ABSTRACT
Introduction: Anti-vaccine disinformation is highly dangerous due to its direct effects on society. Although there is relevant research on typologies of hoaxes, denialist discourses on networks or the popularity of vaccines, this study provides a complementary and pioneering vision about the anti-vaccine discourse of COVID-19 on Twitter, focused on its spreaders’ behavior. Methodology: Given an initial sample of a hundred hoaxes (from December 2020 to September 2021) for the download of 200,246 tweets, around 36,000 tweets (N=36.292) that support or deny disinformation have been filtered through an algorithm for Natural Language Inference (NLI) to analyze their spreaders’ through their metrics in the platform. Results: In relative numbers, the results show, among others, more hoaxes with original content (not retweets) among accounts with more followers and those verified;more irruption of disinformation opposed to its objection by accounts created between 2013 and 2020, and the association of the acknowledgement (more presence in lists or many more followers than followed users) to the preference for denying false information instead of approving it. Discussion: The article shows how the typology of the accounts can be a predictive factor about the behavior of users who spread disinformation. Conclusions: Similar behavioral patterns of anti-vaccine discourse are revealed according to the accounts’ Twitter-related indicators. The size of the sample and the techniques used give a solid foundation for other comparative studies on disinformation about health and on other phenomena on social networks. © 2023, University of La Laguna. All rights reserved.