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        <title>Mental Health Disorder Detection beyond Social Media: A Systematic Review of Available Datasets</title>
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    <abstract>Detecting mental health disorders in a timely manner is an important societal challenge. NLP and machine learning (ML) methods used to assist with detection rely on data collected primarily from social media. However, such datasets often have sampling biases and inherent ethical and privacy issues. One avenue to overcome these limitations is non-social media data. We present the first comprehensive review of non-social media, free-text datasets for mental health research. We use the PRISMA methodology to conduct our survey and we review datasets available in multiple languages. We find that non-social media free-text based datasets are predominantly focused on English and on detecting depression. These datasets also vary in demographics, platforms, data types, annotation techniques, and methodologies. This systematic review also reveals key gaps and highlights opportunities to develop more diverse, reliable and clinically-relevant resources.</abstract>
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%0 Conference Proceedings
%T Mental Health Disorder Detection beyond Social Media: A Systematic Review of Available Datasets
%A Puspo, Sadiya Sayara Chowdhury
%A Bucur, Ana-Maria
%A Chancellor, Stevie
%A Uzuner, Özlem
%A Zampieri, Marcos
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F puspo-etal-2026-mental
%X Detecting mental health disorders in a timely manner is an important societal challenge. NLP and machine learning (ML) methods used to assist with detection rely on data collected primarily from social media. However, such datasets often have sampling biases and inherent ethical and privacy issues. One avenue to overcome these limitations is non-social media data. We present the first comprehensive review of non-social media, free-text datasets for mental health research. We use the PRISMA methodology to conduct our survey and we review datasets available in multiple languages. We find that non-social media free-text based datasets are predominantly focused on English and on detecting depression. These datasets also vary in demographics, platforms, data types, annotation techniques, and methodologies. This systematic review also reveals key gaps and highlights opportunities to develop more diverse, reliable and clinically-relevant resources.
%R 10.63317/32pcux3ahpau
%U https://aclanthology.org/2026.lrec-1.494/
%U https://doi.org/10.63317/32pcux3ahpau
%P 6235-6250

Markdown (Informal)

[Mental Health Disorder Detection beyond Social Media: A Systematic Review of Available Datasets](https://aclanthology.org/2026.lrec-1.494/) (Puspo et al., LREC 2026)

• Mental Health Disorder Detection beyond Social Media: A Systematic Review of Available Datasets (Puspo et al., LREC 2026)

ACL

• Sadiya Sayara Chowdhury Puspo, Ana-Maria Bucur, Stevie Chancellor, Özlem Uzuner, and Marcos Zampieri. 2026. Mental Health Disorder Detection beyond Social Media: A Systematic Review of Available Datasets. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 6235–6250, Palma de Mallorca, Spain. ELRA Language Resource Association.