title: A Linguistic Analysis of Suicide-Related Twitter Posts.
abstract: BACKGROUND
  Suicide is a leading cause of death worldwide. Identifying those at risk and delivering timely interventions is challenging. Social media site Twitter is used to express suicidality. Automated linguistic analysis of suicide-related posts may help to differentiate those who require support or intervention from those who do not.
  
  
  OBJECTIVE
  This study aims to characterize the linguistic profiles of suicide-related Twitter posts.
  
  
  METHODS
  Using a dataset of suicide-related Twitter posts previously coded for suicide risk by experts, Linguistic Inquiry and Word Count (LIWC) and regression analyses were conducted to determine differences in linguistic profiles.
  
  
  RESULTS
  When compared with matched non-suicide-related Twitter posts, strongly concerning suicide-related posts were characterized by a higher word count, increased use of first-person pronouns, and more references to death. When compared with safe-to-ignore suicide-related posts, strongly concerning suicide-related posts were characterized by increased use of first-person pronouns, greater anger, and increased focus on the present. Other differences were found.
  
  
  CONCLUSIONS
  The predictive validity of the identified features needs further testing before these results can be used for interventional purposes.
  
  
  CONCLUSIONS
  This study demonstrates that strongly concerning suicide-related Twitter posts have unique linguistic profiles. The examination of Twitter data for the presence of such features may help to validate online risk assessments and determine those in need of further support or intervention.
authors.affiliation: 1 Black Dog Institute, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
authors.forename: Bridianne
authors.initials: B
authors.lastname: O'Dea
authors.affiliation: 1 Black Dog Institute, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
authors.forename: Mark E
authors.initials: ME
authors.lastname: Larsen
authors.affiliation: 2 Centre for Mental Health Research, Australian National University, Canberra, ACT, Australia.
authors.forename: Philip J
authors.initials: PJ
authors.lastname: Batterham
authors.affiliation: 2 Centre for Mental Health Research, Australian National University, Canberra, ACT, Australia.
authors.forename: Alison L
authors.initials: AL
authors.lastname: Calear
authors.affiliation: 1 Black Dog Institute, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
authors.forename: Helen
authors.initials: H
authors.lastname: Christensen
mesh_terms: D016022:Case-Control Studies; D005260:Female; D006801:Humans; D008037:Linguistics; D008297:Male; D011578:Psycholinguistics; D012307:Risk Factors; D061108:Social Media; D013405:Suicide / Q000523:psychology*
keywords: Twitter; linguistic analysis; prevention; social media; suicide