Social Media Study of Public Opinions on Potential COVID-19 Vaccines: Informing Dissent, Disparities, and Dissemination

Abstract

AbstractThe current development of vaccines for SARS-CoV-2 is unprecedented. Little is known, however, about the nuanced public opinions on the coming vaccines. We adopt a human-guided machine learning framework (using more than 40,000 rigorously selected tweets from more than 20,000 distinct Twitter users) to capture public opinions on the potential vaccines for SARS-CoV-2, classifying them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine. We aggregate opinions at the state and country levels, and find that the major changes in the percentages of different opinion groups roughly correspond to the major pandemic-related events. Interestingly, the percentage of the pro-vaccine group is lower in the Southeast part of the United States. Using multinomial logistic regression, we compare demographics, social capital, income, religious status, political affiliations, geo-locations, sentiment of personal pandemic experience and non-pandemic experience, and county-level pandemic severity perception of these three groups to investigate the scope and causes of public opinions on vaccines. We find that socioeconomically disadvantaged groups are more likely to hold polarized opinions on potential COVID-19 vaccines. The anti-vaccine opinion is the strongest among the people who have the worst personal pandemic experience. Next, by conducting counterfactual analyses, we find that the U.S. public is most concerned about the safety, effectiveness, and political issues regarding potential vaccines for COVID-19, and improving personal pandemic experience increases the vaccine acceptance level. We believe this is the first large-scale social media-based study to analyze public opinions on potential COVID-19 vaccines that can inform more effective vaccine distribution policies and strategies.