Câmaras de eco e mis/desinformação sobre vacinas contra a COVID-19 no Twitter: abordagem baseada em machine learning e análise de redes sociais
Autor(a) principal: | |
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Data de Publicação: | 2023 |
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Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/40159 |
Resumo: | Given the infodemiological importance of echo chambers in the dissemination of mis/disinformation, we aimed to analyze the interaction networks of users most exposed to mis/disinformation or controversy about vaccines in the context of the COVID-19 pandemic. To this end, a methodology based on machine learning and Social Network Analysis is proposed in this research for automated detection of controversial and mis/disinformative content about vaccines, through which a model with 92% accuracy was achieved. Out of the nearly 24 million tweets collected, 12.4 million (52%) were flagged as controversial and/or potential for mis/disinformation, and the months of January and June 2021 were those with the highest activity, being analyzed through a cohort. Unlike previous work, we analyzed the network of all ways of interacting on Twitter, and the entire textual structure of the tweets - not just links or hashtags -. Regarding the conversation about COVID-19 vaccines, the findings were different from those associated with party-political discussion previously described in the literature, since the network of mentions and replies privileges heterophilic relationships, and "echo" conformations were not observable. Finally, further studies are needed to better understand the dissemination of misinformation about vaccines on Twitter. |
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Câmaras de eco e mis/desinformação sobre vacinas contra a COVID-19 no Twitter: abordagem baseada em machine learning e análise de redes sociais Echo chambers and vaccines against COVID-19 mis/disinformation on Twitter: machine learning and network analysis-based approach Cámaras de eco y desinformación sobre vacunas contra COVID-19 en Twitter: enfoque basado en machine learning y análisis de redes VaccineInfodemicsFake NewsEcho ChambersTwitterMachine learning.VacinaInfodemiaFake NewsCâmaras de EcoTwitterMachine learning.VacunaInfodemiaFake NewsCámaras de EcoTwitterMachine learning.Given the infodemiological importance of echo chambers in the dissemination of mis/disinformation, we aimed to analyze the interaction networks of users most exposed to mis/disinformation or controversy about vaccines in the context of the COVID-19 pandemic. To this end, a methodology based on machine learning and Social Network Analysis is proposed in this research for automated detection of controversial and mis/disinformative content about vaccines, through which a model with 92% accuracy was achieved. Out of the nearly 24 million tweets collected, 12.4 million (52%) were flagged as controversial and/or potential for mis/disinformation, and the months of January and June 2021 were those with the highest activity, being analyzed through a cohort. Unlike previous work, we analyzed the network of all ways of interacting on Twitter, and the entire textual structure of the tweets - not just links or hashtags -. Regarding the conversation about COVID-19 vaccines, the findings were different from those associated with party-political discussion previously described in the literature, since the network of mentions and replies privileges heterophilic relationships, and "echo" conformations were not observable. Finally, further studies are needed to better understand the dissemination of misinformation about vaccines on Twitter.Dada la importancia infodemiológica de las cámaras de eco en la difusión de información errónea/desinformación, nos propusimos analizar las redes de interacción de los usuarios más expuestos a la información errónea/desinformación o controversia sobre las vacunas en el contexto de la pandemia COVID-19. Para ello, en esta investigación se propone una metodología basada en aprendizaje de máquina y Análisis de Redes Sociales para la detección automatizada de contenidos controvertidos y erróneos/desinformativos sobre vacunas, a través de la cual se alcanzó un modelo con un 92% de precisión. De los casi 24 millones de tuits recogidos, 12,4 millones (52%) fueron marcados como controvertidos y/o de potencial desinformación, siendo los meses de enero y junio de 2021 los de mayor actividad, analizándose a través de una cohorte. A diferencia de trabajos anteriores, analizamos la red de todas las formas de interactuar en Twitter, y toda la estructura textual de los tuits -no sólo los enlaces o los hashtags-. En cuanto a la conversación sobre las vacunas COVID-19, los resultados fueron diferentes de los asociados a la discusión partidista descritos anteriormente en la literatura, ya que la red de menciones y respuestas privilegia las relaciones heterófilas, y que no se observaron conformaciones "eco". Por último, se necesitan más estudios para comprender mejor la difusión de información errónea sobre las vacunas en Twitter.Haja visto a importância infodemiológica das câmaras de eco na disseminação de mis/desinformação, objetivou-se analisar as redes de interação dos usuários mais expostos à mis/desinformação ou controvérsia sobre vacinas no âmbito da pandemia COVID-19. Para tal, é proposto nesta investigação uma metodologia baseada em machine learning e Análise de Redes Sociais para detecção automatizada de conteúdo controverso e mis/desinformativo sobre vacinas, através da qual chegou-se a um modelo com 92% de acurácia. Dos quase 24 milhões de tweets coletados, 12.4 milhões (52%) foram assinalados como controversos e/ou potenciais à mis/desinformação, sendo os meses de janeiro e junho de 2021 aqueles de maior atividade, sendo analisados através de uma coorte. Diferentemente dos trabalhos anteriores, analisou-se a rede de todas as maneiras de interagir no Twitter, e de toda a estrutura textual dos tweets – não apenas links ou hashtags -. No que concerne à conversação sobre vacinas contra COVID-19, os achados foram diferentes daqueles associados à discussão político-partidária anteriormente descritos na literatura, uma vez que a rede de menções e respostas privilegia relações heterofílicas, e que conformações de “eco” não foram observáveis. Finalmente, faz-se necessário novos estudos para melhor compreender a disseminação de desinformação acerca das vacinas no Twitter.Research, Society and Development2023-02-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/4015910.33448/rsd-v12i2.40159Research, Society and Development; Vol. 12 No. 2; e22812240159Research, Society and Development; Vol. 12 Núm. 2; e22812240159Research, Society and Development; v. 12 n. 2; e228122401592525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/40159/32999Copyright (c) 2023 Arthur da Silva Lopes; Antonio Brotashttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessLopes, Arthur da Silva Brotas, Antonio2023-02-14T20:07:52Zoai:ojs.pkp.sfu.ca:article/40159Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2023-02-14T20:07:52Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Câmaras de eco e mis/desinformação sobre vacinas contra a COVID-19 no Twitter: abordagem baseada em machine learning e análise de redes sociais Echo chambers and vaccines against COVID-19 mis/disinformation on Twitter: machine learning and network analysis-based approach Cámaras de eco y desinformación sobre vacunas contra COVID-19 en Twitter: enfoque basado en machine learning y análisis de redes |
title |
Câmaras de eco e mis/desinformação sobre vacinas contra a COVID-19 no Twitter: abordagem baseada em machine learning e análise de redes sociais |
spellingShingle |
Câmaras de eco e mis/desinformação sobre vacinas contra a COVID-19 no Twitter: abordagem baseada em machine learning e análise de redes sociais Lopes, Arthur da Silva Vaccine Infodemics Fake News Echo Chambers Machine learning. Vacina Infodemia Fake News Câmaras de Eco Machine learning. Vacuna Infodemia Fake News Cámaras de Eco Machine learning. |
title_short |
Câmaras de eco e mis/desinformação sobre vacinas contra a COVID-19 no Twitter: abordagem baseada em machine learning e análise de redes sociais |
title_full |
Câmaras de eco e mis/desinformação sobre vacinas contra a COVID-19 no Twitter: abordagem baseada em machine learning e análise de redes sociais |
title_fullStr |
Câmaras de eco e mis/desinformação sobre vacinas contra a COVID-19 no Twitter: abordagem baseada em machine learning e análise de redes sociais |
title_full_unstemmed |
Câmaras de eco e mis/desinformação sobre vacinas contra a COVID-19 no Twitter: abordagem baseada em machine learning e análise de redes sociais |
title_sort |
Câmaras de eco e mis/desinformação sobre vacinas contra a COVID-19 no Twitter: abordagem baseada em machine learning e análise de redes sociais |
author |
Lopes, Arthur da Silva |
author_facet |
Lopes, Arthur da Silva Brotas, Antonio |
author_role |
author |
author2 |
Brotas, Antonio |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Lopes, Arthur da Silva Brotas, Antonio |
dc.subject.por.fl_str_mv |
Vaccine Infodemics Fake News Echo Chambers Machine learning. Vacina Infodemia Fake News Câmaras de Eco Machine learning. Vacuna Infodemia Fake News Cámaras de Eco Machine learning. |
topic |
Vaccine Infodemics Fake News Echo Chambers Machine learning. Vacina Infodemia Fake News Câmaras de Eco Machine learning. Vacuna Infodemia Fake News Cámaras de Eco Machine learning. |
description |
Given the infodemiological importance of echo chambers in the dissemination of mis/disinformation, we aimed to analyze the interaction networks of users most exposed to mis/disinformation or controversy about vaccines in the context of the COVID-19 pandemic. To this end, a methodology based on machine learning and Social Network Analysis is proposed in this research for automated detection of controversial and mis/disinformative content about vaccines, through which a model with 92% accuracy was achieved. Out of the nearly 24 million tweets collected, 12.4 million (52%) were flagged as controversial and/or potential for mis/disinformation, and the months of January and June 2021 were those with the highest activity, being analyzed through a cohort. Unlike previous work, we analyzed the network of all ways of interacting on Twitter, and the entire textual structure of the tweets - not just links or hashtags -. Regarding the conversation about COVID-19 vaccines, the findings were different from those associated with party-political discussion previously described in the literature, since the network of mentions and replies privileges heterophilic relationships, and "echo" conformations were not observable. Finally, further studies are needed to better understand the dissemination of misinformation about vaccines on Twitter. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-02-09 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/40159 10.33448/rsd-v12i2.40159 |
url |
https://rsdjournal.org/index.php/rsd/article/view/40159 |
identifier_str_mv |
10.33448/rsd-v12i2.40159 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/40159/32999 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2023 Arthur da Silva Lopes; Antonio Brotas https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2023 Arthur da Silva Lopes; Antonio Brotas https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 12 No. 2; e22812240159 Research, Society and Development; Vol. 12 Núm. 2; e22812240159 Research, Society and Development; v. 12 n. 2; e22812240159 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052618133995520 |