Understanding the COVID-19 vaccine hesitancy, such as who and why, is very
crucial since a large-scale vaccine adoption remains as one of the most
efficient methods of controlling the pandemic. Such an understanding also
provides insights into designing successful vaccination campaigns for future
pandemics. Unfortunately, there are many factors involving in deciding whether
to take the vaccine, especially from the cultural point of view. To obtain
these goals, we design a novel culture-aware machine learning (ML) model, based
on our new data collection, for predicting vaccination willingness. We further
analyze the most important features which contribute to the ML model's
predictions using advanced AI explainers such as the Probabilistic Graphical
Model (PGM) and Shapley Additive Explanations (SHAP). These analyses reveal the
key factors that most likely impact the vaccine adoption decisions. Our
findings show that Hispanic and African American are most likely impacted by
cultural characteristics such as religions and ethnic affiliation, whereas the
vaccine trust and approval influence the Asian communities the most. Our
results also show that cultural characteristics, rumors, and political
affiliation are associated with increased vaccine rejection.

本研究利用文化感知的机器学习模型，基于新数据收集，预测 COVID-19 疫苗接种意愿，并使用 PGM 和 SHAP 等高级 AI 解释器分析最重要的特征，揭示影响疫苗接种决策的关键因素，发现文化特征、谣言和政治归属与疫苗拒绝有关。