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 Mariah; Alves dos Santos Santana, Laura Emmanuella; Da Costa Abreu, Márjory (Anais do Simpósio Brasileiro de Cibersegurança (SBSeg), 2021)
The dissemination of fake news is a problem that has already been addressed but by no means is solved. After the manipulation made by Cambridge Analytica which was based on classifying users by their political views and targeting 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 in ‘normal times’. 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 with Covid-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 have focused on posts from four different cities (Natal, São Paulo, Sheffield and London) and have shown interesting pointers that will be discussed.