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Mental Health Disorder Detection beyond Social Media: A Systematic Review of Available Datasets

Summary

Most NLP work on automatically detecting mental health disorders is built on social media posts. That data has known problems: skewed user demographics, self-selection of people who discuss their condition openly, consent and privacy issues, and shrinking API access. This paper asks what free-text alternatives exist outside social media. It runs a PRISMA-guided systematic review of Google Scholar and PubMed via Publish or Perish, covering literature up to June 2025. The authors screened for new corpora, removed social-media, audio/visual-only and purely numeric datasets, and traced original dataset papers through citations. They ended with 45 datasets, which they compare by language, disorder, source platform, text type, annotation scheme and availability. The resource is skewed toward English and toward depression. Labeling practices are inconsistent and sometimes undocumented. The authors argue for multilingual resources, broader coverage of conditions, transparent annotation grounded in DSM/ICD criteria, and LLM-assisted labeling (CoT, CoE prompting). These would help build clinically credible detection models.

Key points

  • Screening funnel: 543 candidate papers became 259 after removing social-media studies, then 57 relevant papers (40 with new datasets). Backtracking added 5 more, giving 45 datasets published 2004–2025.
  • English accounts for 62.2% of datasets and Chinese for 17.8%. The rest include Korean, Polish, Greek, Japanese, Arabic and code-mixed data.
  • Covered conditions include depression (dominant), postnatal depression, anxiety, PTSD, schizophrenia, bipolar disorder and suicidal ideation.
  • Text types include interview transcripts, EHR/EMR discharge summaries, clinical notes, essays, SMS, therapy dialogues and suicide notes. Availability ranges from public to DUA-restricted to unspecified, and more Chinese than English datasets are openly accessible.
  • Dataset releases peaked in 2022, likely linked to post-COVID-19 attention to mental health. The dataset list is published on GitHub (SadiyaPuspo/MHD-Beyond-Social-Media-Datasets-Review).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/32pcux3ahpau

@inproceedings{puspo2026mental,
  title = {{Mental Health Disorder Detection beyond Social Media: A Systematic Review of Available Datasets}},
  author = {Puspo, Sadiya Sayara Chowdhury and Bucur, Ana-Maria and Chancellor, Stevie and Uzuner, Özlem and Zampieri, Marcos},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/32pcux3ahpau},
  url = {https://doi.org/10.63317/32pcux3ahpau}
}

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