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Social Data Science Xennials

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Social Data Science Xennials

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Abstract

This chapter explores the tensions between analogue and digital methods in a processual way, placing social data science within the genealogy of the long-term disciplinary relations between phenomenological sociology, expertise in computer science associated with digitalisation and the narrative positivism linked with the use of statistics in social research. Focusing on what endures as well as on what changes, it discusses the theoretical, epistemological and ontological sensibilities that are involved in a commitment to digital data analysis. Referring to the ESRC Digital Social Research programme and to more recent work by the Alan Turing Institute Interest Group in Social Data Science, it acknowledges a UK-centric take on Social Data Science.

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Notes

  1. 1.

    I refer for example to work by David Stark and colleagues (Vedres and Stark 2010) and to recent developments in research on organisational routines (Pentland et al. 2012). After having carried out decades of ethnographic field research to study the organisational basis of innovation, Stark and his team have recently started to bring the analytical tools of network modelling onto the terrain of cultural sociology. This move granted the authors opportunities to reflect on the intersection of observation theory and network analysis, suggesting that social scientists should adopt a binocular view and reflect on ways to see the same objectivity that are out with the researcher’s own theoretical tradition.

    Another example is Brian Pentland’s research evolution towards computational methods. Having studied organisational routines through intensive fieldwork that involved collecting interview data, deeply rooted in qualitative research tradition of socio-materiality (Pentland et al. 2011), Pentland turned to adopt sequence and process analysis with an interest for identifying regularities across contexts. His intent to break disciplinary boundaries derives from the observation that ‘research grounded in a social science tradition tends to focus on people, while research grounded in an engineering tradition tends to focus on artifacts. However, as people and artifacts become increasingly intertwined in digitized processes and practices, these traditional disciplinary divisions sometimes seem a little outdated’ (Pentland 2013).

  2. 2.

    A similar point is made by Atkinson and Housley’s (2003) book Interactionism: An Essay in Social Amnesia.

  3. 3.

    https://esrc.ukri.org/files/research/research-and-impact-evaluation/dsr-report-executive-summary/.

  4. 4.

    Moats and Borra (2018) agree to this point when they say that ‘none of these [other formats] have (yet) proved as popular as the network diagram in relation to discussions of quali-quantitative methods’ (Moats and Borra 2018: 4).

  5. 5.

    http://www.mappingcontroversies.net/.

  6. 6.

    Another important frame of reference for the intellectual agenda described in this book is the Digital STS project initiated by David Ribes at the 4S conference in Cleveland in 2011 and recently summarised in the ‘DigitalSTS: A Field Guide for Science & Technology Studies’ (Vertesi and Ribes 2019).

  7. 7.

    Exceptions include Becker (1986) and Clifford and Marcus (1986).

  8. 8.

    Metzler (2016) survey of Big Data research in the social sciences found that, out of 3077 respondents involved in Big Data research, just over half (1690) had most recently used administrative data, 927 used social media data, and 697 used commercial/propriety data.

  9. 9.

    With other candidate names being ‘computational social science’, ‘digital social research’, ‘big data social science’ and ‘digital sociology’.

References

  • Abbott, A. (1992). From Causes to Events: Notes of Narrative Positivism. Sociological Methods & Research, 20(4), 428–455.

    Article  Google Scholar 

  • Abbott, A. (1995). Sequence Analysis: New Methods for Old Ideas. Annual Review of Sociology, 21, 93–113.

    Article  Google Scholar 

  • Abbott, A. (2005). Linked Ecologies: States and Universities as Environments for Professions. Sociological Theory, 23(3), 245–274.

    Article  Google Scholar 

  • Anderson, C. (2008). The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired Magazine. Retrieved from 23 June, 2008, from https://www.wired.com/2008/06/pb-theory/.

  • Atkinson, P. A., & Housley, W. (2003). Interactionism. London: SAGE.

    Google Scholar 

  • Barry, A., & Born, G. (2014). Interdisciplinarity: Reconfigurations of the Social and Natural Sciences. London: Routledge.

    Google Scholar 

  • Bartlett, A., Lewis, J., Reyes-Galindo, L., & Stephens, N. (2018). The Locus of Legitimate Interpretation in Big Data Sciences: Lessons for Computational Social Science from -Omic Biology and High-Energy Physics. Big Data & Society, 5(1), 1–15.

    Article  Google Scholar 

  • Beaulieu, A. (2016). Vectors for Fieldwork: Computational Thinking and New Modes of Ethnography. In L. Hjorth, H. Horst, A. Galloway, & G. Bel (Eds.), In Companion to Digital Ethnography (pp. 29–39). London: Routledge.

    Google Scholar 

  • Becker, H. (1986). Writing for Social Scientists: How to Start and Finish Your Thesis, Book, or Article. Chicago: University of Chicago Press.

    Google Scholar 

  • Bergmann, L. (2016). Toward Speculative Data: “Geographic Information” for Situated Knowledges, Vibrant Matter, and Relational Spaces. Society and Space, 34(6), 971–989.

    Google Scholar 

  • Bittner, E. (1965). The Concept of Organization. Social Research, 32(3), 239–255.

    Google Scholar 

  • Bouillier, D. (2018). Médialab Stories: How to Align Actor Network Theory and Digital Methods. Big Data & Society, 5(2), 1–13.

    Google Scholar 

  • Burnap, P., Rana, O., Williams, M., et al. (2014). COSMOS: Towards an Integrated and Scalable Service for Analyzing Social Media on Demand. International Journal of Parallel, Emergent and Distributed Systems (IJPEDS), 30(2), 80–100.

    Article  Google Scholar 

  • Campagnolo, G. M., & Fele, G. (2010). From Specifications to Specific Vagueness: How Enterprise Software Mediates Engineering Relations. Engineering Studies, 2(3), 221–243.

    Article  Google Scholar 

  • Clarke, A. E. (2005). Situational Analysis: Grounded Theory After the Postmodern Turn. Thousand Oaks, CA: SAGE.

    Book  Google Scholar 

  • Clifford, J., & Marcus, G. E. (Eds.). (1986). Writing Culture: The Poetics and Politics of Ethnography. Berkeley: University of California Press.

    Google Scholar 

  • Collins, R. (1984). Statistics Versus Words. Sociological Theory, 2, 329–362.

    Article  Google Scholar 

  • Collins, R. (1994). Why the Social Sciences Won’t Become High-Consensus, Rapid-Discovery Science. Sociological Forum, 9(2), 155–177.

    Article  Google Scholar 

  • Collins, H. M., & Evans, R. (2007). Rethinking Expertise. Chicago, IL: University of Chicago Press.

    Book  Google Scholar 

  • Coulter, J. (1996). Human Practices and the Observability of the ‘Macrosocial’. Zeitschrift für Soziologie, 25, 337–345.

    Article  Google Scholar 

  • Dalton, C., Taylor, L., & Thatcher, J. (2016). Critical Data Studies: A Dialog on Data and Space. SSRN. Retrieved from https://ssrn.com/abstract=2761166.

  • Di Maggio, P. (2015). Adapting Computational Text Analysis to Social Science (and Vice Versa). Big Data & Society, 2(2), 1–5.

    Google Scholar 

  • Dourish, P. (2001). Where the Action Is: The Foundations of Embodied Interaction. Cambridge, MA: MIT Press.

    Book  Google Scholar 

  • Dourish, P., & Button, G. (1998). On “Technomethodology”: Foundational Relationships Between Ethnomethodology and System Design. Human-Computer Interaction, 13(4), 395–432.

    Article  Google Scholar 

  • Dutton, H. W. (2013). The Social Shaping of Digital Research. International Journal of Social Research Methodology, 16(3), 177–195.

    Article  Google Scholar 

  • Edwards, A., Housley, W., Williams, M., Sloan, L., & Williams, M. (2013). Digital Social Research, Social Media and the Sociological Imagination: Surrogacy, Augmentation and Re-orientation. International Journal of Social Research Methodology, 24, 313–343.

    Google Scholar 

  • Fele, G. (2009). Why is Information System Design Interested in Ethnography? Sketches of an Ongoing Story. Ethnografia e Ricerca Qualitativa, 1, 1–38.

    Google Scholar 

  • Fine, T. (1973). Theories of Probability: An Examination of Foundations. New York: Academic Press.

    Google Scholar 

  • Garfinkel, H. (1967). Studies in Ethnomethodology. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Gauthier, J. A., Widmer, E. D., Bucher, P., & Notredame, C. (2010). Multi-channel Sequence Analysis Applied to Social Science Data. Sociological Methodology, 40, 1–38.

    Article  Google Scholar 

  • Gouldner, A. W. (1970). The Coming Crisis of Western Sociology. New York: Basic Books.

    Google Scholar 

  • Greiffenhagen, C., Mair, M., & Sharrock, W. (2011). From Methodology to Methodography: A Study of Qualitative and Quantitative Reasoning in Practice. Methodological Innovations Online, 6(3), 93–107.

    Article  Google Scholar 

  • Hacking, I. (1975). The Emergence of Probability. Cambridge: Cambridge University Press.

    Google Scholar 

  • Hadi, D., & Marcus, G. E. (2011). In the Green Room: An Experiment in Ethnographic Method at the WTO. PoLAR, 34(1), 51–76.

    Article  Google Scholar 

  • Halfpenny, P., & Procter, R. (2015). Innovations in Digital Research Methods. London: Sage.

    Google Scholar 

  • Hindess, B. (1973). The Use of Official Statistics in Sociology: A Critique of Positivism and Ethnomethodology. London: Macmillan.

    Book  Google Scholar 

  • Housley, W., & Smith, R. J. (2017). Interactionism and Digital Society. Qualitative Research 17(2), 187–201.

    Google Scholar 

  • Hyysalo, S. (2010). Health Technology Development and Use: From Practice-Bound Imagination to Evolving Impacts. London: Taylor & Francis.

    Book  Google Scholar 

  • Irons, R. L. (1998). Organizational and Technical Communication: Terminological Ambiguity in Representing Work. Management Communication Quarterly, 12(1), 42–71.

    Article  Google Scholar 

  • Jaton, F. (2017). We Get the Algorithms of Our Ground Truths: Designing Referential Databases in Digital Image Processing. Social Studies of Science, 47(6), 811–840.

    Article  Google Scholar 

  • Kallinikos, J. (2004). Farewell to Constructivism: Technology and Context-embedded Action. In C. Avgerou, C. Ciborra, & F. Land (Eds.), The Social Study of Information and Communication Technology: Innovation, Actors, and Contexts. Oxford: Oxford University Press.

    Google Scholar 

  • Kitchin, R. (2014). Big Data, New Epistemologies and Paradigm Shifts. Big Data & Society, 1(1), 1–12.

    Article  Google Scholar 

  • Kitchin, R., & McArdle, G. (2016). What Makes Big Data, Big Data? Exploring the Ontological Characteristics of 26 Datasets. Big Data & Society, 3(1). https://doi.org/10.1177/2053951716631130.

  • Knorr Cetina, K. (1999). Epistemic Cultures: How the Sciences Make Knowledge. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Kunda, G. (1991). Engineering Culture: Control and Commitment in a High-Tech Corporation. Philadelphia: Temple University Press.

    Google Scholar 

  • Latour, B., Jensen, P., Venturini, T., Grauwin, S., & Bouillier, D. (2012). ‘The Whole Is Always Smaller Than Its Parts’—A Digital Test of Gabriel Tardes’ Monads. The British Journal of Sociology, 63(4), 590–615.

    Article  Google Scholar 

  • Law, J., & Urry, J. (2004). Enacting the Social. Economy and Society, 33(3), 390–410.

    Article  Google Scholar 

  • Lazer, D., & Radford, J. (2017). Data Ex Machina: Introduction to Big Data. Annual Review of Sociology, 43, 19–39.

    Article  Google Scholar 

  • Lupton, D. (2014). Digital Sociology. London and New York: Routledge.

    Book  Google Scholar 

  • MacKenzie, D. (1981). Statistics in Britain, 1865–1930: The Social Construction of Scientific Knowledge. Edinburgh: Edinburgh University Press.

    Google Scholar 

  • MacKenzie, D. (2018). ‘Making’, ‘Taking’ and the Material Political Economy of Algorithmic Trading. Economy and Society, 47(4), 501–523.

    Article  Google Scholar 

  • Mahoney, J., & Goertz, G. (2006). A Tale of Two Cultures: Contrasting Quantitative and Qualitative Research. Political Analysis, 14(3), 33–53.

    Article  Google Scholar 

  • Marres, N. (2017). Digital Sociology. Cambridge: Polity Press.

    Google Scholar 

  • Marres, N., & Moats, D. (2015). Mapping Controversies with Social Media: The Case for Symmetry. SSRN. Retrieved from https://ssrn.com/abstract=2567929 or https://doi.org/10.2139/ssrn.2567929.

  • McFarland, A. D., Lewis, K., & Goldberg, A. (2016). Sociology in the Era of Big Data: The Ascent of Forensic Social Science. The American Sociologist, 47, 12–35.

    Google Scholar 

  • Metzler, K. (2016). The Big Data Rich and the Big Data Poor: The New Digital Divide Raises Questions About Future Academic Research. The Impact Blog, London School of Economics and Political Science. Retrieved from http://blogs.lse.ac.uk/impactofsocialsciences/2016/11/22/the-big-data-rich-and-the-big-data-poor-the-new-digital-divide-raises-questions-about-future-academic-research/.

  • Mills, C. W. (1959). The Sociological Imagination. Oxford: Oxford University Press.

    Google Scholar 

  • Moats, D., & Borra, E. (2018). Quali-Quantitative Methods Beyond Networks: Studying Information Diffusion on Twitter with the Modulation Sequencer. Data & Society, 5(1), 1–17.

    Article  Google Scholar 

  • Molina, M., & Garip, F. (2019). Machine Learning for Sociology. Annual Review Sociology, 45, 27–45.

    Article  Google Scholar 

  • Monteiro, E., Pollock, N., Hanseth, O., & Williams, R. (2013). From Artefacts to Infrastructure. Computer Supported Cooperative Work, 22(4–6), 575–607. (CSCW).

    Article  Google Scholar 

  • Neyland, D. (2016). Bearing Account-able Witness to the Ethical Algorithmic System. Science, Technology, & Human Values, 41(1), 50–76.

    Article  Google Scholar 

  • Orton-Johnson, K., & Prior, N. (Eds.). (2013). Digital Sociology: Critical Perspectives. London: Palgrave Macmillan.

    Google Scholar 

  • Pentland, B. (2013). Desperately Seeking Structures: Grammars of Action in Information Systems Research. The DATA BASE for Advances in Information Systems, 44(2), 7–18.

    Google Scholar 

  • Pentland, B.T., Hærem, T., & Hillison, D. (2011). The (N)Ever-Changing World: Stability and Change in Organizational Routines, Organization Science, 22(6), 1369–1383.

    Google Scholar 

  • Pentland, B. T., Feldman, M. S., Becker, M. C., and Liu, P. (2012). Dynamics of Organizational Routines: A Generative Model, Journal of Management Studies, 49(8), 1484–1508.

    Google Scholar 

  • Pollock, N., & Williams, R. (2008). Software and Organisations: The Biography of the Enterprise-Wide System or How SAP Conquered the World. Oxon: Routledge.

    Book  Google Scholar 

  • Procter, R., Vis, F., & Voss, A. (2013). Reading the Riots on Twitter: Methodological Innovation for the Analysis of Big Data. International Journal of Social Research Methodology, 16(3), 197–214.

    Article  Google Scholar 

  • Randall, D., Harper, R., & Rouncefield, M. (2005). Fieldwork, Ethnography and Design: A Perspective from CSCW. In K. Anderson & T. Lovejoy (Eds.), EPIC 2005: Ethnographic Praxis in Industry Conference (pp. 88–99). Seattle, WA and Arlington, VA: Redmond, American Anthropological Association.

    Google Scholar 

  • Rheinberger, H.-J. (2011). Consistency from the Perspective of an Experimental Systems Approach to the Sciences and Their Epistemic Objects. Manuscrito, 34(1), 307–321.

    Article  Google Scholar 

  • Roepstorff, A., & Frith, C. (2012). Neuroanthropology or Simply Anthropology? Going Experimental as Method, as Object of Study, and as Research Aesthetic. Anthropological Theory, 12(1), 101–111.

    Google Scholar 

  • Rogers, R., & Marres, N. (2000). Landscaping Climate Change: A Mapping Technique for Understanding Science and Technology Debates on the World Wide Web. Public Understanding of Science, 9(2), 141–163.

    Article  Google Scholar 

  • Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Sandvig, C., & Hargittai, E. (2015). How to Think about Digital Research. In E. Hargittai & C. Sandvig (Eds.), Digital Research Confidential: The Secrets of Studying Behavior Online. Cambridge, MA: MIT Press.

    Google Scholar 

  • Savage, M. (2015). Sociology and the Digital Challenge. In P. Halfpenny & R. Procter (Eds.), Innovations in Digital Research Methods. London: Sage.

    Google Scholar 

  • Savage, M., & Burrows, R. (2007). The Coming Crisis of Empirical Sociology. Sociology, 41(5), 885–899.

    Article  Google Scholar 

  • Seaver, N. (2017). Algorithms as Culture: Some Tactics for the Ethnography of Algorithmic Systems. Big Data & Society, 4(2), 1–12.

    Article  Google Scholar 

  • Snow, C. P. (1959). The Two Cultures and the Scientific Revolution. Cambridge: Cambridge University Press.

    Google Scholar 

  • Stump, David, J. 1996. From Epistemology and Metaphysic s to Concrete Connections’, in D. Stump and P. Galison (eds), Disunity of Science: Boundaries, Contexts, and Power. Stanford, CA: Stanford University Press, pp. 255–286.

    Google Scholar 

  • Suchman, L. A. (1987). Plans and Situated Actions: The Problem of Human-Machine Communication. Cambridge: Cambridge Press.

    Google Scholar 

  • Vaast, E., & Walsham, G. (2009). Trans-Situated Learning: Supporting a Network of Practice with an Information Infrastructure. Information Systems Research, 20(4), 547–564.

    Article  Google Scholar 

  • Vedres, B., & Stark, D. (2010). Structural Folds: Generative Disruption in Overlapping Groups. American Journal of Sociology, 115(4) :1150–1190.

    Google Scholar 

  • Veltri, A. G. (2019). Digital Social Research. Cambridge: Polity Books.

    Google Scholar 

  • Venturini, T., Jacomy, M., & Meaner, A. (2017). An Unexpected Journey: A Few Lessons from Sciences Po Médialab’s Experience. Big Data & Society, 4(2), 1–11.

    Article  Google Scholar 

  • Vertesi, J., & Ribes, D. (2019). DigitalSTS: A Field Guide for Science & Technology Studies. Princeton University Press.

    Google Scholar 

  • Williams, R., & Edge, D. (1996). The Social Shaping of Technology. Research Policy Vol., 25(1996), 856–899.

    Google Scholar 

  • Williams, R., & Procter, R. (1998). Trading Places: A Case Study of the Formation and Deployment of Computing Expertise. In R. Williams et al. (Eds.), Exploring Expertise (pp. 197–222). Basingstoke: Macmillan. Chap. 13.

    Chapter  Google Scholar 

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  1. Science, Technology and Innovation Studies, University of Edinburgh, Edinburgh, UK

    Gian Marco Campagnolo

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Campagnolo, G.M. (2020). Social Data Science Xennials. In: Social Data Science Xennials. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-60358-8_1

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