Escola de Atenas, de Rafael
Publicaçoes
Bolsonaro and Lula’s Foreign Affairs and Cultural Discourse surrounding the Globalism versus Globalization dichotomy
Abstract: This research explores the foreign affairs narratives articulated by Bolsonaro and Lula, focusing on their distinct approaches to the debate surrounding globalism and globalization. This dichotomy is contextualized within Olavo de Carvalho's theoretical framework, which posits a 'cultural war' and propagates the notion of 'cultural Marxism' as a conspiratorial force. By analyzing social media content from their official profiles and communications from the Brazilian Ministry of Foreign Affairs during their respective administrations, this study illuminates how these leaders' foreign affairs discourses contribute to the broader phenomena of populism and polarization in Brazil. Bolsonaro's rhetoric is marked by its alignment with Carvalho's portrayal of “globalism” as an existential threat to national sovereignty and traditional cultural values, while Lula’s embrace aspects of globalization, emphasizing its potential for fostering economic growth and advocating for global social equity. This comparative analysis reveals the underlying themes and rhetorical strategies employed by each leader, demonstrating how their perspectives on globalism and globalization are intricately woven into larger populist narratives and actively shape Brazil's public discourse and perceptions of foreign affairs, aiming to deepen the understanding of the relationship between political leadership, international relations discourse, and the dynamics of cultural wars, populism, and polarization in Brazilian politics. Authors:Isabela Rocha, Instituto de Ciência Política da Universidade de Brasília (IPOL-UnB), Joscimar Souza Silva, Instituto de Ciência Política da Universidade de Brasília (IPOL-UnB) Keywords: Political discourse, Digital Social Media, Topological Data Analysis
International Political Science Association
Digital Identities and Constellation Narratives: A Case Study on Brazil's 2022 Electoral Campaign
This article develops a model of analysis of political discourse in Digital Social Media by analyzing Twitter data using Topological Data Analysis (TDA) articulating Robert Shiller’s (2019) propositions for Narrative Economics. The model is tested on a database of tweets collected throughout the timespan of Brazil’s 2022 electoral campaign to illuminate influential digital identities at the center of political discussion and how they shape political narratives within their own socio-digital spheres. Constellations detecting the clusters of communities engaged in political debate were drawn from retweets and organized into a topological space, highlighting both profiles and their retweeting patterns. This method allows for a better understanding of how digital identities and their narratives may influence politics. Through these constellations, a process of political polarization with little-to-no space for alternative political figures has been observed, which allows for a comparison between Bolsonaro and Lula's supporters. The findings contribute firstly to the broader field of Narrative Economics, which explores the ways in which stories and narratives shape economic - and in this case, political - decision-making, highlighting the growing importance of digital narratives in shaping political outcomes. Most importantly, however, the main contribution lies in the application of TDA for the broader field of political science and discourse analysis. Keywords: Political discourse, Digital Social Media, Topological Data Analysis
International Political Science Association
Catalytic Conspiracism: Exploring Persistent Homologies time series in the dissemination of disinformation in conspiracy theory communities on Telegram
Escrito com Ergon Cugler de Moraes Silva (IBICT) e Renata Vicentini Mielli (CGI, PPGCOM-ECA-USP) This study presents a methodological approach for the understanding of the dissemination of disinformation in digital communities through Topological Data Analysis (TDA)’s concept of Persistent Homology (Carlsson, 2020), aiming to identify patterns and underlying structures in complex temporal data. For that end, a computational model that integrates TDA with time series analysis through the filtering of datasets into Topological Constellations was developed, allowing for the detection of Persistent Homologies in the spread of false information. The proposed methodology is applied to a real database of around one million publications in several conspiracy theory communities on Telegram, demonstrating its effectiveness in identifying patterns of disinformation dissemination while also highlighting the shared false content, allowing a holistic analysis that integrates content analysis to advanced models of data analysis. The results obtained highlight Persistent Homologies in the dynamics of digital communication as well as the nature of the shared content, offering new perspectives on the mechanisms of disinformation propagation. This study contributes to the field of data science applied to political communication, providing tools for researchers and professionals interested in combating the spread of disinformation in online networks.
Anais do 14º Encontro da ABCP
Beyond Network Analysis: Identifying Topological Data Analysis’ Persistent Homologies on Social Media processes of Political Personalism and Polarization
This study aims to contrast the application of Topological Data Analysis (TDA) to the more consolidated Network Analysis, emphasizing the merits of the identification of TDA’s Persistent Homologies (Carlsson, 2020) in complex data to identify processes of political personalism and polarization in Social Media. In contrast to Network Analysis, TDA delves into the study of the shapes formed by data, considering not just the individual elements and their connections, but also the overarching geometric and topological properties that emerge from these relationships. For that end, six selected datasets from a database of 2 million tweets were charted into bidimensional spaces, first through the user’s retweeting patterns and then through the k-Nearest-Neighbor (kNN) application. Both filtrations are studied through a topological lens, and from the shapes identified, three categories of Persistent Homologies have been created: Nuclear Constellations, Bipolar Constellations and Multipolar Constellations, each indicating distinct political processes. This article contributes to the field of Computational Social Sciences by introducing the study of the topological characteristics into political-driven data, and an important finding is that there is a direct relation between the underlying topological structure of a filtered dataset – the Constellations – and political phenomena, particularly, in the context of the Brazilian 2022 elections, political personalism and polarization.
Anais do 14º Encontro da ABCP
Publicaçoes
Towards Asimov's Psychohistory: Harnessing Topological Data Analysis, Artificial Intelligence and Social Media data to Forecast Societal Trends
In the age of big data and advanced computational methods, the prediction of large-scale social behaviors, reminiscent of Isaac Asimov's fictional science of Psychohistory, is becoming increasingly feasible. This paper consists of a theoretical exploration of the integration of computational power and mathematical frameworks, particularly through Topological Data Analysis (TDA) (Carlsson, Vejdemo-Johansson, 2022) and Artificial Intelligence (AI), to forecast societal trends through social media data analysis. By examining social media as a reflective surface of collective human behavior through the systematic behaviorist approach (Glenn, et al., 2016), I argue that these tools provide unprecedented clarity into the dynamics of large communities. This study dialogues with Asimov's work, drawing parallels between his visionary concepts and contemporary methodologies, illustrating how modern computational techniques can uncover patterns and predict shifts in social behavior, contributing to the emerging field of digital sociology -- or even, Psychohistory itself. Keywords: Topological Data Analysis, Persistent Homology, Social Media Network Analysis, Political Communication Patterns, Mathematical Generalizations in Data Science.
Persistent Homology Generalizations for Social Media Network Analysis
This study details an approach for the analysis of social media collected political data through the lens of Topological Data Analysis, with a specific focus on Persistent Homology and the political processes they represent by proposing a set of mathematical generalizations using Gaussian functions to define and analyze these Persistent Homology categories. Three distinct types of Persistent Homologies were recurrent across datasets that had been plotted through retweeting patterns and analyzed through the k-Nearest-Neighbor filtrations. As these Persistent Homologies continued to appear, they were then categorized and dubbed Nuclear, Bipolar, and Multipolar Constellations. Upon investigating the content of these plotted tweets, specific patterns of interaction and political information dissemination were identified, namely Political Personalism and Political Polarization. Through clustering and application of Gaussian density functions, I have mathematically characterized each category, encapsulating their distinctive topological features. The mathematical generalizations of Bipolar, Nuclear, and Multipolar Constellations developed in this study are designed to inspire other political science digital media researchers to utilize these categories as to identify Persistent Homology in datasets derived from various social media platforms, suggesting the broader hypothesis that such structures are bound to be present on political scraped data regardless of the social media it's derived from. This method aims to offer a new perspective in Network Analysis as it allows for an exploration of the underlying shape of the networks formed by retweeting patterns, enhancing the understanding of digital interactions within the sphere of Computational Social Sciences. Keywords: Topological Data Analysis, Persistent Homology, Social Media Network Analysis, Political Communication Patterns, Mathematical Generalizations in Data Science.