Disinformation and vaccines on social networks: Behavior of hoaxes on Twitter
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 discours...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad Católica San Antonio de Murcia (UCAM) |
| Repositorio: | RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia |
| OAI Identifier: | oai:repositorio.ucam.edu:10952/6642 |
| Acceso en línea: | http://hdl.handle.net/10952/6642 |
| Access Level: | acceso abierto |
| Palabra clave: | Disinformation Hoaxes Vaccines Artificial Intelligence Health Information |
| Sumario: | 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. |
|---|