UNASSIGNED: Complete written content of N = 192 users, including n = 48 identified as potential suicide completers/highest-risk users (HRUs), on the pro-choice suicide forum, Sanctioned Suicide, was modeled using a combination of lexicon-based topic modeling (EMPATH) and exploratory network analysis techniques to characterize and highlight prominent aspects of censorship-free suicidal discourse.
UNASSIGNED: Modeling of over 2 million tokens across 37,136 forum posts found higher frequency of positive emotion and optimism among HRUs, emphasis on methods seeking and sharing behaviors, prominence of previously undocumented jargon, and semantics related to loneliness and life adversity.
UNASSIGNED: This natural language processing (NLP)- and network-driven exposé of online STB subculture uncovered trends that deserve further attention within suicidology as they may be able to bolster detection, intervention, and prevention of suicidal outcomes and exposures.
■N=192个用户的完整书面内容,包括被确定为潜在自杀完成者/最高风险用户(HRU)的n=48,在支持选择自杀论坛上,制裁自杀,使用基于词典的主题建模(EMPATH)和探索性网络分析技术进行建模,以表征和突出无审查自杀话语的突出方面。
■在37,136个论坛帖子中对超过200万个令牌进行建模,发现HRU中积极情绪和乐观情绪的频率更高,强调寻求和分享行为的方法,突出以前没有记载的行话,以及与孤独和生活逆境有关的语义。
■这种自然语言处理(NLP)和网络驱动的在线STB亚文化暴露揭示了趋势,值得在自杀学中进一步关注,因为它们可能能够支持检测,干预,以及预防自杀结果和暴露。