Social media study of public opinions on potential COVID-19 vaccines: informing dissent, disparities, and dissemination

Lyu, Hanjia; Wang, Junda; Wu, Wei; Duong, Viet; Zhang, Xiyang; Dye, Timothy D.; Luo, Jiebo

Background: The current development of vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unprecedented. Little is known, however, about the nuanced public opinions on the vaccines on social media. Methods: We adopt a human-guided machine learning framework using more than six million tweets from almost two million unique Twitter users to capture public opinions on the vaccines for SARS-CoV-2, classifying them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine. After feature inference and opinion mining, 10,945 unique Twitter users are included in the study population. Multinomial logistic regression and counterfactual analysis are conducted. Results: Socioeconomically disadvantaged groups are more likely to hold polarized opinions on coronavirus disease 2019 (COVID-19) vaccines either pro-vaccine ([Formula: see text]) or anti-vaccine ([Formula: see text]). People who have the worst personal pandemic experience are more likely to hold the anti-vaccine opinion ([Formula: see text]). The U.S. public is most concerned about the safety, effectiveness, and political issues regarding vaccines for COVID-19, and improving personal pandemic experience increases the vaccine acceptance level. Conclusion: Opinion on COVID-19 vaccine uptake varies across people of different characteristics.