MentalHelp: A Multi-Task Dataset for Mental Health in Social Media
MentalHelp is presented, a large-scale semi-supervised mental disorder detection dataset containing 14 million instances and labeled in a semi-supervised way using an ensemble of three separate models - flan-T5, Disor-BERT, and Mental-BERT.
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.