Caracterização da reação de agências de fact-checking às publicações sobre a pandemia da COVID-19 em redes sociais
Abstract
Since the beginning of the COVID-19 pandemic, it has been noticed, especially in social media, the generation of content about this subject. Social medias are an important tool of communication, however, they also create a space to misinformation spreading. This work aims to characterize how factchecking agencies have been combating false information about COVID-19 that circulates on Twitter and Facebook. Fact-checking materials about COVID-19 written by specialized agencies from different countries were collected. Through the news, we searched for social media posts which the misinformation started to be spread. After collecting this material, it was verified how long it took for the agencies to analyze the veracity of the post and react to it. Besides, the news texts were also processed to detect whether the subjects being dealt with by the agencies are, in fact, the ones that have the greatest user engagement within the analyzed social networks. The results showed that the agencies' response time was, on average, 23 days in the case of false posts on Twitter and 6 days on Facebook.
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Amapa, O. (2020). Oms: transmissão de covid-19 a partir de assintomáticos é “muito rara. Amapá Online.
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Brennen, D. J. S., Simon, F., Howard, D. P. N., and Nielsen, P. R. K. (2020). Types, sources, and claims of covid-19 misinformation.
Broniatowski, D. A., Jamison, A. M., Qi, S., AlKulaib, L., Chen, T., Benton, A., Quinn, S. C., and Dredze, M. (2018). Weaponized health communication: Twitter bots and russian trolls amplify the vaccine debate. American Journal of Public Health, 108(10):1378–1384. PMID: 30138075.
Castillo, C., Mendoza, M., and Poblete, B. (2011). Information credibility on twitter. In Proceedings of the 20th International Conference on World Wide Web, WWW ’11, page 675–684, New York, NY, USA. Association for Computing Machinery.
Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., and Scala, A. (2020). The covid-19 social media infodemic. Scientific Reports, 10(1):16598.
Goeking, W. (2020). Auxílio emergencial é alvo de golpes na internet; veja como evitar ciladas. Valor Investe.
Jordan, S. E., Hovet, S. E., Fung, I. C.-H., Liang, H., Fu, K.-W., and andZion Tsz Ho Tse (2019). Using twitter for public health surveillance from monitoring and prediction to public response. Data.
Larson, H. J. (2018). The biggest pandemic risk? viral misinformation. (562).
Lewandowsky, S., Ecker, U. K. H., Seifert, C. M., Schwarz, N., and Cook, J. (2012). Misinformation and its correction: Continued inuence and successful debiasing. Psychological Science in the Public Interest, 13(3):106–131. PMID: 26173286.
Lima, S. (2021). Não há provas de que vídeo de mulher tremendo ao caminhar tenha relação com vacina da pfizer. Acessado em 06/03/2021: [link].
Moraes, M. (2020). Brasil lidera desinformação sobre número de casos e mortes por covid-19 no mundo. Acessado em 12/06/2020.
Poynter (2020). The coronavirusfacts/datoscoronavirus alliance database.
Röder, M., Both, A., and Hinneburg, A. (2015). Exploring the space of topic coherence measures. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, WSDM ’15, page 399–408, New York, NY, USA. Association for Computing Machinery.
Shah, Z., Surian, D., Dyda, A., Coiera, E., Mandl, K. D., and Dunn, A. G. (2019). Automatically appraising the credibility of vaccine-related web pages shared on social media: A twitter surveillance study. J Med Internet Res.
Sousa Jr, J. H., Raasch, M., Soares, J. C., Virgínia, L., and de Sousa Ribeiro, H. A. (2020). Da desinformação ao caos: uma análise das fake news frente à pandemia do coronavírus (covid-19) no brasil. Cadernos de Prospecção.
Tandoc Jr, E. C., Lim, Z. W., and Ling, R. (2018). Defining “fake news”. Digital Journalism, 6(2):137–153.
Vlachos, A. and Riedel, S. (2014). Fact checking: Task definition and dataset construction. In Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pages 18–22, Baltimore, MD, USA. Association for Computational Linguistics.
Vogel, L. (2017). Viral misinformation threatens public health. CMAJ, 189(50):E1567– E1567.
WHO (2020). (covid-19) situation report disease Coronavirus World Health Organization. Acessado – em 07/06/2020: 139. [link].
Published
2021-08-16
How to Cite
PEREIRA, Cefas Garcia; MARQUES-NETO, Humberto Torres.
Caracterização da reação de agências de fact-checking às publicações sobre a pandemia da COVID-19 em redes sociais. In: BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (SBRC), 39. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 113-126.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2021.16715.
