Citation

Ophir Y, Tikochinski R, Brunstein Klomek A, Reichart R. Clinical Psychological Science 2022; 10(2): 212-235.

Copyright

(Copyright © 2022, Association for Psychological Science, Publisher SAGE Publishing)

DOI

10.1177/21677026211022013

PMID

unavailable

Abstract

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. © The Author(s) 2021.

Language: en

Keywords

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