National Center for Veterans Studies, The University of Utah, Salt Lake City, UT, USA.
Northrop Grumman Technology Services, Herndon, VA, USA.
Suicide Life Threat Behav. 2018 Aug;48(4):413-430. doi: 10.1111/sltb.12370. Epub 2017 Jul 28.
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.
自杀是美国的主要死亡原因之一,也是美国军队的第二大死亡原因。先前的研究表明,从社交媒体网络中获取的数据可能为识别高危个体提供重要线索。为了验证这一可能性,对 315 名自杀(n=157)或其他原因(n=158)死亡的军人的社交媒体资料进行了编码,以确定是否存在压力生活情况(即触发器)、躯体抱怨或健康问题(即身体)、适应不良或回避应对策略(即行为)、负面情绪状态(即情绪)和/或消极认知评价(认知)。随后,使用动态系统视角的多层次模型分析内容代码,以确定与自杀死亡特征相关的时间变化过程。结果确定了自杀特有的时间序列,特别是关于触发器的社交媒体帖子之后是更多关于认知的帖子,关于认知的帖子之后是更多关于触发器的帖子,以及关于行为的帖子之后是关于认知的帖子减少。结果表明,社交媒体内容中的某些序列可能预测死亡原因,并估计社交媒体用户何时可能自杀。