名人因素:通过时间序列的贝叶斯模型探索影响者对COVID-19疫苗情绪的影响
Abhishek Shah1, Shweta Shah1, Bill Rand2
1Green Level High School.
Journal of the Southern Association for Information Systems
|January 19, 2026
概括
推动COVID-19疫苗接种的影响者增加了支持疫苗的推文. 然而,来自影响者的反疫苗信息对在线疫苗情绪产生了不同的影响. 这项研究分析了推特数据,以了解影响者的影响.
科学领域:
- 社会科学 社会科学 社会科学
- 公共卫生 公共卫生
- 计算社会科学 计算社会科学
背景情况:
- 在线社交网络促进了信息的快速传播,并影响了公众论.
- 在COVID-19大流行期间,影响者分享了有关疫苗的各种观点,影响了公众的看法.
- 了解影响者的影响对于公共卫生战略和打击疫苗犹至关重要.
研究的目的:
- 模拟11个主要影响者的声明对Twitter上对COVID-19疫苗的公众情绪的影响.
- 分析影响者的支持和反对接种疫苗的信息如何影响在线话语.
- 通过检查社交媒体影响者在疫苗信息传递中的作用,为公共卫生战略提供信息.
主要方法:
- 在大流行期间收集和分析了有关COVID-19疫苗的公开可用的推文.
- 通过使用VADER词典将情绪分数分配给推文.
- 采用线性,顺序和基于树的模型,构建贝叶斯结构时间序列模型用于反事实估计.
主要成果:
- 影响者分享支持疫苗接种的信息,在20天内,一般增加了支持疫苗的推文.
- 影响者的反疫苗信息有时会减少,有时会增加,20天的反疫苗推特.
- 该研究强调了影响者信息对疫苗情绪的复杂和多样化的影响.
结论:
- 影响者声明显著影响有关COVID-19疫苗的在线情绪.
- 公共卫生战略应该考虑社交媒体影响者对疫苗犹的微妙影响.
- 需要进一步的研究,才能充分理解在线疫苗话语和影响者影响的动态.
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