A deep learning model for detecting mental illness from user content on social media

Sci Rep. 2020 Jul 16;10(1):11846. doi: 10.1038/s41598-020-68764-y.

Abstract

Users of social media often share their feelings or emotional states through their posts. In this study, we developed a deep learning model to identify a user's mental state based on his/her posting information. To this end, we collected posts from mental health communities in Reddit. By analyzing and learning posting information written by users, our proposed model could accurately identify whether a user's post belongs to a specific mental disorder, including depression, anxiety, bipolar, borderline personality disorder, schizophrenia, and autism. We believe our model can help identify potential sufferers with mental illness based on their posts. This study further discusses the implication of our proposed model, which can serve as a supplementary tool for monitoring mental health states of individuals who frequently use social media.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Anxiety / diagnosis*
  • Anxiety / psychology
  • Autistic Disorder / diagnosis*
  • Autistic Disorder / psychology
  • Bipolar Disorder / diagnosis*
  • Bipolar Disorder / psychology
  • Blogging
  • Borderline Personality Disorder / diagnosis*
  • Borderline Personality Disorder / psychology
  • Deep Learning*
  • Depression / diagnosis*
  • Depression / psychology
  • Female
  • Humans
  • Male
  • Mental Health / classification
  • Phonetics
  • Psycholinguistics / methods
  • Schizophrenia / diagnosis*
  • Schizophrenia / physiopathology
  • Semantics
  • Social Media