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

arXiv: https://arxiv.org/abs/2012.02165 · HTML: https://mlsanity.com/paper/2012.02165/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [From Selection to Generation: A Survey of LLM-based Active Learning](https://mlsanity.com/paper/2502.11767/) (2025)
- [Can LLMs Simulate Social Media Engagement? A Study on Action-Guided Response Generation](https://mlsanity.com/paper/2502.12073/) (2025)
- [Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media](https://mlsanity.com/paper/2403.04009/) (2024)
- [CoVaxNet: An Online-Offline Data Repository for COVID-19 Vaccine Hesitancy Research](https://mlsanity.com/paper/2207.01505/) (2022)
- [Computational Assessment of Hyperpartisanship in News Titles](https://mlsanity.com/paper/2301.06270/) (2023)
- [From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors](https://mlsanity.com/paper/2103.10901/) (2021)
- [A Large-Scale Analysis of Persian Tweets Regarding Covid-19 Vaccination](https://mlsanity.com/paper/2302.04511/) (2023)
- [Human Behavior in the Time of COVID-19: Learning from Big Data](https://mlsanity.com/paper/2303.13452/) (2023)
- [Beyond Sentiment: Leveraging Topic Metrics for Political Stance Classification](https://mlsanity.com/paper/2310.15429/) (2023)

## Abstract (arXiv)

The current development of vaccines for SARS-CoV-2 is unprecedented. Little is known, however, about the nuanced public opinions on the vaccines on social media. 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 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. People who have the worst personal pandemic experience are more likely to hold the anti-vaccine opinion. 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.