, Cambridge

    Google Scholar 

  • De Castell S et al (2012) Theoretical and methodological challenges (and opportunities) in virtual worlds research. In: Proceedings of the international conference on the foundations of digital games. Raleigh, NC, USA. pp 134–140

    Google Scholar 

  • Dewulf A, Françis G, Pahl-Wostl C, Taillieu T (2007) A framing approach to cross-disciplinary research collaboration: experiences from a large-scale research project on adaptive water management. Ecol Soc 12(2):14

    Article  Google Scholar 

  • Drachen A, Sifa R, Bauckhage C, Thurau C (2012) Guns, swords and data: clustering of player behavior in computer games in the wild. In: Proceedings of IEEE computational intelligence in games. Granada, Spain. pp 163–170

    Google Scholar 

  • Ducheneaut N et al (2006) Building an MMO with mass appeal: a look at gameplay in World of Warcraft. Games Cult 1(4):281–317

    Article  Google Scholar 

  • Ducheneaut N, Yee N, Nickell E, Moore RJ (2007) The life and death of online gaming communities: a look at guilds in World of Warcraft. In: Proceedings of the SIGCHI conference on human factors in computing systems. San Jose, CA, USA. pp 839–848

    Google Scholar 

  • Elliot M, Purdam K (2015) Exploiting new sources of data. In: Halfpenny P, Procter R (eds) Innovations in digital research methods. Sage, London, pp 59–84

    Google Scholar 

  • Ellul J (1954) La technique ou l’enjeu du siècle. Armand Colin, Paris

    Google Scholar 

  • El-Nasr M, Drachen A, Canossa A (2013) Game analytics: maximizing the value of player data. Springer, Londres

    Book  Google Scholar 

  • Feenberg A (1999) Questioning technology. Routledge, New York/London

    Google Scholar 

  • Gitelman L (2013) “Raw data” is an oxymoron. MIT Press, Cambridge

    Book  Google Scholar 

  • Goulden M et al (2017) Wild interdisciplinarity: ethnography and computer science. Int J Soc Res Methodol 20(2):137–150

    Article  Google Scholar 

  • Halfpenny P, Procter R (eds) (2015) Innovations in digital research methods. Sage, London

    Google Scholar 

  • Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Overview of supervised learning. In: Springer series in statistics. Springer, New York, pp 9–41

    Google Scholar 

  • Heidegger M (1977 [1954]) The question concerning technology. Garlang Publishing, New York

    Google Scholar 

  • Hey T, Trefethen A (2003) The data deluge: an e-science perspective. In: Berman K, Fox GC, Hey AJG (eds) Grid computing: making the global infrastructure a reality. Wiley, New York, pp 855–864

    Google Scholar 

  • Illiadis A, Russo F (2016) Critical data studies: an introduction. Big Data Soc 3(2):1–7

    Google Scholar 

  • Jaimes A, Sebe N, Gatica-Perez D (2006) Human-centered computing: a multimedia perspective. In: Proceedings of the 14th ACM international conference on multimedia. Santa Barbara, CA, USA. pp 855–864

    Google Scholar 

  • Kitchin R (2014) Big Data, new epistemologies and paradigm shifts. Big Data Soc 1(1). https://doi.org/10.1177/2053951714528481. Accessed 10 Oct 2017

  • Kitchin R, Lauriault TP (2015) Small data in the era of big data. GeoJournal 80(4):463–475

    Article  Google Scholar 

  • Langlois G, Redden J, Elmer G (2015) Compromised data : From social media to big data. Bloomsbury, New York

    Google Scholar 

  • Lazer D, Kennedy R, King G, Vespignani A (2014) The Parable Google Flu: Traps in Big Data Analysis. Science 343:1203–1205

    Google Scholar 

  • Leinweber D (2007) Stupid data miner tricks: overfitting the S&P 500. J Invest 16(1):15–22

    Article  Google Scholar 

  • Lewis C, Wardrip-Fruin N (2010) Mining game statistics from web services: a World of Warcraft armory case study. In: Proceedings of the fifth international conference on the foundations of digital games. ACM, Monterey, CA, USA. pp 100–107

    Google Scholar 

  • Mailloux LO, Grimaila MR, Hodson DD, Baumgartner GB (2017) The benefits of joining a multidisciplinary research team. IEEE Potentials 36(3):18–22

    Article  Google Scholar 

  • Mayer-Schönberger V, Cukier K (2014) Big data: a revolution that will transform how we live, work, and think. Eamon Dolan/Mariner, London

    Google Scholar 

  • Medler B, Magerko B (2011) Analytics of play: using information visualization and gameplay practices for visualizing video game data. Parsons J Inf Mapp 3(1):1–12

    Google Scholar 

  • Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L (2016) The ethics of algorithms: Mapping the debate, Big Data & Society, July–December: pp 1–21

    Google Scholar 

  • Mongeau P, Saint-Charles J (2011) Les approches communicationnelles des groupes dans les organisations. In: Grosjean S, Bonneville L (eds) Communication organisationnelle: approches, processus et enjeux. Chenelière Éducation, Montréal, pp 253–279

    Google Scholar 

  • Mongeau P, Saint-Charles J (2014) Centralité de réseaux et similitude de discours : une approche sociosémantique du leadership émergent dans les groupes de travail. Communiquer. Revue de communication sociale et publique (12), pp 121–139

    Google Scholar 

  • Morillo F, Bordons M, Gómez I (2003) Interdisciplinarity in science: a tentative typology of disciplines and research areas. J Assoc Inf Sci Technol 54(13):1237–1249

    Article  Google Scholar 

  • Mumford L (1967) The myth of the machine. Volume 1: Technics and human development. Harcourt Brace Jovanovich, San Diego

    Google Scholar 

  • Mumford L (1970) The myth of the machine. Volume 2: The pentagon of power. Harcourt Brace Jovanovich, San Diego

    Google Scholar 

  • Neff G, Tanweer A, Fiore-Gartland B, Osburn L (2017) Critique and contribute: a practice-based framework for improving critical data studies and data science. Big Data 5(2):85–97

    Article  Google Scholar 

  • Pennington DD (2008) Cross-disciplinary collaboration and learning. Ecol Soc 13(2):8

    Article  Google Scholar 

  • Purdam K, Elliot M (2015) The changing social science data landscape. In: Halfpenny P, Procter R (eds) Innovations in digital research methods. Sage, London, pp 25–58

    Google Scholar 

  • Pushmann C, Burgess J (2014) Metaphors of Big Data. Int J Comm vol. 8, 1690–1709

    Google Scholar 

  • Rosa H (2013) Social acceleration: a new theory of modernity. Columbia University Press, New York

    Google Scholar 

  • Saint-Charles J, Mongeau P (2005). Communication: Horizon de pratiques et de recherche. Presses de l’Université du Québec, Québec

    Google Scholar 

  • Savage M, Burrows R (2007) The coming crisis of empirical sociology. Sociology 41(5):885–899

    Article  Google Scholar 

  • Scott J (2017) Social network analysis. Sage, London

    Google Scholar 

  • Simondon G (1958) Du mode d’existence des objets techniques. Aubier, Paris

    Google Scholar 

  • Sommerville I, Rodden T, Sawyer P, Bentley R (1992) Sociologists can be surprisingly useful in interactive system design. In: People and computers VII: proceedings of HCI 92. Cambridge University Press, New York, pp 342–354

    Google Scholar 

  • Sommerville I, Rodden T, Sawyer P, Bentley R (1993) Sociologists can be surprisingly useful in interactive system design. In: People and computers VII: proceedings of HCI 92, York (United Kingdom), Cambridge University Press, New York, pp 342–354

    Google Scholar 

  • Spiller K, Ball K, Daniel E, Dibb S, Meadows M, Canhoto A (2015) Carnivalesque collaboration: reflections on ‘doing’ multi-disciplinary research. Qual Res 15(5):551–567

    Article  Google Scholar 

  • Stiegler B (1994, 1996, 2001) La Technique et le temps (3 tomes). Éditions Galilée, Paris

    Google Scholar 

  • Williams D (2010) The promises and perils of large-scale data extraction. McArthur Foundation, Chicago

    Google Scholar 

  • Williams D, Ducheneaut N, Xiong L, Zhang Y, Yee N, Nickell E (2006) From tree house to barracks: the social life of guilds in World of Warcraft. Games Cult 1(4):338–361

    Article  Google Scholar 

  • Williams D, Yee N, Caplan SE (2008) Who plays, how much, and why? Debunking the stereotypical gamer profile. J Comput-Mediat Commun 13(4):993–1018

    Article  Google Scholar 

  • Witten IH, Frank E, Hall MA, Pal CJ (2016) Data mining: practical machine learning tools and techniques. Morgan Kaufmann Publishers, New York

    Google Scholar 

  • Zwitter A (2014) Big data ethics. Big Data Soc 1(2). https://doi.org/10.1177/2053951714559253. Accessed 10 Oct 2017

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Author information

Authors and Affiliations

  1. Department of Social and Public Communication, University of Quebec in Montreal, Montreal, QC, Canada

    Maude Bonenfant

  2. Department of Computer Science, University of Quebec in Montreal, Montreal, QC, Canada

    Marie-Jean Meurs

Authors
  1. Maude Bonenfant
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  2. Marie-Jean Meurs
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Corresponding author

Correspondence to Maude Bonenfant .

Editor information

Editors and Affiliations

  1. Communication Studies, Wilfrid Laurier University, Waterloo, ON, Canada

    Jeremy Hunsinger

  2. Deakin University, Burwood, VIC, Australia

    Matthew M. Allen

  3. IT University of Copenhagen, Copenhagen, Denmark

    Lisbeth Klastrup

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Bonenfant, M., Meurs, MJ. (2020). Collaboration Between Social Sciences and Computer Science: Toward a Cross-Disciplinary Methodology for Studying Big Social Data from Online Communities. In: Hunsinger, J., Allen, M., Klastrup, L. (eds) Second International Handbook of Internet Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-1555-1_39

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