Normalizing Digital Trace Data

About

Over the last ten years, 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. As I will argue, these fallacies can be addressed by developing a measurement theory for the use of digital trace data. For this, researchers will have to test the consequences of variations in research designs, account for sample problems arising from digital trace data, and explicitly link signals identified in digital trace data to sophisticated conceptualizations of social phenomena.

This talk will outline the two fallacies in greater detail and discuss their consequences with regard to three general areas in the work with digital trace data in the social sciences: digital ethnography, proxies, and hybrids. For each of these areas, I will present selected prominent studies predominantly from political communication research. The talk will close by a short assessment of the road ahead and how these fallacies might be constructively addressed by the systematic development of a measurement theory for the work with digital trace data in the social sciences.