Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides.

Mrinal Kumar, Mark Dredze, Glen Coppersmith, Munmun De Choudhury
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引用次数: 136

Abstract

The Werther effect describes the increased rate of completed or attempted suicides following the depiction of an individual's suicide in the media, typically a celebrity. We present findings on the prevalence of this effect in an online platform: r/SuicideWatch on Reddit. We examine both the posting activity and post content after the death of ten high-profile suicides. Posting activity increases following reports of celebrity suicides, and post content exhibits considerable changes that indicate increased suicidal ideation. Specifically, we observe that post-celebrity suicide content is more likely to be inward focused, manifest decreased social concerns, and laden with greater anxiety, anger, and negative emotion. Topic model analysis further reveals content in this period to switch to a more derogatory tone that bears evidence of self-harm and suicidal tendencies. We discuss the implications of our findings in enabling better community support to psychologically vulnerable populations, and the potential of building suicide prevention interventions following high-profile suicides.

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发现名人自杀后社交媒体上自杀内容的变化。
维特效应描述的是媒体(通常是名人)对个人自杀的描述后,完成或企图自杀的比率上升。我们在Reddit上的一个在线平台r/SuicideWatch上展示了这一现象的普遍性。我们研究了10个备受瞩目的自杀者死亡后的发帖活动和发帖内容。在名人自杀的报道之后,帖子活动增加,帖子内容显示出相当大的变化,表明自杀意念的增加。具体来说,我们观察到名人后的自杀内容更有可能是内向的,表现出较少的社会关注,并且充满了更多的焦虑、愤怒和负面情绪。话题模型分析进一步揭示了这一时期的内容转向更加贬损的语气,带有自残和自杀倾向的证据。我们讨论了我们的研究结果在为心理脆弱人群提供更好的社区支持方面的意义,以及在高调自杀事件发生后建立自杀预防干预措施的潜力。
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HT '22: 33rd ACM Conference on Hypertext and Social Media, Barcelona, Spain, 28 June 2022- 1 July 2022 HT '21: 32nd ACM Conference on Hypertext and Social Media, Virtual Event, Ireland, 30 August 2021 - 2 September 2021 HT '20: 31st ACM Conference on Hypertext and Social Media, Virtual Event, USA, July 13-15, 2020 Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides. QualityRank: assessing quality of wikipedia articles by mutually evaluating editors and texts
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