Department of Psychology, Beijing Forestry University, Beijing, China.
National Computer System Engineering Research Institute of China, Beijing, China.
Front Public Health. 2023 Jan 16;10:1061590. doi: 10.3389/fpubh.2022.1061590. eCollection 2022.
The highly public nature of cybersuicide contradicts long-held beliefs of offline suicide, which may cause differences in the way people perceive and respond to both of them. However, knowledge of whether and how suicide literacy differs between cybersuicide and offline suicide is limited.
By analyzing social media data, this paper focused on livestreamed suicide and aimed to compare suicide literacy between cybersuicide and offline suicide on three aspects, including false knowledge structure, extent of association with stigma, and linguistic expression pattern. 7,236 Sina Weibo posts with relevant keywords were downloaded and analyzed. First, a content analysis was performed by human coders to determine whether each post reflected suicide-related false knowledge and stigma. Second, a text analysis was conducted using the Simplified Chinese version of LIWC software to automatically extract psycholinguistic features from each post. Third, based on selected features, classification models were developed using machine learning techniques to differentiate false knowledge of cybersuicide from that of offline suicide.
Results showed that, first, cybersuicide-related posts generally reflected more false knowledge than offline suicide-related posts ( 255.13, < 0.001). Significant differences were also observed in seven false knowledge types. Second, among posts reflecting false knowledge, cybersuicide-related posts generally carried more stigma than offline suicide-related posts ( = 116.77, < 0.001). Significant differences were also observed in three false knowledge types. Third, among established classification models, the highest F1 value reached 0.70.
The findings provide evidence of differences in suicide literacy between cybersuicide and offline suicide, and indicate the need for public awareness campaigns that specifically target cybersuicide.
网络自杀的高度公开性与长期以来的线下自杀观念相悖，这可能导致人们对两者的看法和反应存在差异。然而，关于网络自杀和线下自杀的自杀知识是否存在差异以及差异程度如何，目前的了解还很有限。
本研究通过分析社交媒体数据，重点关注直播自杀事件，旨在从错误知识结构、与污名关联程度以及语言表达模式三个方面比较网络自杀和线下自杀的自杀知识。共下载并分析了 7236 条含有相关关键词的新浪微博帖。首先，通过人工编码进行内容分析，以确定每个帖子是否反映了与自杀相关的错误知识和污名。其次，使用简化中文版本的 LIWC 软件进行文本分析，自动提取每个帖子的心理语言特征。最后，基于选定的特征，使用机器学习技术开发分类模型，以区分网络自杀和线下自杀的错误知识。
结果表明，首先，与网络自杀相关的帖子普遍反映出比线下自杀相关帖子更多的错误知识（ 255.13， < 0.001）。在七种错误知识类型中也观察到了显著差异。其次，在反映错误知识的帖子中，与网络自杀相关的帖子普遍比线下自杀相关的帖子带有更多的污名（ = 116.77， < 0.001）。在三种错误知识类型中也观察到了显著差异。第三，在建立的分类模型中，最高的 F1 值达到 0.70。
研究结果为网络自杀和线下自杀的自杀知识差异提供了证据，并表明需要开展针对网络自杀的公众意识宣传活动。
