The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention

Yaakov Ophir, Refael Tikochinski, Anat Brunstein Klomek, Roi Reichart

Research output: Contribution to journalReview articlepeer-review

7 Scopus citations


Suicide, a leading cause of death, is a complex and a hard-to-predict human tragedy. In this article, we introduce a comprehensive outlook on the emerging movement to integrate computational linguistics (CL) in suicide prevention research and practice. Focusing mainly on the state-of-the-art deep neural network models, in this “travel guide” article, we describe, in a relatively plain language, how CL methodologies could facilitate early detection of suicide risk. Major potential contributions of CL methodologies (e.g., word embeddings, interpretational frameworks) for deepening that theoretical understanding of suicide behaviors and promoting the personalized approach in psychological assessment are presented as well. We also discuss principal ethical and methodological obstacles in CL suicide prevention, such as the difficulty to maintain people’s privacy/safety or interpret the “black box” of prediction algorithms. Ethical guidelines and practical methodological recommendations addressing these obstacles are provided for future researchers and clinicians.

Original languageEnglish
Pages (from-to)212-235
Number of pages24
JournalClinical Psychological Science
Issue number2
StatePublished - Mar 2022
Externally publishedYes


  • artificial intelligence
  • computational linguistics
  • deep neural networks
  • machine learning
  • natural language processing
  • suicide prevention


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