在COVID-19大流行期间识别高自杀风险的Reddit用户及其语言特征:基于增长的轨迹模型
Yifei Yan1, Jun Li2, Xingyun Liu3
1Department of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, China (Hong Kong).
Journal of medical Internet research
|August 8, 2024
概括
这项研究在COVID-19大流行期间在社交媒体上确定了两个不同的自杀风险轨迹. 一个高风险群体显示出更多的发布,具有独特的语言模式,突出了实时社交媒体监控的需要.
科学领域:
- 公共卫生监督 公共卫生监督
- 计算社会科学 计算社会科学
- 心理健康研究 心理健康研究
背景情况:
- 由于COVID-19大流行,公众健康问题增加了自杀风险.
- 社交媒体成为表达自杀想法的关键平台.
- 现有的研究往往忽视了用户多样性和自杀风险的时间动态.
研究的目的:
- 在COVID-19大流行期间使用r/SuicideWatch的后体积轨迹调查异构的自杀风险模式.
- 识别高自杀风险的社交媒体用户.
- 在大流行之前和期间,对高风险用户的语言特征进行表征.
主要方法:
- 从2019年3月到2022年8月的r/SuicideWatch用户 (N=6163) 的分析后数据.
- 采用基于增长的轨迹模型来识别后体积和自杀风险的变化.
- 通过回归分析检查了语言特征趋势,并通过回归分析确定了轨迹组中的标记物.
主要成果:
- 确定了两种不同的邮件量轨迹:一个高风险组 (12.07%) 邮件数量急剧增加,一个低风险组 (87.93%) 邮件数量轻微增加.
- 两个群体的语言特征初步增加,但高风险组的趋势在下降之前继续下降,而低风险组则下降.
- 高风险群体表现出与愤怒,悲伤,健康,成就,运动,未来关注和死亡有关的独特语言使用.
结论:
- 在r/SuicideWatch上的用户可以根据与流行病相关的后体积轨迹分为高和低自杀风险组.
- 自杀风险在应对流行病等公共卫生危机时表现出异质的变化.
- 建议实时监控社交媒体,以便在未来的危机中及时提供支持.
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