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Drowning in Data: Industry and Academic Approaches to Mixed Methods in “Holistic” Big Data Studies

Sat, June 11, 11:00 to 12:15, Fukuoka Hilton, Navis C

Session Submission Type: Panel


In the past five years, as “big data” research increasingly has been adopted and adapted in the social sciences, the question of multimodal analysis pays a larger role in approaches and perspectives of research methodology. The buzzword "big data" has provoked critiques by a number of social scientists (eg., boyd & Crawford 2011; Bruns & Burgess 2012; Burrell 2012; Baym 2013; Lazer, et al. 2014; Tufekci 2014) on the theories, methodologies, and analysis of large data sources, and yet a growing number of scholars are experimenting with new ways to think about applying traditional and established methods to a newer domain and scale of data. Past panels (e.g., ICA 2013’s “Downsizing Data: Analyzing Social Digital Traces” and ICA 2014’s “Data-Driven Data Research Using Data and Databases: A Practical Critique of Methods and Approaches in ‘Big Data’ Studies”) have examined the practice of large-scale data analysis in social media research. This panel extends those discussions to look at the complications of mixed-methods research in big data studies, specifically in cases when “holistic,” population-level data is available.

The panel focuses on sociotechnical systems – social media platforms and online multiplayer games – where researchers can analyze data from every individual participant. By removing the constraints of data access and sampling, new questions around the process of using mixed research methodologies can be examined. The panelists will explore processes for observation, interviewing, surveying, and conducting advanced statistical analysis in concert with or in opposition to each other. When you have hundreds of millions of data points, where do you start, how do you explore, and why do you end up with a particular set of results?

The panel brings together scholars to speak about their successes and failures working on projects that employ holistic data sources to understand social behaviors. The panelists come from both academia and industry, as well as diverse methodological backgrounds (from ethnographers to computer scientists). The panel will discuss empirical research they have conducted and the issues they have encountered with 1) dealing with massive, holistic datasets, 2) employing a variety of research methods, from observational to computational, and 3) synthesizing theory and results across these methods. The projects – spanning a diverse set of sociotechnical platforms: Facebook, reddit, Wikipedia, and League of Legends – are all exemplars of an emerging mode of scholarship, and collectively they aim to generate a productive and concrete discussion about methodology and epistemology. Participants will spend 5 minutes each to speak in detail about the methodologies of their projects, after which the latter half of the panel will open to discussion with the audience.


Baym, N. (2013). Data Not Seen: The Uses and Shortcomings of Social Media Metrics. First Monday, 18 (10). Retrieved from

boyd, d. & Crawford, K. (2011). Six Provocations for Big Data. Presented at A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, Oxford Internet Institute, September 2011. Retrieved from

Bruns, A. & Burgess, J. (2012). Notes towards the Scientific Study of Public Communication on Twitter. Presented at Conference on Science and the Internet, Düsseldorf, August 2012. Retrieved from

Burrell, J. (2012). The Ethnographer’s Complete Guide to Big Data: Small Data People in a Big Data World. Ethnography Matters. Retrieved from

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

Tufekci, Z. (2014). Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls. In Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, ICWSM '14: 505-514.

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