Rich Theorizing from Digital Trace Data

Abstract
Ever more social interactions take place in digital technology environments where they produce vast amounts of digital trace data. Such data are not generated for the sake of research but are “found” and can thus relate to multiple phenomena of social interaction with digital technology simultaneously. Previous work has shown the potential of leveraging digital trace data as well as the need for reflection about the generation and interpretation of trace data for theorizing. To complement these efforts, we develop a model of theorizing from digital trace data by attending to researchers’ conceptual focus (e.g., phenomena of structure, process, or semantics) and the types of analytical patterns (from low to high fidelity). Based on this model, we derive different theorizing strategies that combine one or many conceptual foci with patterns of varying fidelity. This allows researchers to leverage the multi-facetted characteristics of digital trace data and generate theory of emerging phenomena that exist on the intersection of structural, processual, or semantic aspects captured in digital traces. To illustrate the utility of these strategies for theorizing, we explain their implementation in archetypal and actual research designs. This paper contributes to the bourgeoning literature on computational methods and trace data analysis by providing a vocabulary as well as actionable guidance for building and assessing theory that draws from digital trace data.

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