Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: AI-Enabled Content Analysis of Online Mental Health Communities.

Journal: Journal of medical Internet research
PMID: 39919286

Abstract

BACKGROUND: Early maladaptive schemas (EMSs) are pervasive, self-defeating patterns of thoughts and emotions underlying most mental health problems and are central in schema therapy. However, the characteristics of EMSs vary across demographics, and despite the growing use of online mental health communities (OMHCs), how EMSs manifest in these online support-seeking environments remains unclear. Understanding these characteristics could inform the design of more effective interventions powered by artificial intelligence to address online support seekers' unique therapeutic needs.

Authors

  • Beng Heng Ang
    Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore, Singapore.
  • Sujatha Das Gollapalli
    Institute of Data Science, National University of Singapore, Singapore, Singapore.
  • Mingzhe Du
    Institute of Data Science, National University of Singapore, Singapore, Singapore.
  • See-Kiong Ng
    Institute of Data Science, National University of Singapore, Singapore, Singapore.

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