通过自然语言处理分析使用社交媒体数据对联邦电子烟法规的公众反应:主题建模研究研究
Shuo-Yu Lin1, Sahithi Kiran Tulabandu2, J Randy Koch3
1Department of Health Administration and Policy, George Mason University, Fairfax, VA, United States.
Journal of medical Internet research
|October 1, 2024
概括
围绕FDA的公众讨论
科学领域:
- 公共卫生 公共卫生
- 社交媒体分析 社交媒体分析
- 监管政策 监管政策
背景情况:
- 电子烟的使用在美国是一个重大的公共卫生问题.
- 美国食品和药物管理局于2022年6月23日发布了Juul产品的销售拒绝令 (MDO).
- 一个联邦上诉法院暂时停止了禁令,允许Juul产品继续在市场上销售.
研究的目的:
- 分析关于FDA禁止Juul的社交媒体上的公众情绪.
- 了解社交媒体上公共卫生政策讨论的生命周期.
- 检查禁令前和后的公众对"七月"的看法.
主要方法:
- 利用自然语言处理 (NLP),包括BERTopic建模和情绪分析.
- 从2022年6月到10月收集和分析了6023条推文和22288条回复/转发推文.
- 进行了时间趋势分析和转发的内容分析,以评估公共话语.
主要成果:
- 关于FDA的Juul禁令的Twitter关注持续不到一周.
- 在宣布之后,包括健康问题在内的讨论迅速下降.
- 公众对禁令的反应主要是负面的 (42%),中立情绪为51%.
结论:
- 新闻周期和公众对FDA的Juul禁令的参与显示了短暂的生命周期.
- 很大一部分公众对禁令表达了负面情绪.
- 政策制定者应该考虑持续的沟通策略和社交媒体参与,以保持公共卫生政策对话.
关键词:
推特分析 推特分析数据挖掘是数据挖掘的一个方法.电子烟法规 电子烟法规营销拒绝命令是指拒绝订单.自然语言处理自然语言处理.与公共卫生相关的政策情绪分析是一种情绪分析.社交媒体分析分析主题建模主题建模电子烟 (vaping) 是一种电子烟.更多相关视频
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