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Detecting Coping Style from Twitter

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Social Informatics (SocInfo 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10046))

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Abstract

Coping styles are psychological and behavioral strategies people use to deal with stressful situations. They may be adaptive (helping to reduce stressors), or maladaptive (which tend to reduce symptoms without addressing the underlying problem). Some coping styles—particularly maladaptive ones—are tied to specific conditions.

This study explores whether coping style can be predicted by analyzing user behavior on Twitter. Our results show that a combination of text analysis and behavioral information can be used to build a classifier that can accurately determine whether individuals use primarily adaptive or maladaptive coping styles. Furthermore, we show this can be predicted using a small feature set of psycholinguistic measures, which directly map to core elements of coping as identified in the psychological literature.

In addition to the results contributing to the literature on individual attribute prediction, information about coping strategies is useful for understanding more complex psychological phenomena (like addiction and PTSD). Understanding such attributes is of growing interest to the research community, and our results add a tool to support further work in that area. Our results may also be useful in contributing to personalization, especially in health-related topics, and to a personal analysis tool to guide people toward building healthier coping styles if their current actions are maladaptive.

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Author information

Authors and Affiliations

  1. College of Information Studies, University of Maryland, College Park, Maryland, 20742, USA

    Jennifer Golbeck

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  1. Jennifer Golbeck
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Correspondence to Jennifer Golbeck .

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Editors and Affiliations

  1. University of Washington, Seattle, Washington, USA

    Emma Spiro

  2. Indiana University, Bloomington, Indiana, USA

    Yong-Yeol Ahn

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Golbeck, J. (2016). Detecting Coping Style from Twitter. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10046. Springer, Cham. https://doi.org/10.1007/978-3-319-47880-7_28

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  • DOI: https://doi.org/10.1007/978-3-319-47880-7_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47879-1

  • Online ISBN: 978-3-319-47880-7

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