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

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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.