Exploring temporal dynamics in digital trace data: mining user-sequences for communication research
For communication researchers, it addresses the gap between theoretical dynamicality and non-dynamical methodologies, but the contribution is primarily conceptual with limited empirical validation.
The paper proposes a framework for analyzing temporal dynamics in digital trace data by maintaining hyper-longitudinal information and analyzing 'user-sequences', demonstrated on a dataset of 1.26M traces from 309 users. The results suggest a conceptual reorientation towards understanding temporal dimensions in communication processes.
Communication is commonly considered a process that is dynamically situated in a temporal context. However, there remains a disconnection between such theoretical dynamicality and the non-dynamical character of communication scholars' preferred methodologies. In this paper, we argue for a new research framework that uses computational approaches to leverage the fine-grained timestamps recorded in digital trace data. In particular, we propose to maintain the hyper-longitudinal information in the trace data and analyze time-evolving 'user-sequences,' which provide rich information about user activity with high temporal resolution. To illustrate our proposed framework, we present a case study that applied six approaches (e.g., sequence analysis, process mining, and language-based models) to real-world user-sequences containing 1,262,775 timestamped traces from 309 unique users, gathered via data donations. Overall, our study suggests a conceptual reorientation towards a better understanding of the temporal dimension in communication processes, resting on the exploding supply of digital trace data and the technical advances in analytical approaches.