Liu Yang, Zhao Chenxu, Zhang Chengzhi
School of Information Management, Wuhan University, Wuhan, China.
School of Computer Science, Wuhan University, Wuhan, China.
Digit Health. 2024 Sep 25;10:20552076241272712. doi: 10.1177/20552076241272712. eCollection 2024 Jan-Dec.
This paper aims to understand vaccine hesitancy in the post-epidemic era by analyzing texts related to vaccine reviews and public attitudes toward three prominent vaccine brands: Sinovac, AstraZeneca, and Pfizer, and exploring the relationship of vaccine hesitancy with the prevalence of epidemics in different regions.
We collected 165629 Twitter user comments associated with the vaccine brands. The comments were labeled based on willingness and attitude toward vaccination. We utilize a causality deep learning model, the Bert multi-channel convolutional neural network (BertMCNN), to predict users' willingness and attitude mutually.
When applied to the provided dataset, the proposed BertMCNN model demonstrated superior performance to traditional machine learning algorithms and other deep learning models. It is worth noting that after March 2022, the public was more hesitant about the Sinovac vaccines.
This study reveals a connection between vaccine hesitancy and the prevalence of the epidemic in different regions. The analytical results obtained from this method can assist governmental health departments in making informed decisions regarding vaccination strategies.
本文旨在通过分析与疫苗评价以及公众对三种知名疫苗品牌（科兴、阿斯利康和辉瑞）的态度相关的文本，了解后疫情时代的疫苗犹豫情况，并探讨疫苗犹豫与不同地区疫情流行程度之间的关系。
我们收集了165629条与疫苗品牌相关的推特用户评论。这些评论根据对疫苗接种的意愿和态度进行了标注。我们利用一种因果深度学习模型，即伯特多通道卷积神经网络（BertMCNN），来相互预测用户的意愿和态度。
当应用于所提供的数据集时，所提出的BertMCNN模型表现出优于传统机器学习算法和其他深度学习模型的性能。值得注意的是，2022年3月之后，公众对科兴疫苗更为犹豫。
本研究揭示了疫苗犹豫与不同地区疫情流行程度之间的联系。通过这种方法获得的分析结果可以帮助政府卫生部门在制定疫苗接种策略时做出明智的决策。
