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...

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Autores: Noguera Vivo, José Manuel, Grandío Pérez, María del Mar, Villar Rodríguez, Guillermo, Martín, Alejandro, Camacho, David
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
Twitter
Artificial Intelligence
Health Information
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spelling Disinformation and vaccines on social networks: Behavior of hoaxes on TwitterNoguera Vivo, José ManuelGrandío Pérez, María del MarVillar Rodríguez, GuillermoMartín, AlejandroCamacho, DavidDisinformationHoaxesVaccinesTwitterArtificial IntelligenceHealth InformationAnti-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 Comunicación2023info:eu-repo/semantics/articlehttp://hdl.handle.net/10952/6642reponame:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murciainstname:Universidad Católica San Antonio de Murcia (UCAM)InglésFundación BBVA, within the call for research teams on SARS-CoV-2 and COVID-19, with the CIVIC project: Intelligent Characterization of the Accuracy of Information related to COVID-19 (2021-2022)info:eu-repo/semantics/openAccessoai:repositorio.ucam.edu:10952/66422026-06-07T18:35:21Z
dc.title.none.fl_str_mv Disinformation and vaccines on social networks: Behavior of hoaxes on Twitter
title Disinformation and vaccines on social networks: Behavior of hoaxes on Twitter
spellingShingle Disinformation and vaccines on social networks: Behavior of hoaxes on Twitter
Noguera Vivo, José Manuel
Disinformation
Hoaxes
Vaccines
Twitter
Artificial Intelligence
Health Information
title_short Disinformation and vaccines on social networks: Behavior of hoaxes on Twitter
title_full Disinformation and vaccines on social networks: Behavior of hoaxes on Twitter
title_fullStr Disinformation and vaccines on social networks: Behavior of hoaxes on Twitter
title_full_unstemmed Disinformation and vaccines on social networks: Behavior of hoaxes on Twitter
title_sort Disinformation and vaccines on social networks: Behavior of hoaxes on Twitter
dc.creator.none.fl_str_mv Noguera Vivo, José Manuel
Grandío Pérez, María del Mar
Villar Rodríguez, Guillermo
Martín, Alejandro
Camacho, David
author Noguera Vivo, José Manuel
author_facet Noguera Vivo, José Manuel
Grandío Pérez, María del Mar
Villar Rodríguez, Guillermo
Martín, Alejandro
Camacho, David
author_role author
author2 Grandío Pérez, María del Mar
Villar Rodríguez, Guillermo
Martín, Alejandro
Camacho, David
author2_role author
author
author
author
dc.subject.none.fl_str_mv Disinformation
Hoaxes
Vaccines
Twitter
Artificial Intelligence
Health Information
topic Disinformation
Hoaxes
Vaccines
Twitter
Artificial Intelligence
Health Information
description 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.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10952/6642
url http://hdl.handle.net/10952/6642
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Fundación BBVA, within the call for research teams on SARS-CoV-2 and COVID-19, with the CIVIC project: Intelligent Characterization of the Accuracy of Information related to COVID-19 (2021-2022)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
instname:Universidad Católica San Antonio de Murcia (UCAM)
instname_str Universidad Católica San Antonio de Murcia (UCAM)
reponame_str RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
collection RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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