A text mining and machine learning study on the trends of and dynamics between collective action and mental health in politically polarized online environments

Calvin Lam and Christian S. Chan ()
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Calvin Lam: The University of Hong Kong
Christian S. Chan: The University of Hong Kong

Journal of Computational Social Science, 2024, vol. 7, issue 2, No 9, 1379-1401

Abstract: Abstract Social media and online forums play an increasingly important role in the mobilization of collective action. This study examined how the discussion of collective actions impacts the expression of psychological distress in politically polarized online environments. We used text mining and machine learning models to analyze 39,487,911 user-generated comments during the 2019 social unrest in Hong Kong on two online forums frequented by anti-government (Lihkg.com) and pro-government (Discuss.com.hk) netizens. Results from time-series models yielded two main findings. First, there was a time-lagged association between the discussion of protest and the mention of psychological distress on both forums. Second, on Discuss.com.hk but not Lihkg.com, fewer comments containing psychological distress were created on days with offline protests (especially on days with violent conflicts) than days without. Together, these findings suggest that politically polarizing environments contribute to psychological distress.

Keywords: Collective actions; Psychological distress discourse; Political polarity; Social media; Text mining & machine learning; Time-series (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s42001-024-00274-7

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