Big data and explanation: Reflections on the uses of big data in media and communication research

Abstract

In the article, we argue that the advent of data mining techniques and big data in media and communication studies present problems that involve fundamental methodological questions, requiring us to revisit existing ways in which the link between theory, operationalization and data are explained and justified. We note that the discourse of instrumental optimization that surrounds big data clouds epistemic debates about their appropriate integration in scholarly explanations, and argue that a discussion of these problems can usefully depart from a distinction between the two main types of data mining models (supervised and unsupervised). We argue that both types pose specific challenges and give examples of ways they have been productively overcome. In particular, we argue that while big data approaches have introduced novel opportunities for research, they have fundamentally been incorporated into media and communication studies in ways that comply with existing, prototypical explanatory schemes. Our examples link specific empirical studies to general strategies of scientific explanation, focusing on neo-positivist, critical realist and interpretivist explanations.