ABSTRACT
Despite the promise of quantitative ethnographic approaches for visualizing the trajectories of change over time (temporal analysis) further work is needed to develop strategies for accurately representing phenomena. This holds especially true for identifying the relational context of discourse, which includes the creation of time units that group lines of data for the purpose of interpretation. While in-depth interpretive review of discourse may serve as the ‘gold standard’ for identification of thematic time units, this approach is tedious and may not be appropriate for larger datasets. Incremental approaches, such as creating a new unit for every ten lines of chronological data, are functional for larger datasets, but may lack nuance. This work introduces the Knowledge Building Discourse Explorer (KBDeX), which computationally identifies relational units using socio-semantic network analysis, allowing for the identification of time units based on characteristics of the discourse that can be systematically applied to larger datasets. To examine the utility of each approach, epistemic networks of COVID-19 press releases from seven countries were created with time units derived from the incremental and computational approaches, which were then compared to the interpretive approach. Results indicated that KBDeX and incremental network means were closer to the ‘gold standard’ interpretive approach in some instances. Two countries’ trajectories are examined in greater depth to understand when each approach might be most appropriate. The work concludes with a discussion of the affordances and constraints of each approach, and contexts in which they may be useful. © 2022, Springer Nature Switzerland AG.