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.

通过自然语言处理技术训练立场检测算法，通过主题建模技术检查疫苗接种的公共言论，发现双重立场使用者发布的推文占主要地位，正反两面的推文共同讨论了许多不同的问题，以真实问题为主的反疫苗推文与虚假信息相关的含义占大多数，表情和笑话占据了最多的转发信息。