在不同地区对COVID-19疫苗态度的概括因素:总结生成和主题建模方法
Yang Liu1, Jiale Shi2, Chenxu Zhao2
1School of Information Management, Wuhan University, Wuhan, China.
Digital health
|July 24, 2023
概括
这项研究使用了主题建模和摘要生成,以找到影响不同地区对疫苗态度的关键因素. 这些与疫苗,卫生系统和个体属性相关的因素在全球范围内是一致的.
科学领域:
- 计算社会科学 计算社会科学
- 公共卫生信息学 公共卫生信息学
- 自然语言处理自然语言处理.
背景情况:
- 了解公众对疫苗的态度对于有效的公共卫生战略至关重要.
- 社交媒体平台产生了大量数据,反映了公众对疫苗的看法.
- 识别疫苗犹和接受的关键驱动因素对于有针对性的干预措施至关重要.
研究的目的:
- 通过使用社交媒体数据,在不同地区识别和概括影响疫苗态度的因素.
- 为了比较BERTopic集群和对比学习 (CL) 的有效性,分析与疫苗有关的讨论.
- 为决策者和医疗机构提供有关疫苗接种问题的可操作见解.
主要方法:
- 从2020年12月到2021年12月收集了5562条关于Sinovac,AstraZeneca和Pfizer疫苗的推文.
- 雇佣BERTopic集群用于推特分类和对比学习 (CL) 用于主题总结.
- 将确定的主题概括为三个核心因素:疫苗特异性,与卫生系统相关的和个人的社会属性.
主要成果:
- 与隐藏的迪里克莱特分配 (LDA) 相比,BERTopic集群显示出更高的性能.
- 对比学习增强了主题建模,特别是在中心集群中.
- 确定了影响疫苗态度的三个主要因素,在不同地理区域一致.
结论:
- 深度学习方法有效地识别区域疫苗态度决定因素.
- 这些发现支持制定有针对性的公共卫生活动,以解决疫苗接种问题.
- 鉴定出来的因素可以为改善全球疫苗接种和接种的战略提供信息.
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