自杀话语的隐藏深处:网络分析和自然语言处理揭露了未经审查的表达方式
Damien Lekkas1,2, Nicholas C Jacobson1,2,3,4
1Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
Digital health
|November 6, 2023
概括
分析自杀论坛上的在线讨论显示,高风险用户 (HRU) 表现出更积极的情绪和乐观情绪. 这项研究提供了对不受审查的自杀性言论及其对预防自杀的影响的见解.
科学领域:
- 计算语言学计算语言学
- 社会科学 社会科学 社会科学
- 心理学 心理学 心理学
背景情况:
- 自杀思想和行为 (STB) 被污名化,导致数字平台上的审查.
- 在线数据为研究STB提供了机会,但审查限制了主流社区的研究.
- 在审查减轻的环境中分析内容可以揭示STB表达的新趋势.
研究的目的:
- 用自然语言处理 (NLP) 和网络分析来描述无审查的自杀性言论.
- 在网上自杀论坛中,在高风险用户 (HRU) 中识别独特的语言模式和主题.
- 探索在线亚文化如何表达STB,以告知检测和预防策略.
主要方法:
- 在一个支持选择自杀的论坛上,模拟了192名用户 (48名HRU) 的书面内容.
- 采用了基于词典的主题建模 (EMPATH) 和探索性网络分析.
- 在37,136个论坛帖子中分析了超过200万个代币.
主要成果:
- 人力资源管理人员显示,积极情绪和乐观情绪的频率更高.
- 观察到强调寻找方法和分享行为.
- 识别了与孤独和逆境相关的新术语和语义.
结论:
- 在线STB亚文化的NLP和网络分析揭示了与自杀学相关的趋势.
- 这些发现可能会提高自杀结果的检测,干预和预防.
- 进一步研究不受审查的在线话语是有必要的.
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