在社交媒体上建立一个分析框架,针对与烟草有关的错误信息:利用生成性人工智能协助进行探索性分析
medRxiv : the preprint server for health sciences
|June 6, 2025
概括
社交媒体上的烟草错误信息不成比例地伤害弱势群体. 这项研究制定了一个框架,以识别和分类有关烟草和电子烟政策的虚假陈述,帮助公共卫生干预.
科学领域:
- 公共卫生 公共卫生
- 社交媒体分析 社交媒体分析
- 错误信息研究 错误信息研究
背景情况:
- 对烟草的错误信息对公共卫生产生了重大影响,特别是在受教育程度较低的人群中.
- 弱势群体经历了与烟草有关的不成比例的发病率和死亡率.
- 社交媒体平台是传播健康错误信息的关键舞台.
研究的目的:
- 分析Twitter (X) 上与烟草有关的错误信息的特征.
- 制定一个框架来识别,分类和验证烟草错误信息.
- 告知干预措施,保护弱势群体免受健康错误信息的影响.
主要方法:
- 分析了340万条与烟草/尼古丁相关的推文,提炼到842,754条独特的帖子.
- 利用隐性迪里克莱特分配 (LDA) 主题建模来识别关键主题.
- 对抽样推文进行了定性和人工智能辅助的分析,以分类错误信息.
主要成果:
- 根据内容 (安全,政策),虚假类型 (捏造,扭曲) 和来源 (个人,团体) 分类错误信息.
- 在Twitter (X) 上发现了与政策相关的烟草错误信息的高流行率.
- 观察到支持vaping内容的扩大,误解科学发现和政策.
结论:
- 开发了一个全面的框架来分析社交媒体烟草错误信息.
- 该框架强调了烟草错误信息中的政策辩论和阴谋叙述.
- 这种框架可以改善机器学习的干预和数据注释.
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