MentalHelp: A Multi-Task Dataset for Mental Health in Social Media

Nishat Raihan, Sadiya Sayara Chowdhury Puspo, Shafkat Farabi, Ana-Maria Bucur, Tharindu Ranasinghe, Marcos Zampieri

Abstract
Early detection of mental health disorders is an essential step in treating and preventing mental health conditions. Computational approaches have been applied to users’ social media profiles in an attempt to identify various mental health conditions such as depression, PTSD, schizophrenia, and eating disorders. The interest in this topic has motivated the creation of various depression detection datasets. However, annotating such datasets is expensive and time-consuming, limiting their size and scope. To overcome this limitation, we present MentalHelp, a large-scale semi-supervised mental disorder detection dataset containing 14 million instances. The corpus was collected from Reddit and labeled in a semi-supervised way using an ensemble of three separate models - flan-T5, Disor-BERT, and Mental-BERT.
Anthology ID:
2024.lrec-main.977
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
11196–11203
Language:
URL:
https://aclanthology.org/2024.lrec-main.977
DOI:
Bibkey:
Cite (ACL):
Nishat Raihan, Sadiya Sayara Chowdhury Puspo, Shafkat Farabi, Ana-Maria Bucur, Tharindu Ranasinghe, and Marcos Zampieri. 2024. MentalHelp: A Multi-Task Dataset for Mental Health in Social Media. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11196–11203, Torino, Italia. ELRA and ICCL.
Cite (Informal):
MentalHelp: A Multi-Task Dataset for Mental Health in Social Media (Raihan et al., LREC-COLING 2024)
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