Yan Yifei, Li Jun, Liu Xingyun, Li Qing, Yu Nancy Xiaonan
Department of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, China (Hong Kong).
Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China (Hong Kong).
J Med Internet Res. 2024 Aug 8;26:e48907. doi: 10.2196/48907.
Suicide has emerged as a critical public health concern during the COVID-19 pandemic. With social distancing measures in place, social media has become a significant platform for individuals expressing suicidal thoughts and behaviors. However, existing studies on suicide using social media data often overlook the diversity among users and the temporal dynamics of suicide risk.
By examining the variations in post volume trajectories among users on the r/SuicideWatch subreddit during the COVID-19 pandemic, this study aims to investigate the heterogeneous patterns of change in suicide risk to help identify social media users at high risk of suicide. We also characterized their linguistic features before and during the pandemic.
We collected and analyzed post data every 6 months from March 2019 to August 2022 for users on the r/SuicideWatch subreddit (N=6163). A growth-based trajectory model was then used to investigate the trajectories of post volume to identify patterns of change in suicide risk during the pandemic. Trends in linguistic features within posts were also charted and compared, and linguistic markers were identified across the trajectory groups using regression analysis.
We identified 2 distinct trajectories of post volume among r/SuicideWatch subreddit users. A small proportion of users (744/6163, 12.07%) was labeled as having a high risk of suicide, showing a sharp and lasting increase in post volume during the pandemic. By contrast, most users (5419/6163, 87.93%) were categorized as being at low risk of suicide, with a consistently low and mild increase in post volume during the pandemic. In terms of the frequency of most linguistic features, both groups showed increases at the initial stage of the pandemic. Subsequently, the rising trend continued in the high-risk group before declining, while the low-risk group showed an immediate decrease. One year after the pandemic outbreak, the 2 groups exhibited differences in their use of words related to the categories of personal pronouns; affective, social, cognitive, and biological processes; drives; relativity; time orientations; and personal concerns. In particular, the high-risk group was discriminant in using words related to anger (odds ratio [OR] 3.23, P<.001), sadness (OR 3.23, P<.001), health (OR 2.56, P=.005), achievement (OR 1.67, P=.049), motion (OR 4.17, P<.001), future focus (OR 2.86, P<.001), and death (OR 4.35, P<.001) during this stage.
Based on the 2 identified trajectories of post volume during the pandemic, this study divided users on the r/SuicideWatch subreddit into suicide high- and low-risk groups. Our findings indicated heterogeneous patterns of change in suicide risk in response to the pandemic. The high-risk group also demonstrated distinct linguistic features. We recommend conducting real-time surveillance of suicide risk using social media data during future public health crises to provide timely support to individuals at potentially high risk of suicide.
在新冠疫情期间,自杀已成为一个关键的公共卫生问题。随着社交距离措施的实施,社交媒体已成为个人表达自杀想法和行为的重要平台。然而,现有的利用社交媒体数据进行的自杀研究往往忽视了用户之间的差异以及自杀风险的时间动态变化。
通过研究新冠疫情期间r/SuicideWatch子版块用户发帖量轨迹的变化,本研究旨在调查自杀风险变化的异质性模式,以帮助识别自杀高风险的社交媒体用户。我们还对疫情前和疫情期间他们的语言特征进行了描述。
我们收集并分析了2019年3月至2022年8月期间r/SuicideWatch子版块用户每6个月的发帖数据(N = 6163)。然后使用基于增长的轨迹模型来研究发帖量轨迹,以识别疫情期间自杀风险的变化模式。还绘制并比较了帖子中语言特征的趋势,并使用回归分析在轨迹组中识别语言标记。
我们在r/SuicideWatch子版块用户中识别出两种不同的发帖量轨迹。一小部分用户(744/6163,12.07%)被标记为自杀风险高,在疫情期间发帖量急剧且持续增加。相比之下,大多数用户(5419/6163,87.93%)被归类为自杀风险低,在疫情期间发帖量一直较低且略有增加。就大多数语言特征的频率而言,两组在疫情初期均呈现增加趋势。随后,高风险组的上升趋势在下降之前持续,而低风险组则立即下降。疫情爆发一年后,两组在与个人代词类别、情感、社会、认知和生物过程、驱动力、相对性、时间取向和个人关注相关的词汇使用上表现出差异。特别是,在这个阶段,高风险组在使用与愤怒(优势比[OR] 3.23,P <.001)、悲伤(OR 3.23,P <.001)、健康(OR 2.56,P =.005)、成就(OR 1.67,P =.049)、运动(OR 4.17,P <.001)、未来关注(OR 2.86,P <.001)和死亡(OR 4.35,P <.001)相关的词汇方面具有判别性。
基于疫情期间识别出的两种发帖量轨迹,本研究将r/SuicideWatch子版块的用户分为自杀高风险组和低风险组。我们的研究结果表明,自杀风险对疫情的反应存在异质性变化模式。高风险组也表现出独特的语言特征。我们建议在未来的公共卫生危机期间利用社交媒体数据对自杀风险进行实时监测,以便为潜在的自杀高风险个体提供及时支持。