Digital Trace Data in Social Media Research: Constructing Opportunities and Contributions

Milad Mirbabaie () and Jonas Rieskamp
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Milad Mirbabaie: University of Bamberg, Faculty Information Systems and Applied Computer Sciences
Jonas Rieskamp: University of Bamberg, Faculty Information Systems and Applied Computer Sciences

Chapter Chapter 9 in Digital Trace Data Research in Information Systems, 2026, pp 199-224 from Springer

Abstract: Abstract Social media trace data offers valuable insights into social practices and public communications. The most used platforms include Twitter/X, Facebook, Instagram, TikTok, and LinkedIn. With its origins in the studies of political communication, information systems research adopted the social media analytics framework to study social media. The emergence of computationally intensive theory construction gave rise to increased scholarly acceptance of social media trace data. Following recent criticism on the social media analytics framework with regard to its capacities for theorizing, we borrow means of phenomenon-focused problematization and the temporal dynamics methodology for computationally intensive social media research to discuss ways for constructing opportunities and constructing contributions. Grounded in this discussion, we span the theorizing superstructure, which poses an accompanying phase that overarches the traditional phases of the social media analytics framework.

Keywords: Text mining; Social media; Social media analytics framework (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-032-05497-5_9

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DOI: 10.1007/978-3-032-05497-5_9

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