在线仇恨言论,错误信息和一般心理健康的拓数据映射:基于语言模型的大型研究
Andrew William Alexander1,2, Hongbin Wang1
1College of Medicine, Texas A&M University, Houston, Texas, United States of America.
PLOS digital health
|July 29, 2025
概括
这项研究通过机器学习探索了在线仇恨言论,错误信息和心理健康之间的联系. 分析Reddit的帖子揭示了有害的在线内容和心理健康之间的潜在联系.
科学领域:
- 计算社会科学 计算社会科学
- 心理学 心理学 心理学
- 数据科学数据科学数据科学
背景情况:
- 社交媒体在传播仇恨言论和错误信息方面的作用越来越令人担忧.
- 现有的研究将在线有害内容与人格特征联系起来,但不是整体心理健康.
- 分析大规模的社交媒体数据以寻找隐藏的模式需要先进的分析工具.
研究的目的:
- 调查在线仇恨言论/错误信息与心理健康之间的关系.
- 利用机器学习和大型语言模型来分析社交媒体数据.
- 揭示在线内容模式和心理健康指标之间的隐藏联系.
主要方法:
- 收集了成千上万的Reddit帖子从选定的社区.
- 使用OpenAI的GPT3来生成代表后语义的嵌入式.
- 应用机器学习分类和拓数据分析 (TDA) 到嵌入式.
主要成果:
- 识别了仇恨言论/错误信息模式和社区特定言论之间的潜在相似之处.
- 通过TDA生成一个视觉地图,通过TDA连接在线仇恨言论,错误信息,精神疾病和心理健康.
- 展示了使用先进人工智能来分析复杂的社交媒体数据的可行性.
结论:
- 机器学习和LLM可以分析庞大的社交媒体数据,揭示复杂的关系.
- 这项研究提供了一种新的方法来可视化在线行为和心理健康之间的联系.
- 进一步的研究可以探索这些联系,以告知针对在线伤害和心理健康问题的干预措施.
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