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dc.contributor.authorSanz Suarez-Lledo, Víctor José 
dc.contributor.authorÁlvarez Gálvez, Javier 
dc.contributor.otherBiomedicina, Biotecnología y Salud Públicaes_ES
dc.date.accessioned2023-03-10T11:02:29Z
dc.date.available2023-03-10T11:02:29Z
dc.date.issued2022-08
dc.identifier.issn1438-8871
dc.identifier.urihttp://hdl.handle.net/10498/28080
dc.description.abstractBackground: Social media has changed the way we live and communicate, as well as offering unprecedented opportunities to improve many aspects of our lives, including health promotion and disease prevention. However, there is also a darker side to social media that is not always as evident as its possible benefits. In fact, social media has also opened the door to new social and health risks that are linked to health misinformation. Objective: This study aimed to study the role of social media bots during the COVID-19 outbreak. Methods: The Twitter streaming API was used to collect tweets regarding COVID-19 during the early stages of the outbreak. The Botometer tool was then used to obtain the likelihood of whether each account is a bot or not. Bot classification and topic-modeling techniques were used to interpret the Twitter conversation. Finally, the sentiment associated with the tweets was compared depending on the source of the tweet. Results: Regarding the conversation topics, there were notable differences between the different accounts. The content of nonbot accounts was associated with the evolution of the pandemic, support, and advice. On the other hand, in the case of self-declared bots, the content consisted mainly of news, such as the existence of diagnostic tests, the evolution of the pandemic, and scientific findings. Finally, in the case of bots, the content was mostly political. Above all, there was a general overriding tone of criticism and disagreement. In relation to the sentiment analysis, the main differences were associated with the tone of the conversation. In the case of self-declared bots, this tended to be neutral, whereas the conversation of normal users scored positively. In contrast, bots tended to score negatively. Conclusions: By classifying the accounts according to their likelihood of being bots and performing topic modeling, we were able to segment the Twitter conversation regarding COVID-19. Bot accounts tended to criticize the measures imposed to curb the pandemic, express disagreement with politicians, or question the veracity of the information shared on social media.es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherJMIR PUBLICATIONS, INCes_ES
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceJournal of Medical Internet Research, Vol. 24, Núm. 8es_ES
dc.subjectinfodemicses_ES
dc.subjectsocial mediaes_ES
dc.subjectmisinformationes_ES
dc.subjectepidemicses_ES
dc.subjectoutbreakses_ES
dc.subjectCOVID-19es_ES
dc.subjectinfodemiologyes_ES
dc.subjecthealth promotiones_ES
dc.subjectpandemices_ES
dc.subjectchatbotes_ES
dc.subjectsocial media botes_ES
dc.subjectTwitter streames_ES
dc.subjectBotometeres_ES
dc.subjectpeer supportes_ES
dc.titleAssessing the Role of Social Bots During the COVID-19 Pandemic: Infodemic, Disagreement, and Criticismes_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.2196/36085
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118589RB-I00/ES/DINAMICAS COLECTIVAS DE CONTAGIO DE OPINIONES SOBRE SALUD: LA INFODEMIA DE COVID19 Y SUS EFECTOS SOBRE LOS PROCESOS DE TOMA DE DECISION /es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO//RYC-2016-19353/ES/RYC-2016-19353/es_ES


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Atribución 4.0 Internacional
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional