  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.
