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Assessing the Role of Social Bots During the COVID-19 Pandemic: Infodemic, Disagreement, and Criticism

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URI: http://hdl.handle.net/10498/28080

DOI: 10.2196/36085

ISSN: 1438-8871

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SC2022_535.pdf (686.9Kb)
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Autor/es
Sanz Suarez-Lledo, Víctor JoséAutoridad UCA; Álvarez Gálvez, JavierAutoridad UCA
Fecha
2022-08
Departamento/s
Biomedicina, Biotecnología y Salud Pública
Fuente
Journal of Medical Internet Research, Vol. 24, Núm. 8
Resumen
Background: 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.
Materias
infodemics; social media; misinformation; epidemics; outbreaks; COVID-19; infodemiology; health promotion; pandemic; chatbot; social media bot; Twitter stream; Botometer; peer support
Colecciones
  • Artículos Científicos [12001]
  • Articulos Científicos Biomedicina [582]
  • Artículos Científicos INDESS [1043]
Atribución 4.0 Internacional
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional

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