CoViNAR:用于流行病严重程度预测和分析的背景感知社交媒体数据集
Soofi Shafiya1, Mudasir Ahmad Wani2, Suraiya Jabin1
1Department of Computer Science, Faculty of Sciences, Jamia Millia Islamia, New Delhi, India.
Frontiers in artificial intelligence
|September 5, 2025
概括
这项研究使用社交媒体数据实时跟踪COVID-19资源需求,改善流行病准备和资源分配. 调查结果显示,社交媒体与社交媒体之间存在很强的关联.
科学领域:
- 公共卫生
- 计算社会科学
- 数据科学
背景情况:
- COVID-19 疫情凸显了全球卫生资源管理和需求预测方面的缺陷.
- 实时数据分析对于有效应对流行病至关重要.
研究的目的:
- 通过分析社交媒体以实时发现卫生资源需求, 提高疫情防控能力.
- 开发一种检测和监测卫生危机期间资源短缺和可用性的方法.
主要方法:
- 使用SnScrape收集了超过2750万条与COVID-19相关的推文.
- 使用BERTopic对14000条注释的推文创建了CoViNAR数据集,
- 经过训练和评估的机器学习分类器与DistilBERT嵌入式用于推特分类.
主要成果:
- 最好的分类器实现了超过96%的准确性,精度,回忆和F1分数.
- 时间分析显示",需要/可用性"的推文与美国,英国和印度的COVID-19病例激增之间存在很强的相关性.
- 展示了社交媒体分析对实时资源监控的有效性.
结论:
- 社交媒体分析为积极的公共卫生监测提供了可行的工具.
- 这种方法可以改善疫情期间的资源分配和早期危机干预.
- 这种方法可以加强全球卫生管理系统,以应对未来的卫生紧急情况.
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