# COVID-19 Vaccine Misinformation Campaigns and Social Media Narratives

arXiv: https://arxiv.org/abs/2106.08423 · HTML: https://mlsanity.com/paper/2106.08423/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Uncovering Coordinated Networks on Social Media: Methods and Case Studies](https://mlsanity.com/paper/2001.05658/) (2020)
- [Identifying Coordinated Accounts on Social Media through Hidden Influence and Group Behaviours](https://mlsanity.com/paper/2008.11308/) (2020)
- [Detection of Novel Social Bots by Ensembles of Specialized Classifiers](https://mlsanity.com/paper/2006.06867/) (2020)
- [Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset](https://mlsanity.com/paper/2008.00791/) (2020)
- [Quantifying Controversy in Social Media](https://mlsanity.com/paper/1507.05224/) (2015)
- [COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Dataset of Anti-vaccine Content, Vaccine Misinformation and Conspiracies](https://mlsanity.com/paper/2105.05134/) (2021)
- [A first look at COVID-19 information and misinformation sharing on Twitter](https://mlsanity.com/paper/2003.13907/) (2020)
- [Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter](https://mlsanity.com/paper/2003.11906/) (2020)
- [Characterizing the 2016 Russian IRA Influence Campaign](https://mlsanity.com/paper/1812.01997/) (2018)
- [Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation](https://mlsanity.com/paper/2101.08419/) (2021)
- [Misinformation Warning Labels: Twitter's Soft Moderation Effects on COVID-19 Vaccine Belief Echoes](https://mlsanity.com/paper/2104.00779/) (2021)

## Built on by (most cited first, 7 of 7)

- [Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation](https://mlsanity.com/paper/2303.06433/) (2023)
- [Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media](https://mlsanity.com/paper/2210.07518/) (2022)
- [An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models](https://mlsanity.com/paper/2402.05359/) (2024)
- [Interpretable Detection of Out-of-Context Misinformation with Neural-Symbolic-Enhanced Large Multimodal Model](https://mlsanity.com/paper/2304.07633/) (2023)
- [Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media](https://mlsanity.com/paper/2403.04009/) (2024)
- [Classifying COVID-19 vaccine narratives](https://mlsanity.com/paper/2207.08522/) (2022)
- [Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework](https://mlsanity.com/paper/2305.00182/) (2023)

## Abstract (arXiv)

COVID-19 vaccine hesitancy has increased concerns about vaccine uptake required to overcome the pandemic and protect public health. A critical factor associated with anti-vaccine attitudes is the information shared on social media. In this work, we investigate misinformation communities and narratives that can contribute to COVID-19 vaccine hesitancy. During the pandemic, anti-science and political misinformation/conspiracies have been rampant on social media. Therefore, we investigate misinformation and conspiracy groups and their characteristic behaviours in Twitter data collected on COVID-19 vaccines. We identify if any suspicious coordinated efforts are present in promoting vaccine misinformation, and find two suspicious groups - one promoting a 'Great Reset' conspiracy which suggests that the pandemic is orchestrated by world leaders to take control of the economy, with vaccine related misinformation and strong anti-vaccine and anti-social messages such as no lock-downs; and another promoting the Bioweapon theory. Misinformation promoted is largely from the anti-vaccine and far-right communities in the 3-core of the retweet graph, with its tweets proportion of conspiracy and questionable sources to reliable sources being much higher. In comparison with the mainstream and health news, the right-leaning community is more influenced by the anti-vaccine and far-right communities, which is also reflected in the disparate vaccination rates in left and right U.S. states. The misinformation communities are also more vocal, either in vaccine or other discussions, relative to remaining communities, besides other behavioral differences.