Carsten Schwemmer was kind enough to invite me to speak at the Bamberg branch of this year’s Summer Institute in Computational Social Science (SICSS). So I used the chance to speak about some of the limitations of current practices in computational social science and the need to foreground social science methodology and practices in the work with advanced computational methods and large data sets:
Social scientists have found themselves confronting a massive increase in available data sources. In the debates on how to use these new data, the research potential of “digital trace data” has featured prominently. While various commentators expect digital trace data to create a “measurement revolution”, empirical work has fallen somewhat short of these grand expectations. In fact, empirical research based on digital trace data is largely limited by the prevalence of two central fallacies: First, the n=all fallacy; second, the mirror fallacy. These fallacies can be addressed by developing a measurement theory for the use of digital trace data.
The talk is motivated by two recent papers. In Normalizing Digital Trace Data, I wrote about the limitations of computational social science if it does not sufficiently engage topical debates in the social sciences and loses sight of concept validation [Preprint]. Building on this, the paper The Empiricist’s Challenge (with Yannis Theocharis) focused on the need for theory-driven research in computational social science.
Here are the slides.