# Automated clustering of COVID-19 anti-vaccine discourse on Twitter

arXiv: https://arxiv.org/abs/2203.01549 · HTML: https://mlsanity.com/paper/2203.01549/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Uncovering Coordinated Networks on Social Media: Methods and Case Studies](https://mlsanity.com/paper/2001.05658/) (2020)
- [Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms](https://mlsanity.com/paper/2007.07996/) (2020)
- [Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ](https://mlsanity.com/paper/1703.00565/) (2017)
- [Studying Moral-based Differences in the Framing of Political Tweets](https://mlsanity.com/paper/2103.11853/) (2021)
- [Attention Inequality in Social Media](https://mlsanity.com/paper/1601.07200/) (2016)
- [An analysis of replies to Trump's tweets](https://mlsanity.com/paper/2011.11688/) (2020)

## Abstract (arXiv)

Attitudes about vaccination have become more polarized; it is common to see vaccine disinformation and fringe conspiracy theories online. An observational study of Twitter vaccine discourse is found in Ojea Quintana et al. (2021): the authors analyzed approximately six months' of Twitter discourse -- 1.3 million original tweets and 18 million retweets between December 2019 and June 2020, ranging from before to after the establishment of Covid-19 as a pandemic. This work expands upon Ojea Quintana et al. (2021) with two main contributions from data science. First, based on the authors' initial network clustering and qualitative analysis techniques, we are able to clearly demarcate and visualize the language patterns used in discourse by Antivaxxers (anti-vaccination campaigners and vaccine deniers) versus other clusters (collectively, Others). Second, using the characteristics of Antivaxxers' tweets, we develop text classifiers to determine the likelihood a given user is employing anti-vaccination language, ultimately contributing to an early-warning mechanism to improve the health of our epistemic environment and bolster (and not hinder) public health initiatives.