@inproceedings{raihan-etal-2024-mentalhelp,
title = "{M}ental{H}elp: A Multi-Task Dataset for Mental Health in Social Media",
author = "Raihan, Nishat and
Puspo, Sadiya Sayara Chowdhury and
Farabi, Shafkat and
Bucur, Ana-Maria and
Ranasinghe, Tharindu and
Zampieri, Marcos",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.977/",
pages = "11196--11203",
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."
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%0 Conference Proceedings
%T MentalHelp: A Multi-Task Dataset for Mental Health in Social Media
%A Raihan, Nishat
%A Puspo, Sadiya Sayara Chowdhury
%A Farabi, Shafkat
%A Bucur, Ana-Maria
%A Ranasinghe, Tharindu
%A Zampieri, Marcos
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F raihan-etal-2024-mentalhelp
%X 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.
%U https://aclanthology.org/2024.lrec-main.977/
%P 11196-11203
Markdown (Informal)
[MentalHelp: A Multi-Task Dataset for Mental Health in Social Media](https://aclanthology.org/2024.lrec-main.977/) (Raihan et al., LREC-COLING 2024)
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.