@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.",
}

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.
Raihan, Nishat Puspo, Sadiya Sayara Chowdhury Farabi, Shafkat Bucur, Ana-Maria Ranasinghe, Tharindu Zampieri, Marcos
MentalHelp: A Multi-Task Dataset for Mental Health in Social Media