关于COVID-19疫苗的深度学习分析在6个高收入国家在Twitter上表达的犹和信心:长度观察研究
Xinyu Zhou1,2,3, Suhang Song4, Ying Zhang1,2
1School of Public Health, Fudan University, Shanghai, China.
Journal of medical Internet research
|November 6, 2023
概括
在Twitter上监控COVID-19疫苗犹,发现随着时间的推移,接受度下降,不信任度上升. 深度学习模型有效地跟踪了这些趋势,为公共卫生政策提供了洞察力.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 社交媒体分析
背景情况:
- 持续监测COVID-19疫苗犹对于制定有效的公共卫生政策至关重要.
- 了解接种疫苗的全国和地方趋势至关重要.
研究的目的:
- 为了追踪COVID-19疫苗犹和信心的时间和空间分布.
- 为了分析整个大流行期间在主要的英语国家/地区在Twitter上表达的情绪.
主要方法:
- 从2020年1月到2022年6月收集了超过520万条关于COVID-19疫苗接种的英语tweet.
- 利用基于变压器的深度学习模型来分类疫苗接受/拒绝和感知疫苗安全性.
- 分析了与美国疫苗犹相关的社会人口统计因素.
主要成果:
- 所有6个国家在疫苗犹和信心方面都显示出类似的趋势.
- 接受接种疫苗的意图显著下降,而对疫苗不安全的信念增加.
- 犹程度因国家,制造商和地区而异,美国的政治归属和信心与较低的犹程度有关.
结论:
- 随着大流行病的发展,COVID-19疫苗的犹和信心也随之发展.
- 社交媒体话语和深度学习为监测疫苗犹提供了一种经济高效的方法.
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