# COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Dataset of Anti-vaccine Content, Vaccine Misinformation and Conspiracies

arXiv: https://arxiv.org/abs/2105.05134 · HTML: https://mlsanity.com/paper/2105.05134/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2021.

## Built on by (most cited first, 17 of 17)

- [A Holistic Framework for Analyzing the COVID-19 Vaccine Debate](https://mlsanity.com/paper/2205.01817/) (2022)
- [VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter](https://mlsanity.com/paper/2301.06660/) (2023)
- [Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks](https://mlsanity.com/paper/2207.08349/) (2022)
- [Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision](https://mlsanity.com/paper/2210.10031/) (2022)
- [Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications](https://mlsanity.com/paper/2206.14358/) (2022)
- [COVID-19 Vaccine Misinformation Campaigns and Social Media Narratives](https://mlsanity.com/paper/2106.08423/) (2021)
- [CoVaxNet: An Online-Offline Data Repository for COVID-19 Vaccine Hesitancy Research](https://mlsanity.com/paper/2207.01505/) (2022)
- [Accurate Measures of Vaccination and Concerns of Vaccine Holdouts from Web Search Logs](https://mlsanity.com/paper/2306.07457/) (2023)
- [Detecting Anti-Vaccine Users on Twitter](https://mlsanity.com/paper/2110.11333/) (2021)
- [COVID-19 Vaccine Misinformation in Middle Income Countries](https://mlsanity.com/paper/2311.18195/) (2023)
- [Classifying COVID-19 vaccine narratives](https://mlsanity.com/paper/2207.08522/) (2022)
- [Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination](https://mlsanity.com/paper/2407.03190/) (2024)
- [Vaccine skepticism detection by network embedding](https://mlsanity.com/paper/2110.13619/) (2021)
- [Demystifying the COVID-19 vaccine discourse on Twitter](https://mlsanity.com/paper/2208.13523/) (2022)
- [Suffering from Vaccines or from Government? : Partisan Bias in COVID-19 Vaccine Adverse Events Coverage](https://mlsanity.com/paper/2211.10707/) (2022)
- [COVID-19 Twitter Sentiment Classification Using Hybrid Deep Learning Model Based on Grid Search Methodology](https://mlsanity.com/paper/2406.10266/) (2024)
- [DISHONEST: Dissecting misInformation Spread using Homogeneous sOcial NEtworks and Semantic Topic classification](https://mlsanity.com/paper/2412.09578/) (2024)

## Abstract (arXiv)

False claims about COVID-19 vaccines can undermine public trust in ongoing vaccination campaigns, thus posing a threat to global public health. Misinformation originating from various sources has been spreading online since the beginning of the COVID-19 pandemic. In this paper, we present a dataset of Twitter posts that exhibit a strong anti-vaccine stance. The dataset consists of two parts: a) a streaming keyword-centered data collection with more than 1.8 million tweets, and b) a historical account-level collection with more than 135 million tweets. The former leverages the Twitter streaming API to follow a set of specific vaccine-related keywords starting from mid-October 2020. The latter consists of all historical tweets of 70K accounts that were engaged in the active spreading of anti-vaccine narratives. We present descriptive analyses showing the volume of activity over time, geographical distributions, topics, news sources, and inferred account political leaning. This dataset can be used in studying anti-vaccine misinformation on social media and enable a better understanding of vaccine hesitancy. In compliance with Twitter's Terms of Service, our anonymized dataset is publicly available at: https://github.com/gmuric/avax-tweets-dataset