Exploring temporal dynamics in digital trace data: mining user-sequences for communication research
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911668068417536 |
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| author | Fan, Yangliu Ohme, Jakob Wedel, Lion |
| author_facet | Fan, Yangliu Ohme, Jakob Wedel, Lion |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18790 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring temporal dynamics in digital trace data: mining user-sequences for communication research Fan, Yangliu Ohme, Jakob Wedel, Lion Social and Information Networks 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. |
| title | Exploring temporal dynamics in digital trace data: mining user-sequences for communication research |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2505.18790 |