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

Abstract

\n \n 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.\n \n 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.\n \n Results: Socioeconomically disadvantaged groups are more likely to hold polarized opinions on coronavirus disease 2019 (COVID-19) vaccines either pro-vaccine (\n \n B\n =\n 0.40\n ,\n S\n E\n =\n 0.08\n ,\n P\n <\n .\n 001\n ,\n O\n R\n =\n 1.49\n ;\n 95\n %\n C\n I\n =\n [\n 1.26\n ,\n 1.75\n ]\n \n ) or anti-vaccine (\n \n B\n =\n 0.52\n ,\n S\n E\n =\n 0.06\n ,\n P\n <\n .\n 001\n ,\n O\n R\n =\n 1.69\n ;\n 95\n %\n C\n I\n =\n [\n 1.49\n ,\n 1.91\n ]\n \n ). People who have the worst personal pandemic experience are more likely to hold the anti-vaccine opinion (\n \n B\n =\n −\n 0.18\n ,\n S\n E\n =\n 0.04\n ,\n P\n <\n .\n 001\n ,\n O\n R\n =\n 0.84\n ;\n 95\n %\n C\n I\n =\n [\n 0.77\n ,\n 0.90\n ]\n \n ). 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.\n \n Conclusion: Opinion on COVID-19 vaccine uptake varies across people of different characteristics.\n