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

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.

Crime and social media

“…Robinson et al (2017) concluded that social media could potentially be an important mechanism for the prevention and moderation of distress as well as suicidal behavior and thoughts. This is consistent with the position of Bryan et al (2018) who subsequently found that certain sequences in the content of social media could predict the cause and timeline of death by suicide. The conclusions of Bryan et al (2018) are in line with Tan et al (2017) who reported that whereas web-based interventions can be effective in the prevention of online suicide, it is also imperative to increase user engagement with online information and discussion groups.…”

“…Noticeably, the foregoing literature has the shortcoming of being too exploratory and countryspecific. For instance, the study by Robinson et al (2017), Tan et al (2017) and Chen et al (2017) were focused on China while, Bryan et al (2018) was concerned with the United States of America. The present study addresses this country-specific shortcoming by employing a global dataset of 148 countries on the one hand and on the other, decomposing the dataset into regions and income levels, in order to increase the policy relevance of the study.…”

Digital conversations about suicide among teenagers and adults with epilepsy: A big‐data, machine learning analysis

“…The finding that teenagers and young adults are more likely to share their suicide risk factors on social media sites than with their doctors 24 highlight the importance of availability of such online venues and the need for harnessing this readily available individualized information to prevent tragedies like suicide. In fact, the analysis of social media content of military personnel who died by suicide compared to deaths from other causes showed that it could not only predict the cause of death but also the timing of suicide 25 …”

Impact of Sustainable Product Design on Elderly Ergonomics

“…The two coders independently identified codes and grouped into themes, then compared the themes. Discrepancies were discussed and resolved among the two coders [11,33].…”