Mitsuhashi Toshiharu
Center for Innovative Clinical Medicine, Okayama University Hospital, Okayama, Japan.
JMIR Form Res. 2023 Aug 10;7:e47798. doi: 10.2196/47798.
As the use of social media becomes more widespread, its impact on health cannot be ignored. However, limited research has been conducted on the relationship between social media and suicide. Little is known about individuals' vulnerable to suicide, especially when social media suicide information is extremely prevalent.
This study aims to identify the characteristics underlying individuals' vulnerability to suicide brought about by an increase in suicide-related tweets, thereby contributing to public health.
A case-only design was used to investigate vulnerability to suicide using individual data of people who died by suicide and tweet data from January 1, 2011, through December 31, 2014. Mortality data were obtained from Japanese government statistics, and tweet data were provided by a commercial service. Tweet data identified the days when suicide-related tweets surged, and the date-keyed merging was performed by considering 3 and 7 lag days. For the merged data set for analysis, the logistic regression model was fitted with one of the personal characteristics of interest as a dependent variable and the dichotomous exposure variable. This analysis was performed to estimate the interaction between the surges in suicide-related tweets and personal characteristics of the suicide victims as case-only odds ratios (ORs) with 95% CIs. For the sensitivity analysis, unexpected deaths other than suicide were considered.
During the study period, there were 159,490 suicides and 115,072 unexpected deaths, and the number of suicide-related tweets was 2,804,999. Following the 3-day lag of a highly tweeted day, there were significant interactions for those who were aged 40 years or younger (OR 1.09, 95% CI 1.03-1.15), male (OR 1.12, 95% CI 1.07-1.18), divorced (OR 1.11, 95% CI 1.03 1.19), unemployed (OR 1.12, 95% CI 1.02-1.22), and living in urban areas (OR 1.26, 95% CI 1.17 1.35). By contrast, widowed individuals had significantly lower interactions (OR 0.83, 95% CI 0.77-0.89). Except for unemployment, significant relationships were also observed for the 7-day lag. For the sensitivity analysis, no significant interactions were observed for other unexpected deaths in the 3-day lag, and only the widowed had a significantly larger interaction than those who were married (OR 1.08, 95% CI 1.02-1.15) in the 7-day lag.
This study revealed the interactions of personal characteristics associated with susceptibility to suicide-related tweets. In addition, a few significant relationships were observed in the sensitivity analysis, suggesting that such an interaction is specific to suicide deaths. In other words, individuals with these characteristics, such as being young, male, unemployed, and divorced, may be vulnerable to surges in suicide-related tweets. Thus, minimizing public health strain by identifying people who are vulnerable and susceptible to a surge in suicide-related information on the internet is necessary.
随着社交媒体的使用日益广泛，其对健康的影响不容忽视。然而，关于社交媒体与自杀之间关系的研究有限。对于易受自杀影响的个体，尤其是在社交媒体上自杀信息极为普遍的情况下，我们了解甚少。
本研究旨在确定因与自杀相关推文增加而导致个体易受自杀影响的潜在特征，从而为公共卫生做出贡献。
采用病例对照设计，利用2011年1月1日至2014年12月31日期间自杀死亡者的个体数据和推文数据来调查自杀易感性。死亡率数据来自日本政府统计，推文数据由一家商业服务机构提供。推文数据确定了与自杀相关推文激增的日期，并通过考虑3天和7天的滞后天数进行日期键合并。对于用于分析的合并数据集，将逻辑回归模型拟合为以感兴趣的个人特征之一为因变量和二分暴露变量。进行此分析是为了估计与自杀相关推文激增与自杀受害者个人特征之间的相互作用，作为具有95%置信区间的病例对照比值比（OR）。对于敏感性分析，考虑了除自杀以外的意外死亡。
在研究期间，有159,490例自杀和115,072例意外死亡，与自杀相关的推文数量为2,804,999条。在推文高发日滞后3天后，40岁及以下人群（OR 1.09，95% CI 1.03 - 1.15）、男性（OR 1.12，95% CI 1.07 - 1.18）、离婚者（OR 1.11，95% CI 1.03 - 1.19）、失业者（OR 1.12，95% CI 1.02 - 1.22）以及居住在城市地区的人群（OR 1.26，95% CI 1.17 - 1.35）存在显著相互作用。相比之下，丧偶者的相互作用显著较低（OR 0.83，95% CI 0.77 - 0.89）。除失业外，在滞后7天时也观察到显著关系。对于敏感性分析，在滞后3天时，其他意外死亡未观察到显著相互作用，在滞后7天时只有丧偶者的相互作用显著大于已婚者（OR 1.08，95% CI 1.02 - 1.15）。
本研究揭示了与对与自杀相关推文易感性相关的个人特征之间的相互作用。此外，在敏感性分析中观察到一些显著关系，表明这种相互作用特定于自杀死亡。换句话说，具有这些特征的个体，如年轻、男性、失业和离婚，可能易受与自杀相关推文激增的影响。因此，通过识别易受互联网上与自杀相关信息激增影响的脆弱人群来最小化公共卫生压力是必要的。
