Literature DB >> 35636131

Linguistic features of suicidal thoughts and behaviors: A systematic review.

Stephanie Homan1, Marion Gabi2, Nina Klee2, Sandro Bachmann3, Ann-Marie Moser3, Martina Duri'2, Sofia Michel4, Anna-Marie Bertram5, Anke Maatz6, Guido Seiler3, Elisabeth Stark7, Birgit Kleim8.   

Abstract

Language is a potential source of predictors for suicidal thoughts and behaviors (STBs), as changes in speech characteristics, communication habits, and word choice may be indicative of increased suicide risk. We reviewed the current literature on STBs that investigated linguistic features of spoken and written language. Specifically, we performed a search in linguistic, medical, engineering, and general databases for studies that investigated linguistic features as potential predictors of STBs published in peer-reviewed journals until the end of November 2021.We included 75 studies that investigated 279,032 individuals with STBs (age = 29.53 ± 10.29, 35% females). Of those, 34 (45%) focused on lexicon, 20 (27%) on prosody, 15 (20%) on lexicon and first-person singular, four (5%) on (morpho)syntax, and two (3%) were unspecified. Suicidal thoughts were predicted by more intensifiers and superlatives, while suicidal behaviors were predicted by greater usage of pronouns, changes in the amount of verb usage, more prepend and multifunctional words, more nouns and prepositions, and fewer modifiers and numerals. A diverse field of research currently investigates linguistic predictors of STBs, and more focus is needed on their specificity for either suicidal thoughts or behaviors.
Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  (morpho)syntax; First-person singular; Lexicon; Linguistic features; Prosody; Suicidal thoughts and behavior

Mesh:

Year:  2022        PMID: 35636131     DOI: 10.1016/j.cpr.2022.102161

Source DB:  PubMed          Journal:  Clin Psychol Rev        ISSN: 0272-7358


  2 in total

1.  Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion.

Authors:  Jingfang Liu; Mengshi Shi; Huihong Jiang
Journal:  Int J Environ Res Public Health       Date:  2022-07-05       Impact factor: 4.614

2.  Improving ascertainment of suicidal ideation and suicide attempt with natural language processing.

Authors:  Cosmin A Bejan; Michael Ripperger; Drew Wilimitis; Ryan Ahmed; JooEun Kang; Katelyn Robinson; Theodore J Morley; Douglas M Ruderfer; Colin G Walsh
Journal:  Sci Rep       Date:  2022-09-07       Impact factor: 4.996

  2 in total

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