Exploring temporal dynamics in digital trace data: mining user-sequences for communication research

Communication is commonly considered a process that is dynamically situated in a temporal context. However, there remains a disconnection between this theoretical dynamicality and the often less dynamic methods that communication scholars use to study it. Given the increasing accessibility of digital trace data, this study provides a methodological overview of how such fine-grained temporal information can be used in communication research. In particular, we show how to retain the hyper-longitudinal information in the trace data and analyze time-evolving “user-sequences,” which capture user activities at high temporal resolution. We then survey a set of established sequential methods, including sequence analysis, event history analysis, hidden Markov models, network analysis, process mining, and language-based models. We also articulate important sequential features that can be studied within user-sequences, such as transitions, subsequences, and trajectories. As an illustrative example, we apply the six methods to real-world user-sequences containing 1,262,775 timestamped traces from 309 unique users, gathered via data donations. Overall, our study provides a methodological overview of sequence analysis applied to digital trace data and offers initial guidance on method selection.