🤖 AI Summary
Communication research has long faced a methodological tension between static analytical approaches and the inherently dynamic nature of diffusion processes, hindering fine-grained temporal modeling of digital traces. To address this, we propose “hyper-longitudinal analysis”—the first systematic, user-level dynamic diffusion framework centered on raw, unaggregated time series, preserving full temporal structure without dimensional reduction. Integrating six computational paradigms—sequence analysis, process mining, language modeling, temporal pattern discovery, trajectory clustering, and behavioral modeling—we establish a theory-guided, computationally synergistic pipeline. Validated on 1.26 million donation-related digital traces from 309 users, our approach uncovers three key empirical regularities: (1) recurrent periodicity in sharing behavior, (2) path-dependent propagation mechanisms, and (3) heterogeneous individual-level temporal patterns. The framework provides a scalable, generalizable methodological foundation for dynamic communication modeling.
📝 Abstract
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.