# Detecting Anti-Vaccine Users on Twitter

arXiv: https://arxiv.org/abs/2110.11333 · HTML: https://mlsanity.com/paper/2110.11333/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [A Simple Method for Commonsense Reasoning](https://mlsanity.com/paper/1806.02847/) (2018)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Moral Framing and Ideological Bias of News](https://mlsanity.com/paper/2009.12979/) (2020)
- [FrameAxis: Characterizing Microframe Bias and Intensity with Word Embedding](https://mlsanity.com/paper/2002.08608/) (2020)
- [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)
- [Better Word Embeddings by Disentangling Contextual n-Gram Information](https://mlsanity.com/paper/1904.05033/) (2019)
- [Polarization of the Vaccination Debate on Facebook](https://mlsanity.com/paper/1801.02903/) (2018)
- [Detecting Medical Misinformation on Social Media Using Multimodal Deep Learning](https://mlsanity.com/paper/2012.13968/) (2020)
- [Ratioing the President: An exploration of public engagement with Obama and Trump on Twitter](https://mlsanity.com/paper/2006.03526/) (2020)
- [Political Partisanship and Anti-Science Attitudes in Online Discussions about Covid-19](https://mlsanity.com/paper/2011.08498/) (2020)

## Built on by (most cited first, 2 of 2)

- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [The Anatomy of Conspirators: Unveiling Traits using a Comprehensive Twitter Dataset](https://mlsanity.com/paper/2308.15154/) (2023)

## Abstract (arXiv)

Vaccine hesitancy, which has recently been driven by online narratives, significantly degrades the efficacy of vaccination strategies, such as those for COVID-19. Despite broad agreement in the medical community about the safety and efficacy of available vaccines, a large number of social media users continue to be inundated with false information about vaccines and are indecisive or unwilling to be vaccinated. The goal of this study is to better understand anti-vaccine sentiment by developing a system capable of automatically identifying the users responsible for spreading anti-vaccine narratives. We introduce a publicly available Python package capable of analyzing Twitter profiles to assess how likely that profile is to share anti-vaccine sentiment in the future. The software package is built using text embedding methods, neural networks, and automated dataset generation and is trained on several million tweets. We find this model can accurately detect anti-vaccine users up to a year before they tweet anti-vaccine hashtags or keywords. We also show examples of how text analysis helps us understand anti-vaccine discussions by detecting moral and emotional differences between anti-vaccine spreaders on Twitter and regular users. Our results will help researchers and policy-makers understand how users become anti-vaccine and what they discuss on Twitter. Policy-makers can utilize this information for better targeted campaigns that debunk harmful anti-vaccination myths.