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 antivaccine 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 as opposed to its objection by accounts created between 2013 and
2020, and the association of the acknowledgment (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 other phenomena on social networks.Ciencias de la Comunicació