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Strategies for Coping with Twitter's "Dark Side": A Transactional Model of Stress and Coping Analysis

Authors: Sin, Sei-Ching Joanna; Kim, Kyung-Sun;

Strategies for Coping with Twitter's "Dark Side": A Transactional Model of Stress and Coping Analysis

Abstract

Social media's "dark side"—including dis/misinformation and harassment—is increasingly problematic. What strategies do people use to cope with such issues in their daily life? Based on Lazarus and Folkman's Transactional Model of Stress and Coping (TMSC), this study investigated how individuals cope with such issues on Twitter. An online survey, including the Ways of Coping Questionnaire, was used to analyze 800 US adult Twitter users' coping strategies. The study found Folkman and colleagues' 8-strategy grouping applicable to this context. The top coping strategies were Distancing (e.g., "Went on as if nothing had happened"), Self-Controlling (e.g., "I tried to keep my feelings to myself"), and Planful Problem-Solving (e.g., "I made a plan of action and followed it"). Primary and secondary appraisals were associated with most strategies (7 out of 8). Significant demographic differences were found, especially with Twitter usage frequency and gender. Implications for research and practice are discussed.

Keywords

Coping strategies, Information behavior, Individual differences, Twitter, Social media problems

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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