Demystifying the COVID-19 vaccine discourse on Twitter
Zainab Zaidi, Mengbin Ye, Fergus John Samon, Abdisalam Jama, Binduja, Gopalakrishnan, Chenhao Gu, Shanika Karunasekera, Jamie Evans, and Yoshihisa, Kashima

TL;DR
This study analyzes 75 million COVID-19 vaccine-related tweets to understand public discourse, revealing polarization, common topics, and the prevalence of falsehoods, with implications for future health communication strategies.
Contribution
It introduces a stance detection NLP algorithm and provides a comprehensive analysis of Twitter discourse on COVID-19 vaccination, highlighting user behavior and misinformation patterns.
Findings
Pro-vax tweets outnumber anti-vax tweets
Majority of tweets from dual-stance users posted both pro- and anti-vax content
Anti-vax discourse includes genuine concerns and falsehoods
Abstract
Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the current COVID-19 pandemic, but also for future pathogen outbreaks. We examine a Twitter dataset containing 75 million English tweets discussing COVID-19 vaccination from March 2020 to March 2021. We train a stance detection algorithm using natural language processing (NLP) techniques to classify tweets as `anti-vax' or `pro-vax', and examine the main topics of discourse using topic modelling techniques. While pro-vax tweets (37 million) far outnumbered anti-vax tweets (10 million), a majority of tweets from both stances (63% anti-vax and 53% pro-vax tweets) came from dual-stance users who posted both pro- and anti-vax tweets during the observation period. Pro-vax tweets focused mostly on vaccine development, while anti-vax tweets covered a wide range of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVaccine Coverage and Hesitancy · Misinformation and Its Impacts · Hate Speech and Cyberbullying Detection
