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Area of Science:

  • Computational Social Science
  • Public Health Informatics
  • Artificial Intelligence in Mental Health

Background:

  • Suicide is a significant global public health issue requiring effective prevention strategies.
  • Traditional suicide risk screening methods face challenges in early detection.
  • Individuals express suicidal thoughts on social media platforms like Reddit, offering new avenues for detection.

Purpose of the Study:

  • To investigate machine learning (ML) and natural language processing (NLP) methods for detecting suicidal ideation on Reddit.
  • To systematically review recent literature on ML/NLP applications for suicidality detection in online forums.

Main Methods:

  • Conducted a systematic literature review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
  • Selected 26 relevant studies published between 2018 and 2022 focusing on ML/NLP techniques applied to Reddit data.
  • Analyzed methods for data collection, annotation, preprocessing, feature engineering, model development, and evaluation.

Main Results:

  • Identified prevalent ML and NLP techniques used for detecting suicidal ideation on Reddit.
  • Cataloged various Reddit-based datasets employed in developing suicidality detection models.
  • Outlined common approaches across the lifecycle of model development, from data handling to evaluation.

Conclusions:

  • ML and NLP offer promising tools for identifying suicidal ideation within online communities like Reddit.
  • The review provides insights into current methodologies and available datasets for this critical research area.
  • Further research is needed to address existing limitations and explore future directions in online suicidality detection.