Automatic detection of eating disorder-related social media posts that could benefit from a mental health intervention

Hao Yan, Ellen E. Fitzsimmons-Craft, Micah Goodman, Melissa Krauss, Sanmay Das, Patricia Cavazos-Rehg

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Objective: Online forums allow people to semi-anonymously discuss their struggles, often leading to greater honesty. This characteristic makes forums valuable for identifying users in need of immediate help from mental health professionals. Because it would be impractical to manually review every post on a forum to identify users in need of urgent help, there may be value to developing algorithms for automatically detecting posts reflecting a heightened risk of imminent plans to engage in disordered behaviors. Method: Five natural language processing techniques (tools to perform computational text analysis) were used on a data set of 4,812 posts obtained from six eating disorder-related subreddits. Two licensed clinical psychologists labeled 53 of these posts, deciding whether or not the content of the post indicated that its author needed immediate professional help. The remaining 4,759 posts were unlabeled. Results: Each of the five techniques ranked the 50 posts most likely to be intervention-worthy (the “top-50”). The two most accurate detection techniques had an error rate of 4% for their respective top-50. Discussion: This article demonstrates the feasibility of automatically detecting—with only a few dozen labeled examples—the posts of individuals in need of immediate mental health support for an eating disorder.

Original languageEnglish
Pages (from-to)1150-1156
Number of pages7
JournalInternational Journal of Eating Disorders
Volume52
Issue number10
DOIs
StatePublished - Oct 1 2019

Keywords

  • eating disorders
  • machine learning
  • mass screening
  • natural language processing
  • social media

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