Polarização, fragmentação, desinformação e intolerância: dinâmicas problemáticas para a esfera pública nas discussões políticas no Twitter

O problema de pesquisa que guia esta tese é: como se caracterizam as dinâmicas problemáticas para a esfera pública nas discussões políticas no Twitter? São analisadas quatro dinâmicas, definidas com base nas discussões teóricas sobre fenômenos que ameaçam ou prejudicam a esfera pública e a democracia: (1) polarização, a divisão entre grupos com posicionamentos opostos (BARBERÁ, 2020); (2) fragmentação, o isolamento de grupos em contextos de redução de acesso a conteúdo heterogêneo, como as câmaras de eco (SUNSTEIN, 2001, 2017); (3) desinformação, informações distorcidas, manipuladas ou interamente fabricadas que possuem a função de enganar (FALLIS, 2015); (4) intolerância, discursos antidemocráticos, que envolvem o ataque a grupos sociais, ameaças pessoais e reforço de sentimentos contrários aos valores democráticos (PAPACHARISSI, 2004; ROSSINI, 2019a). São utilizados métodos mistos para analisar quatro discussões políticas com foco em Jair Bolsonaro durante as campanhas eleitorais brasileiras de 2018. A Análise de Redes Sociais é utilizada para analisar redes de retweets, redes de circulação de URLs e redes de replies. A Análise de Conteúdo é utilizada para analisar amostras das mensagens mais retuitadas, URLs que mais circularam e amostras de respostas a tweets. Os resultados mostram (1) discussões polarizadas, em que são identificados um grupo anti-Bolsonaro e outro pró-Bolsonaro. Estas estruturas favorecem a circulação de conteúdo distinto em cada grupo. (2) Apesar da polarização, a fragmentação é limitada, já que as redes de replies mostram interações que cruzam barreiras ideológicas e a análise das fontes de informações mais utilizadas nos grupos mostra sobreposições. (3) A desinformação ocorre de forma assimétrica, com o grupo pró-Bolsonaro compartilhando mais conteúdo desinformativo. Além disso, há uma dinâmica coletiva que faz da desinformação sistêmica, já que os índices de desinformação no grupo seguem padrões semelhantes de variações em todos os níves (Tweets mais retuitados, URLs que mais circularam e amostras de respostas). (4) a intolerância também é identificada de forma assimétrica, novamente mais associada ao grupo pró-Bolsonaro. Além disso, a intolerância é identificada em um número reduzido de mensagens, enquanto a impolidez, ou seja, comentários rudes, uso de termos de baixo calão, etc, aparece com índices mais amplos. A partir destes resultados, argumenta-se que as estruturas e dinâmicas que caracterizam as discussões políticas no Twitter não são pressupostos das arenas da esfera pública na plataforma, mas resultado das ações dos usuários, influenciadas por um contexto social mais amplo.Our research problem is as follows: how are characterized the problematic dynamics to the public sphere in political discussions on Twitter? We analyze four dynamics, which are selected based on the theoretical discussions about the phenomena that threaten or harm the public sphere and democracy: (1) polarization, the division between groups with opposite views (BARBERÁ, 2020); (2) fragmentation, the isolation of groups in context with reduced access to heterogeneous content, such as echo chambers (SUNSTEIN, 2001, 2017); (3) disinformation, distorted, manipulated or entirely fabricate information that has the function of misleading (FALLIS, 2015); (4) intolerance, anti-democratic discourse, which includes attacks toward social groups, personal threats and reinforcing sentiments that are opposite to the democratic values (PAPACHARISSI, 2004; ROSSINI, 2019a). We use mixed methods to analyze four political discussions about Jair Bolsonaro during the 2018 Brazilian presidential election campaigns. We use Social Network Analysis to analyze retweet networks, URLs circulation networks and replies networks. We use Content Analysis to analyze samples of the most retweeted messages, most shared URLs and replies. Our main findings are: (1) polarized discussions, in which we identified an anti-Bolsonaro group and a pro-Bolsonaro group. The polarized structure favored the circulation of different content within each group. (2) Although the polarized structure, the fragmentation is limited, as we identified cross-cutting interactions in the replies networks and overlaps in the analysis of the most central sources of information within the groups. (3) The disinformation is asymmetric, as the pro-Bolsonaro group shares more disinformation. In addition, we identified a collective dynamic that makes disinformation spread systemic. The amount of disinformation follows a pattern within the pro-Bolsonaro group at every level (most retweeted messages, most shared URLs, samples of replies). (4) The intolerance is also asymmetric, once again most prevalent within the pro-Bolsonaro group. Furthermore, we identified less intolerant messages compared to impolite messages, that is, rude comments, swearing, etc. Based on these results, we argue that the structures and dynamics of the political discussions on Twitter are the results of users’ actions, influenced by a broader social context, not presuppositions of public arenas on the platform

How the Mainstream Media Help to Spread Disinformation about Covid-19

In this article, we hypothesise how mainstream media coverage can promote the spread of disinformation about Covid-19. Mainstream media are often discussed as opposed to disinformation (Glasser; Benkler et al.). While the disinformation phenomenon is related to the intentional production and spread of misleading and false information to influence public opinion (Fallis; Benkler et al.), mainstream media news is expected to be based on facts and investigation and focussed on values such as authenticity, accountability, and autonomy (Hayes et al.). However, journalists might contribute to the spread of disinformation when they skip some stage of information processing and reproduce false or misleading information (Himma-Kadakas). Besides, even when the purpose of the news is to correct disinformation, media coverage might contribute to its dissemination by amplifying it (Tsfati et al.). This could be particularly problematic in the context of social media, as users often just read headlines while scrolling through their timelines (Newman et al.; Ofcom). Thus, some users might share news from the mainstream media to legitimate disinformation about Covid-19. The pandemic creates a delicate context, as journalists are often pressured to produce more information and, therefore, are more susceptible to errors

#VACHINA: How Politicians Help to Spread Disinformation About COVID-19 Vaccines

This paper focuses on how Brazilian politicians helped to spread disinformation about Covid-19 vaccines, discussing legitimation strategies and actors that played a significant role on Twitter and Facebook. Based on data gathered through CrowdTangle and Twitter API, we selected the 250 most shared/retweeted posts for each dataset (n=500) and examined if they contained disinformation, who posted it, and what strategy was used to legitimize this discourse. Our findings indicate that politicians and hyperpartisan accounts have a key influence in validating the Brazilian president’s populist discourse through rationalization (pseudo-science) and denunciation (against the vaccine). The political frame also plays an important role in disinformation messages

Hashtag Wars: Political Disinformation and Discursive Struggles on Twitter Conversations During the 2018 Brazilian Presidential Campaign

In this article, we analyze the spread of political disinformation in events of discursive struggles on Twitter, during the 2018 presidential election in Brazil. These were disputes for the hegemonic narrative between two stories based on opposed hashtags: one based on news from mainstream media and the other, based on disinformation, mostly from hyperpartisan outlets. Our goal was to understand how hyperpartisan outlets created and shaped these discursive struggles and the strategies used to spread disinformation to create an “alternative narrative” to the facts. Our case study is focused on two discursive struggles, for which we will use critical discourse analysis and social network analysis. Our findings suggest that (1) the structure of the hashtag wars was very polarized and right-wing groups had higher exposure to hyperpartisan content and disinformation, while traditional media discourse circulates more among other different ideological clusters; (2) right-wing hyperpartisan media mostly used biased framing and polarized ideological discourse structure as manipulative strategies to reframe the events and create a counter-narrative (and thus, to create the dispute); and (3) opinion leaders were major spreaders of disinformation among far-right users, as they reinforced hyperpartisan content and became key actors in the discursive struggles (and thus, reinforced the dispute)

“Made in China”: disinformation and Sinophobia on Facebook during the Covid-19 pandemic in Brazil

In this study, we analyze Facebook posts to explore the role of disinformation and Sinophobia in the context of the Covid-19 pandemic in Brazil. Although Sinophobic discourse is not new, it has been frequently associated with disinformation about Covid-19 in the country (Recuero; Soares, 2022). After collecting posts related to China from public groups on Facebook, we used Social Network Analysis to explore the networks and further analyze the most posted links. Connected Concept Analysis (CCA – Lindgren, 2016) was employed to examine the Facebook posts containing links that were posted at least 10 times from each cluster (n= 2,302 posts) and a qualitative deep reading was performed in order to make sense of the connections identified in the CCA. We identified three trends in the pro-Bolsonaro cluster: (1) blaming China for the pandemic, (2) reproducing conspiracy theories about China intentionally creating the virus, and (3) reinforcing a political framing of the pandemic (created by Communists). Sinophobic discourse was used to reinforce these claims. In addition to posts with overt Sinophobic discourse, other posts contained covert Sinophobia when blaming China for the pandemic

How coordinated link sharing behavior and partisans’ narrative framing fan the spread of COVID-19 misinformation and conspiracy theories

This study examines the presence and role of Coordinated Link Sharing Behavior (CLSB) on Facebook around the “America’s Frontline Doctors” press conference, and the promotion of several unproven conspiracy theories including the false assertion that hydroxychloroquine is a “cure” for COVID-19 by Dr. Stella Immanuel, one of the doctors who took part in the press conference. We collected 7,737 public Facebook posts mentioning Stella Immanuel using CrowdTangle and then applied the specialized program CooRnet to detect CLSB among Facebook public pages, groups and verified profiles. Finally, we used a mixed-method approach consisting of both network and content analysis to examine the nature and scope of the detected CLSB. Our analysis shows how Facebook accounts engaged in CLSB to fuel the spread of misinformation. We identified a coalition of Facebook accounts that engaged in CLSB to promote COVID-19 related misinformation. This coalition included US-based pro-Trump, QAnon, and anti-vaccination accounts. In addition, we identified Facebook accounts that engaged in CLSB in other countries, such as Brazil and France, that primarily promoted hydroxychloroquine, and some accounts in African countries that criticized the government's pandemic response in their countries

The disinformation discourse about COVID-19’s cure on Twitter: a case study

O presente artigo analisa como se deu a circulação de desinformação a respeito de uma possível “cura” para o coronavírus no Twitter brasileiro, em um período de dez dias do mês de março de 2020. Seu foco é na discussão (1) dos modos através dos quais os discursos relacionados à desinformação sobre supostas curas da pandemia foram espalhados, (2) os diferentes tipos de discurso desinformativo e sua prevalência, e (3) os modos de disputa contra os discursos que desmentem a desinformação. Através de um estudo de métodos mistos, com análise de conteúdo e análise de redes sociais de 57.295 tweets, observamos (1) o alinhamento do discurso da desinformação com o discurso político de apoio ao presidente da República, Jair Bolsonaro; (2) o espalhamento da desinformação associado à ação de influenciadores líderes de opinião notadamente alinhados à sua base de apoio; (3) o crescimento da circulação de desinformação a partir dos pronunciamentos do presidente; (4) a circulação de enquadramentos enganosos de informações verdadeiras como a estratégia-chave da disputa discursiva, buscando alinhar o discurso da “cura” com a desinformaçãodades na área. Evidenciamos que esses postulados são prevalentemente de matriz gestionária.This paper analyzes how the circulation of disinformation regarding a possible “cure” for coronavirus on Brazilian Twitter took place in a period of ten days in March 2020. Our focus is the discussion of (1) how discourses related to disinformation about alleged cures were spread, (2) the different types of disinformation discourse and their prevalence and (3) the discursive disputes against other discourses that deny this disinformation. Through a mixed methods study, using both content analysis and social network analysis of 57.295 tweets, we observed: (1) the alignment of the disinformation discourse with the political discourse of support of the President of the Republic, Jair Bolsonaro, mostly by the actions of opinion leaders aligned with his support base; (2) the growth of disinformation circulation based on the president’s speeches; (3) the circulation of misleading framing as the key disinformation strategy, seeking to connect the “cure” discourse with disinformation.Este artículo analiza cómo la circulación de desinformación sobre una posible “cura” para el coronavirus en Twitter brasileño tuvo lugar durante un período de diez días en marzo de 2020. Se centra en la discusión: (1) de las formas en que se difundieron discursos relacionados con la desinformación sobre supuestas curas, (2) los diferentes tipos de discurso de desinformación y su prevalencia y (3) los modos de disputa contra los discursos que niegan la desinformación. A través de un estudio de métodos mixtos, con análisis de contenido y análisis de redes sociales de 57,295 tweets, observamos (1) la centralidad del discurso de desinformación con el discurso político en apoyo del Presidente de la República, Jair Bolsonaro; (2) la difusión de información errónea asociada con la acción de los principales influyentes de opinión alineados notablemente con su base de apoyo; (3) el crecimiento de la circulación de desinformación basada en los pronunciamientos del presidente; (4) la circulación de marcos engañosos de información verdadera como la estrategia clave de la disputa discursiva, buscando alinear el discurso de “cura” con la desinformación

From Trolling to Cyberbullying: Using Machine Learning and Network Analysis to Study Anti-Social Behavior on Social Media

The rise of social media and other web and mobile applications has transformed how people interact, but it has also created new challenges, such as anti-social behavior like trolling, cyberbullying, and hatespeech. This behavior can have severe negative consequences for individuals and communities. This tutorial is intended for researchers and practitioners interested in computational social science and provides an overview of how to use machine learning and social network analysis techniques to detect and examine anti-social behavior in online discourse

Using Social Network Analysis and Social Capital to Identify User Roles on Polarized Political Conversations on Twitter

In this article, we discuss the roles users play in political conversations on Twitter. Our case study is based on data collected in three dates during the former Brazilian president Lula’s corruption trial. We used a combination of social network analysis metrics and social capital to identify the users’ roles during polarized discussions that took place in each of the dates analyzed. Our research identified four roles, each associated with different aspects of social capital and social network metrics: activists, news clippers, opinion leaders, and information influencers. These roles are particularly useful to understand how users’ actions on political conversations may influence the structure of information flows

Hyperpartisanship, Disinformation and Political Conversations on Twitter: The Brazilian Presidential Election of 2018

This paper examines the role of hyperpartisanship and polarization on Twitter during the 2018 Brazilian Presidential Election. Based on a mixed-methods approach, we collected and analyzed a dataset of over 8 million tweets about Jair Bolsonaro, a far-right candidate from the Social Liberty Party. Our results show that there is a strong connection between polarization, hyperpartisanship and disinformation. As the centrality of hyperpartisan outlets on Twitter grew, more traditional media outlets became less central and conversations became more polarized. We also confirmed that hyperpartisan outlets often shared disinformation or biased information, presented as a “truth-telling” alternative to journalistic outlets. And while disinformation was more frequently observed in the far-right group, it was also present in the anti-Bolsonaro cluster, especially towards the runoff period