从Twitter数据中提取与疫苗接种相关的因素,并将其映射到行为模型中
Md Rafiul Biswas1, Zubair Shah1
1Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
Human vaccines & immunotherapeutics
|November 28, 2023
概括
推特数据揭示了公众对疫苗的态度,将其映射到5C模型和健康信念模型 (HBM). 对疫苗安全性的信任增加了接受度,而错误信息则加剧了犹,使得有针对性的公共卫生干预措施成为可能.
科学领域:
- 公共卫生 公共卫生
- 社交媒体分析 社交媒体分析
- 行为科学 行为科学
背景情况:
- 像Twitter这样的社交媒体平台为公众对疫苗的看法提供了有价值的见解.
- 已建立的行为模型,包括5C心理先例模型和健康信念模型 (HBM),为理解与疫苗有关的行为转变提供了框架.
研究的目的:
- 调查推特数据是否能让我们了解阿拉伯人口对疫苗的公众态度.
- 确定这些见解是否可以映射到5C模型和HBM.
主要方法:
- 使用阿拉伯语的疫苗相关关键词进行了为期两年的推特数据搜索 (2020年1月至2022年1月).
- 应用BERTopic建模来提取主题,然后将主题手动映射到5C模型和HBM的因子上.
- 分析包括1,068,466个唯一用户和3,368,258条推文,以及性别和地区 (MENA) 的人口统计数据.
主要成果:
- BERTopic的建模确定了25个主题,并成功地将其映射到5C模型和HBM的15个因素中.
- 推特用户在信任疫苗的安全性和有效性时,显示了疫苗接受度的增加.
- 疫苗犹与阴谋论和错误信息有着显著的联系.
结论:
- 推特数据是理解和预测阿拉伯人口疫苗相关行为变化的丰富多样的来源.
- 将主题映射到5C和HBM等行为模型中,证明了社交媒体数据对公共卫生的有用性.
- 与传统方法相比,这种方法可以为疫苗接种计划提供更及时,更有效的干预措施.
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