| Summary: | Topic modeling, which uses machine learning algorithms to identify the emergence of topics, can help public health professionals monitor online public responses during health crises. This study used Latent Dirichlet Allocation algorithm to model the topics in Twitter messages (or “tweets”) from the US during the COVID-19 pandemic from March 20th to August 9th, 2020. Topic sizes and sentiment were calculated as the pandemic evolved, for major topics about vaccination and mask-wearing as a nonpharmaceutical intervention measure. Despite the pandemic, positive sentiments were found among most topics. While users were found to react more often to positive sentiment about mask-wearing, negative content on vaccination was found more popular. Noticeable trends in topic sizes and sentiment were observed for various topics, which correlated in time with some key pandemic events and policy changes, implying their impacts on social media responses. By analyzing such trends and impacts, this research offers insights on health campaign message design and how to outreach the general public most effectively.
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