Disinformation about the Covid-19 vaccine in Brazil measuring the reach and impacts of fake news on health

Article Sidebar

PDF (Portuguese)
Published: 2022-10-26
DOI: https://doi.org/10.26807/rp.v26i114.1951
Keywords:
disinformation; bots; Covid-19; vaccines.

Main Article Content

Fernanda Vasques Ferreira
Universidade Católica de Brasília (UCB)
Rafiza Varão
Universidade de Brasília (UnB)
Marco Boselli
Universidade Federal de Uberlândia (UFU)

Abstract

In this text, we discuss the use of bots and artificial intelligence (AI) to combat the phenomenon of fake news and disinformation in the context of the Covid-19 pandemic. To this end, we selected content about vaccines checked and published by three Brazilian fact-checking agencies, as well as content about immunizers on Twitter. A bot in Python code measured the relationship and reach of these contents, evaluating possible impacts on the complex Brazilian social context, in May 2021. It is noticed that the use of AI can reduce the impacts of fake news on the media ecosystem. We highlight the importance of checking information and the need for it to have a scope and speed similar to the spread of fake news to save human lives by preventing through communication.

Article Details

Issue

Vol. 26 No. 114 (2022): Communication, Negationism, Ideology and Science

Section

Monographic
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Author Biographies

Fernanda Vasques Ferreira, Universidade Católica de Brasília (UCB)

PhD student in Communication at the University of Brasília, Master in Communication at the same institution. Professor of Journalism and Social Communication - Publicity and Propaganda courses at the Catholic University of Brasília (UCB)

Rafiza Varão, Universidade de Brasília (UnB)

Doutora em Comunicação pela Universidade de Brasília (UnB), professora do departamento de Jornalismo da Universidade de Brasília (UnB) e coordenadora da Rede Nacional de Observatórios da Imprensa (RENOI). Professora visitante da Universidade de Concordia, Montreal, Canadá. Dedica-se a estudos na área de jornalismo, teorias da comunicação, ética e desinformação. Membra do grupo de pesquisa GINC - Grupo de Estudos Multidisciplinares em Linguagens, Comunicação e Cultura.

Marco Boselli, Universidade Federal de Uberlândia (UFU)

Pós-Doutorado na Université Pierre et Marie Curie, LISE/CNRS, França. Doutorado em Física pela Universidade Estadual de Campinas. Professor Associado da Universidade Federal de Uberlândia (UFU). Membro do grupo de pesquisa GINC - Grupo de Estudos Multidisciplinares em Linguagens, Comunicação e Cultura.

How to Cite

Disinformation about the Covid-19 vaccine in Brazil: measuring the reach and impacts of fake news on health. (2022). Razón Y Palabra, 26(114). https://doi.org/10.26807/rp.v26i114.1951

References

Abonizio, H. Q., Morais, J. I., Tavares, G. M. & Barbon Junior, S. (2020). Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features. Future Internet, 12, 87. https://doi.org/10.3390/fi12050087

Barcelos, T. N., Muniz, L. N., Dantas, D. M., Cotrim Junior, D. F., Cavalcante, J. R., Faerstein, E. (2021) Análise de fake news veiculadas durante a pandemia de COVID-19 no Brasil. Rev. Panam. Salud Publica, 45, pp. 1-8. https://doi.org/10.26633/RPSP.2021.65

Chat Api (2019). A criação do Whatsapp bot em Python. O guia completo. https://chat-api.com/pt-br/whatsapp-bot-python.html

Ferrara, E. (2020) What types of COVID-19 conspiracies are populated by Twitter bots?

First Monday, 25, Number 6 - 1 doi: http://dx.doi.org/10.5210/fm.v25i6.10633

Garcia, M. How to Make a Twitter Bot in Python With Tweepy. https://realpython.com/twitter-bot-python-tweepy/

Glik, D. (2007). Risk communication for public health emergencies. Annual Review of Public Health, 28, 33-54. https://doi.org/10.1146/annurev.publhealth.28.021406.144123

Huszár, F., Ktena, S. I., O’Brien, C., Beli, L., Schlaikjer, A., Hardt, M. Algorithmic amplification of politics on Twitter. (2022) PANAS, 119, pp e2025334119.

https://doi.org/10.1073/pnas.2025334119

Katsaros, D.; Stavropoulos, G. & Papakostas, D. (2019) Which machine learning paradigm for fake news detection?, WI ’19, October 14–17, https://doi.org/10.1145/3350546.3352552

Kertysova, K. (2018). Artificial Intelligence and Disinformation, Security and Human Rights, 29(1-4), 55-81. doi: https://doi.org/10.1163/18750230-02901005

Koumchatzky, N. & Andryeyev, A. (2017). Using Deep Learning at Scale in Twitter’s Timelines

https://blog.twitter.com/engineering/en_us/topics/insights/2017/using-deep-learning-at-scale-in-twitters-timelines

Moura, D. O. (2008). Comunicação em saúde: apenas remediar ou participar e prevenir? In Mendonça, V. et al. (Org.). Comunicação da informação em saúde: aspectos de qualidade. Brasília: CID/UnB, 2008.

Organização Pan-americana da Saúde. (2020). Página Informativa nº 5 Entenda a infodemia e a desinformação na luta contra a COVID-19. Brasil: https://iris.paho.org/bitstream/handle/10665.2/52054/Factsheet-Infodemic_por.pdf?sequence=14

Paganotti, I. (2018). Notícias falsas, problemas reais: propostas de intervenção contra noticiários fraudulentos. In Costa & Blanco (orgs.). Pós-tudo e crise da democracia (pp. 96-105). São Paulo: ECA-USP. Doi: 10.11606/9788572052092.

Pérez-Dasilva, J.A., Meso-Ayerdi, K. & Mendiguren-Galdospín, T. (2020). Fake news y coronavirus: detección de los principales actores y tendencias a través del análisis de las conversaciones en Twitter. El profesional de la información, v. 29, n. 3, e290308. https://doi.org/10.3145/epi.2020.may

Pinheiro, M. M. K. & Brito, V. P. (2014). Em busca do significado da desinformação. DataGramaZero, 15, 6. http://hdl.handle.net/20.500.11959/brapci/8068

Recuero, R. (2021). Desinformação, mídia social e COVID-19 no Brasil: relatório, resultados e estratégias de combate. Pelotas, RS: MIDIARS - Grupo de Pesquisa em Mídia Discurso e Análise de Redes Sociais. Recuperado de https://wp.ufpel.edu.br/midiars/files/2021/05/Desinformac%CC%A7a%CC%83o-covid-midiars-2021-1.pdf

Ruediguer, M. A., Liguori Filho, C. A., Santos, E. F., Santos, G. K., Salvador, J. P. F., Karolczak, R. M., Guimarães, T., Aquino, T. M., Silveira, V. D. (2019). Bots e o Direito Eleitoral Brasileiro: nas eleições de 2018. FGV DAPP. http://hdl.handle.net/10438/26227.

Shchur, A. (2020). Fake news detector with deep learning approach (Part-I) EDA. Recuperado em 25 julho de 2021 de Medium.com: https://towardsdatascience.com/fake-news-detector-with-deep-learning-approach-part-i-eda-757f5c052

Singh, L., Bansal, S., Bode, L., Budak, C., Chi, G., Kawintiranon, K., Padden, C., Vanarsdall, R., Vraga, E. & Wang, Y. (2020). A first look at COVID-19 information and misinformation sharing on Twitter.

https://arxiv.org/pdf/2003.13907.pdf

Vosoughi, S., Roy, D., Aral, S. (2018) The spread of true and false news online. Science 359, 1146–1151. https://www.science.org/doi/10.1126/science.aap9559

Xu, G., Mu, Y. & Liu, J. (2018) Inclusion of artificial intelligence in communication networks and services. Itu Journal: ICT Discoveries, 1 (1). https://www.itu.int/en/journal/001/Documents/ITU2017-4.pdf

Wardle, C. (Fevereiro, 2017). Fake news. It’s complicated. Recuperado em 28 de maio de 2021 de Medium.com. https://medium.com/1st-draft/fake-newsits-complicated-d0f773766c79

Similar Articles

You may also start an advanced similarity search for this article.