Fake News on the Covid-19 outbreak: a new metadata-based dataset for the analysis of Brazilian and British Twitter posts

LIMA DO NASCIMENTO, Tuany Maria, ALVES DOS SANTOS SANTANA, Laura Emmanuella and DA COSTA ABREU, Marjory (2021). Fake News on the Covid-19 outbreak: a new metadata-based dataset for the analysis of Brazilian and British Twitter posts. In: E EDUARDO SOUTO, Marco Amaral Henriques, (ed.) 2021: Proceedings of the 21st Brazilian Symposium on Information and Computational Systems Security. SBSeg, 397-402.

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Official URL: https://sol.sbc.org.br/index.php/sbseg/article/vie...
Link to published version:: https://doi.org/10.5753/sbseg.2021.17332
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Abstract. The dissemination of fake news is a problem that has already been ad-dressed but by no means is solved. After the manipulation made by Cambridge Analytica which was based on classifying users by their political views and tar-geting specific political propaganda on the Brexit campaign, the Trump election and the Bolsonaro election, there is no doubt this issue can have a real impact on society. During a pandemic, any type of fake news can be the difference between life and death when the data shared can directly hurt the people who are believing in it. Moreover, there is also a new trend of using artificial robots to disseminate such news with a special target on Twitter which can be linked with political campaigns. Thus, it is essential that we identify and understand what kind of news is selected to be dressed as fake and how it is disseminated. This paper aims to investigate the dissemination of fake news related withCovid-19 in the UK and Brazil in order to understand the impact of fake news on public sector actions, social isolation and quarantine imposition. Those two case studies are well versed on the fake news dissemination. Our initial dataset of Twitter posts has focused on posts from four different cities (Natal, Sao Paulo, Sheffield and London) and has shown interesting pointers that will be discussed.

Item Type: Book Section
Identification Number: https://doi.org/10.5753/sbseg.2021.17332
Page Range: 397-402
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 08 Sep 2021 12:22
Last Modified: 18 Oct 2021 08:00
URI: https://shura.shu.ac.uk/id/eprint/28997

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