Grounding Theory in Digital Data: A Methodological Approach for a Reflective Procedural Framework

Abstract

Instead of looking for new paradigms for Digital Humanities (DH), we present Grounded Theory Methodology (GTM) as a methodological approach to frame digital research practices more reflectively. By turning to the epistemological and practical implications of digital tools like Topic Modeling and digital data sources like YouTube comments, we highlight the theoretical assumptions that are already in the game—and call for more explicitness and methodical monitoring. To explain the procedures of GTM and the proposed worth for DH, we present an example of a qualitative research project using machine learning techniques to narrow down a large scale of data to human interpretable resample. The methodically monitored resampling process provided valuable means to validly minimize the amount of data without losing a qualitative trajectory of the process itself. Defining and tracing relevant content in our original data set enabled us to find related comments and textual conversations to be analyzed further. We discuss the example iteration in two ways: Our prototype and procedure show on the one hand, how qualitative research and computational methods can be better intertwined without compromising their epistemological foundations. On the other hand, we argue for an understanding of DH as research practice, that should follow an abductive research agenda in order to ground its theories in data.