Mapping the Political Discourse in the Brazilian Chamber of Deputies: A Multi-Faceted Computational Approach

2026-04-23

AI Summary

This study addresses the limitations of traditional legislative behavior research, which predominantly relies on voting records while overlooking the rich semantic and rhetorical content embedded in parliamentary speeches. To bridge this gap, the authors propose a scalable, general-purpose multidimensional computational framework that integrates diachronic stylometric analysis, contextual topic modeling, and semantic clustering to systematically examine the expressive styles, issue evolution, and discursive similarities in speeches delivered by members of the Brazilian Chamber of Deputies. The analysis reveals that legislators’ speeches have become increasingly concise and direct over time, that legislative agendas shift markedly in response to national crises, and that discursive alliances are more strongly shaped by regional and gender identities than by formal party affiliations. This framework offers a novel paradigm for understanding the dynamics of political discourse.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Application Domains: Humanities & Computational Social Science
• Knowledge Representation and Reasoning: Argumentation

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems

Abstract

Analyses of legislative behavior often rely on voting records, overlooking the rich semantic and rhetorical content of political speech. In this paper, we ask three complementary questions about parliamentary discourse: how things are said, what is being said, and who is speaking in discursively similar ways. To answer these questions, we introduce a scalable and generalizable computational framework that combines diachronic stylometric analysis, contextual topic modeling, and semantic clustering of deputies' speeches. We apply this framework to a large-scale case study of the Brazilian Chamber of Deputies, using a corpus of over 450,000 speeches from 2003 to 2025. Our results show a long-term stylistic shift toward shorter and more direct speeches, a legislative agenda that reorients sharply in response to national crises, and a granular map of discursive alignments in which regional and gender identities often prove more salient than formal party affiliation. More broadly, this work offers a robust methodology for analyzing parliamentary discourse as a multidimensional phenomenon that complements traditional vote-based approaches.

Problem

• parliamentary discourse
• legislative behavior
• political speech
• semantic content
• rhetorical analysis

Innovation

• diachronic stylometric analysis
• contextual topic modeling
• semantic clustering
• parliamentary discourse
• computational framework