A Collaborative Way of Knowing: Bridging Computational Communication Research and Grounded Theory Ethnography

Abstract

Abstract One of the great strengths of the field of communication is its interdisciplinarity. Yet this strength brings challenges, including rifts between diverse subfields. In this study, we illustrate the rich potential of collaborations across subfields. Specifically, we argue that due to often-overlooked epistemological similarities, unsupervised machine learning and grounded theory ethnography subfields are well-suited for an especially enriching collaboration. To demonstrate, a team of computational and ethnographic researchers together applied the analysis of topic model networks approach to ethnographic field notes. We illustrate how the inclusion of the ethnographer in the modeling stages, and of the computational researchers in the analysis stages, led to mutual reflexivity affecting every stage of the study, enabling profound reflections on the technical, conceptual, and theoretical pillars of both subfields. We conclude by discussing the potential future of such collaborative ways of knowing to open doors for cutting-edge interdisciplinary research for the new information era.

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