![]() ![]() In this war, political instability can be considered as a fertile soil to the spread of false or misleading information (Lazer et al. The alignment with specific groups turned the debate into a wrestling between scientific discoveries and leaders’ opinions based on personal experience or unreliable sources. The mismatch between health agencies, scientist and state leaders was brought to social media having an impact on people’s opinions over the use of masks, social distancing, lockdown, and even about the guidelines for preventing, treating or curing from the virus (Graham et al. The political polarization in many democracies contributed to a heated debate by non-experts over what measures are acceptable or not to contain the virus spread and treat infected people. ![]() 2017), the COVID-19 pandemic brought a strong political component to it. 2017).Īlthough social media such as Twitter has been previously used to understand and predict the spread of other endemic diseases (Albinati et al. On the other hand, the spread of messages in social media can lead to a massive misinformation based on conspiracy theories, as well as unfounded panic (Bhattacharya et al. The opinions spread about the current pandemic can help people understand the disease and behave well regarding the proper health protocols recommended by reliable sources. ![]() Their reach and impact depends on a few aspects as the personality traits of those who receive the information and the strength of the connections between people in the network (Araújo et al. It is known that opinions spread out across social media platforms through users’ connections. The amount of opinions, statements and news spread and shared in social media turned it into one of the main sources of information regarding the outcomes and results of public policies about the coronavirus pandemic (Gallotti et al. Social media is part of society, and has proven its value during many global events and catastrophes even before the COVID-19 pandemic started in 2020. We collected over 100 million tweets from 26 April 2020 to 3 January 2021, and observed in general a highly polarized population (with polarization index varying from 0.57 to 0.86), which focuses on very different topics of discussions over the most polarized weeks–but all related to government and health-related events. In this work we present a computational method to analyze Twitter data and: (i) identify users with a high probability of being bots using only COVID-19 related messages (ii) quantify the political polarization of the Brazilian general public in the context of the COVID-19 pandemic (iii) analyze how bots tweet and affect political polarization. Particularly in highly politically polarized countries, users tend to be divided in those in-favor or against government policies. Divergent opinions regarding these measures are leading to heated discussions and polarization. The discussions in many social media platforms is related not only to health aspects of the disease, but also public policies and non-pharmacological measures to mitigate the spreading of the virus and propose alternative treatments. The debate over the COVID-19 pandemic is constantly trending at online conversations since its beginning in 2019. ![]()
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