在COVID-19大流行中描述公众情绪和药物相互作用使用社交媒体:自然语言处理和网络分析
Wanxin Li1, Yining Hua2,3, Peilin Zhou4
1School of Public Health, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Journal of medical Internet research
|March 7, 2025
概括
这项研究开发了一个自然语言处理 (NLP) 管道来分析社交媒体关于COVID-19药物的讨论. 该NLP管道确定了公众情绪和潜在的药物相互作用,为公共卫生监测提供了洞察力.
科学领域:
- 计算语言学计算语言学
- 公共卫生信息学 公共卫生信息学
- 社交媒体分析.
背景情况:
- 关于COVID-19药物的传统研究面临着诸如报告偏差和长时间收集数据等局限性.
- 社交媒体提供实时与毒品相关的数据,这对于了解大流行对毒品使用的影响和监测错误信息至关重要.
研究的目的:
- 开发一种自然语言处理 (NLP) 管道,用于分析关于COVID-19相关药物的社交媒体话语.
- 利用实时社交媒体数据,及时获得公共卫生洞察力和错误信息监测.
主要方法:
- 一个使用预训练语言模型的NLP技术的管道被构建.
- 关键模块包括命名实体识别,情绪分析,话题建模和药物网络分析,用于药物不良反应 (ADR) 和药物相互作用 (DDI).
- 该管道分析了2020年2月至2022年4月的COVID-19药物相关推文.
主要成果:
- 分析了超过210万条相关推文,其中艾弗麦丁,化,雷梅西维尔,和维生素D是最常被讨论的药物.
- 公众的看法受到认可和指令的影响,而不是经验证据;重新设计的药物比批准的药物引起了更多的关注.
- 分析确定了关键讨论主题和潜在的ADR/DDI模式,这对于公共卫生监测至关重要.
结论:
- 一个NLP管道为大规模的公共卫生监测提供了一个强大的方法.
- 该框架为有关药物安全性和相互作用的流行病学研究提供了有价值的补充数据.
- 这项研究为未来基于社交媒体的公共卫生分析奠定了基础.
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