"This study employed three machine learning algorithms, Naïve Bayes, SVM, and a Balanced Random Forest to build a sentiment model that can detect Muslim sentiment about Muslim clerics’ anti-misinformation campaign on YouTube. Overall, 9701 comments were collected. An LDA-based topic model was also employed to understand the most expressed topics in the YouTube comments. Results: The confusion matrix and accuracy score assessment revealed that the balanced random forest-based model demonstrated the best performance. Overall, the sentiment analysis discovered that 74 percent of the comments were negative, and 26 percent were positive. An LDA-based topic model also revealed the eight most discussed topics associated with ten keywords in those YouTube comments. Practical implications: The sentiment and topic model from this study will particularly help public health professionals and researchers to better understand the nature of vaccine misinformation and hesitancy in the Muslim communities." (Abstract)