This chapter addresses the creation of political conflict on Twitter in a comparative study between Brazil and Spain. Based on an analysis of the political debate on dealing with two countries' health crises, it analyses the most retweeted messages published during the first week of vaccination in Europe and the Americas. Firstly, it analysed the general characteristics of the online debate on the immunisation of COVID-19. Secondly, it carried out an analysis of information disorder in each country. Although governmental positions in both countries are opposed, the results allow establishing common patterns of polarized profiles in both countries that question the management of the pandemic. It can be seen how political polarization is shaped as a characteristic of disinformation in both countries. That reveals that, after the health crisis, there is a crisis of democratic institutions that impact public health actions, but specifically to combat COVID-19.
TopIntroduction
This chapter addresses political conflict on Twitter using a comparative study between Brazil and Spain and considers information disorder as a critical element of that conflict. The transformation that the digital public sphere has undergone since the 2016 U.S. election is crucial in interpreting its analysis. Since then, international literature has questioned the destructive role of social media platforms and the potentially damaging risk to democracies (Chadwick, 2019). This all resulted after large-scale automated activity on social networks designed to manipulate public attention during the 2016 U.S. presidential election (Kollanyi et al., 2016), the 2018 Brazilian election (Nascimento & Alves, 2018), the Brexit referendum (Bastos & Mercea, 2019), among others, became public.
Since then, currents and studies that address monopoly power based on surveillance capitalism have become visible (Couldry & Mejías, 2019), and deep uncertainty about the long-term impact of digital forms of communication has been spreading. For example, studies have shown that social media channels carried fake news, usually by bots, to generate confusion, mutual distrust, intolerance and even hatred on issues of great social importance, such as migration (Quandt, 2018). This created states of confusion among citizens in the face of politically relevant processes, with socially ambivalent motivations that lead people to share false and misleading information (Chadwick et al., 2018; Chadwick & Vaccari, 2019).
From here, the authors interpret the logic of algorithms and perceptions about disinformation, which, as Kreiss argues, can imply the fracturing of how people understand and agree on facts and political truths (O’Neil, 2016; Kreiss, 2017; Waisbord, 2018).
With the advancement of the COVID-19 pandemic, the spread of misinformation, fake news, conspiracy theories, “magic remedies”, and other false information on social media have caused enormous problems for countries in developing policies to fight the virus. Brazil and Spain are two such cases because, in addition to the large number of victims affected by COVID-19, they have also witnessed intense online conflicts over measures to prevent and combat the pandemic.
Studies show that political polarization and disinformation on social media are key factors that hinder the adoption of measures to combat COVID-19 (Van Bavel et al., 2020; Oxford Analytica, 2020). The absence of a political consensus to face the threat of COVID-19, especially among political leaders, makes it difficult for the population to mobilize in response to the crisis (Green et al., 2020). The polarization on social media creates a favorable environment to spread misinformation and conspiracy theories (Van Bavel et al., 2020).
The authors have opted not to use the term “fake news” as it is largely applied to describe the dissemination of fake content, while the disinformation on social media is mostly composed of genuine content as rumors, memes, manipulated videos or photos, but shaped or reframed to mislead readers. Wardle & Derakhshan (2018) proposed a framework to examine information disorder phenomena, identifying three different types: mis-, dis- and mal-information. Misinformation is when “false information is shared, but no harm is meant”, disinformation is when “false information is knowingly shared to cause harm” and mal-information is when “genuine information is shared to cause harm, often by moving information designed to stay private into the public sphere”.