Demystifying the COVID-19 vaccine discourse on Twitter
This research addresses the problem of understanding public discourse on vaccination for public health officials and policymakers, though it is incremental as it applies existing NLP methods to a new dataset.
The study analyzed 75 million English tweets about COVID-19 vaccination from March 2020 to March 2021, finding that pro-vax tweets (37 million) outnumbered anti-vax tweets (10 million), but a majority of tweets from both stances came from dual-stance users, and it identified key topics and the prevalence of memes and jokes.
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 topics, some of which included genuine concerns, though there was a large dose of falsehoods. A number of topics were common to both stances, though pro- and anti-vax tweets discussed them from opposite viewpoints. Memes and jokes were amongst the most retweeted messages. Whereas concerns about polarisation and online prevalence of anti-vax discourse are unfounded, targeted countering of falsehoods is important.