• Bridianne O'Dea, Mark E Larsen, Philip J Batterham, Alison L Calear, and Helen Christensen.
• 1 Black Dog Institute, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
• Crisis. 2017 Sep 1; 38 (5): 319-329.

BackgroundSuicide 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.AimsThis study aims to characterize the linguistic profiles of suicide-related Twitter posts.MethodUsing 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.ResultsWhen 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.LimitationsThe predictive validity of the identified features needs further testing before these results can be used for interventional purposes.ConclusionThis 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.

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