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

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dc.contributor.authorFan, Yangliu
dc.contributor.authorOhme, Jakob
dc.contributor.authorWedel, Lion
dc.date.accessioned2025-07-21T14:14:07Z
dc.date.available2025-07-21T14:14:07Z
dc.date.issued2025
dc.description.abstractCommunication 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.
dc.identifier.citationFan, Y., Ohme, J., & Wedel, L. (2025). Exploring temporal dynamics in digital trace data: Mining user-sequences for communication research (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2505.18790
dc.identifier.doihttps://doi.org/10.48550/arxiv.2505.18790
dc.identifier.urihttps://www.weizenbaum-library.de/handle/id/932
dc.language.isoeng
dc.publisherarXiv
dc.rightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectDigital trace data
dc.subjectsequence analysis
dc.subjectlongitudinal data
dc.subjectplatform research
dc.subjectcomputational method
dc.titleExploring temporal dynamics in digital trace data: mining user-sequences for communication research
dc.typePreprint
dc.type.statuspreprintVersion
dcmi.typeText
dcterms.bibliographicCitation.urlhttps://arxiv.org/abs/2505.18790
local.researchgroupDynamiken digitaler Nachrichtenvermittlung
local.researchtopicDigitale Märkte und Öffentlichkeiten auf Plattformen
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