Literature DB >> 28752655

Predictors of Emerging Suicide Death Among Military Personnel on Social Media Networks.

Craig J Bryan1, Jonathan E Butner1, Sungchoon Sinclair1, Anna Belle O Bryan1, Christina M Hesse2, Andree E Rose3.   

Abstract

Suicide is a leading cause of death in the United States and is the second leading cause of death in the U.S. military. Previous research suggests that data obtained from social media networks may provide important clues for identifying at-risk individuals. To test this possibility, the social media profiles from 315 military personnel who died by suicide (n = 157) or other causes (n = 158) were coded for the presence of stressful life situations (i.e., triggers), somatic complaints or health issues (i.e., physical), maladaptive or avoidant coping strategies (i.e., behaviors), negative mood states (i.e., emotion), and/or negative cognitive appraisals (cognition). Content codes were subsequently analyzed using multilevel models from a dynamical systems perspective to identify temporal change processes characteristic of suicide death. Results identified temporal sequences unique to suicide, notably social media posts about triggers followed by more posts about cognitions, posts about cognitions followed by more posts about triggers, and posts about behaviors followed by fewer posts about cognitions. Results suggest that certain sequences in social media content may predict cause of death and provide an estimate of when a social media user is likely to die by suicide.
© 2017 The American Association of Suicidology.

Entities:  

Mesh:

Year:  2017        PMID: 28752655     DOI: 10.1111/sltb.12370

Source DB:  PubMed          Journal:  Suicide Life Threat Behav        ISSN: 0363-0234


  16 in total

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3.  Nonlinear change processes and the emergence of suicidal behavior: a conceptual model based on the fluid vulnerability theory of suicide.

Authors:  Craig J Bryan; Jonathan E Butner; Alexis M May; Kelsi F Rugo; Julia Harris; D Nicolas Oakey; David C Rozek; AnnaBelle O Bryan
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Review 4.  Leveraging data science to enhance suicide prevention research: a literature review.

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5.  Pre-deployment predictors of suicide attempt during and after combat deployment: Results from the Army Study to Assess Risk and Resilience in Servicemembers.

Authors:  Kelly L Zuromski; Samantha L Bernecker; Carol Chu; Chelsey R Wilks; Peter M Gutierrez; Thomas E Joiner; Howard Liu; James A Naifeh; Matthew K Nock; Nancy A Sampson; Alan M Zaslavsky; Murray B Stein; Robert J Ursano; Ronald C Kessler
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6.  Introducing the Psychological Autopsy Methodology Checklist.

Authors:  Kenneth R Conner; Benjamin P Chapman; Annette L Beautrais; David A Brent; Jeffrey A Bridge; Yeates Conwell; Tyler Falter; Amanda Holbrook; Barbara Schneider
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7.  Intervention among Suicidal Men: Future Directions for Telephone Crisis Support Research.

Authors:  Tara Hunt; Coralie J Wilson; Alan Woodward; Peter Caputi; Ian Wilson
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Review 8.  Natural Language Processing of Social Media as Screening for Suicide Risk.

Authors:  Glen Coppersmith; Ryan Leary; Patrick Crutchley; Alex Fine
Journal:  Biomed Inform Insights       Date:  2018-08-27

9.  Help-Seeking on Facebook Versus More Traditional Sources of Help: Cross-Sectional Survey of Military Veterans.

Authors:  Alan R Teo; Heather E Marsh; Samuel B L Liebow; Jason I Chen; Christopher W Forsberg; Christina Nicolaidis; Somnath Saha; Steven K Dobscha
Journal:  J Med Internet Res       Date:  2018-02-26       Impact factor: 5.428

10.  Improving risk prediction accuracy for new soldiers in the U.S. Army by adding self-report survey data to administrative data.

Authors:  Samantha L Bernecker; Anthony J Rosellini; Matthew K Nock; Wai Tat Chiu; Peter M Gutierrez; Irving Hwang; Thomas E Joiner; James A Naifeh; Nancy A Sampson; Alan M Zaslavsky; Murray B Stein; Robert J Ursano; Ronald C Kessler
Journal:  BMC Psychiatry       Date:  2018-04-03       Impact factor: 3.630

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