arXiv — NLP / Computation & Language · · 3 min read

Linguistic Distancing on Social Media: Indicators of Emotion Regulation Across Age Groups

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Computer Science > Computation and Language

arXiv:2606.30957 (cs)
[Submitted on 29 Jun 2026]

Title:Linguistic Distancing on Social Media: Indicators of Emotion Regulation Across Age Groups

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Abstract:Managing our emotional responses to events is key to emotional well-being, a process referred to as emotion regulation in psychology. Previous work has established that the degree to which we distance events is a type of emotion regulation. When we psychologically distance from events there can be markers in our language. These markers have been referred to as linguistic distancing. We build upon a previous metric to operationalize linguistic distancing, and explore how it changes across the lifespan. We explore this systematically by analyzing large amounts of social media text, a venue where people express their emotions. By investigating how distancing varies across age groups we can better understand how emotion regulation varies with age and provide initial benchmarks on social media data. We provide additional evidence further strengthening the hypothesis that linguistic distancing occurs in proportionally more instances with age. These findings align with past work in psychology which indicate improved well-being with older age. Better understanding how linguistic distancing changes with age is important because it functions as a marker of well-being and can inform effective health interventions. We provide a foundation for further exploring emotion regulation through linguistic distancing in text data.
Comments: 13 pages, 3 figures, Computational Affective Science Workshop
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2606.30957 [cs.CL]
  (or arXiv:2606.30957v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2606.30957
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Daniela Teodorescu [view email]
[v1] Mon, 29 Jun 2026 22:27:40 UTC (253 KB)
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