在Omicron变种期间对COVID-19症状的Twitter数据的监控:情绪分析
Kaiyue Zhang1,2, Zhaojin Guo3, Yujie Ai1,3,4,5
1School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China (Hong Kong).
JMIR formative research
|September 23, 2025
概括
像Twitter这样的社交媒体可以跟踪COVID-19的症状,并预测Omicron亚型的出现. 关于发烧,头痛和喉疼痛的推文中的负面情绪与病毒演变相关,有助于公共卫生反应.
科学领域:
- 公共卫生监督 公共卫生监督
- 计算流行病学计算流行病学
- 社交媒体分析
背景情况:
- 由于COVID-19,医疗当局需要快速获取信息.
- 对于COVID-19的传统数据收集是资源密集的.
- 像Twitter这样的社交媒体平台为监控感染提供了一个新的数据源.
研究的目的:
- 在Twitter上分析COVID-19症状趋势.
- 与传统监测数据和病毒演变相关联推特模式.
- 评估负面情绪对Omicron亚变体出现的预测能力.
主要方法:
- 提取了2022年关于COVID-19症状的Twitter数据.
- 使用微调的RoBERTa模型进行99.7%准确的情绪分析.
- 应用结点回归来分析负症状推文中的趋势,并与Omicron亚变体相关联.
主要成果:
- 发烧,喉疼痛和头痛是主要的主题.
- 负发烧推文在BA.2和BA.5主导期间达到顶峰,显示对亚变种演变的敏感性.
- 关于发烧,头痛和喉疼痛的负面情绪是最普遍的.
结论:
- 推特趋势为COVID-19感染和症状提供了有价值的实时分析.
- 推特中的负面情绪可以预测新的Omicron亚变体的出现.
- 社交媒体监控有助于及时进行公共卫生干预.
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