CoDEl-BR: An Electoral Debate Corpus from Brazilian Municipal Elections

2026-04-12 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Social Sciences & Behavioral Studies · Depth: Expert, quick

Summary

CoDEl-BR (Corpus de Debates Eleitorais) is a new corpus of transcripts from 22 second-round mayoral debates held in 13 Brazilian state capitals during the 2024 municipal elections. This corpus, presented by Alessandra Gomes, Aline Paes, and Helena Caseli, contains 2,943 transcript segments, totaling approximately 32 hours of content. Initial exploratory analyses of CoDEl-BR reveal distinct thematic priorities between candidates and voter questions, alongside variations influenced by race and party affiliation. The primary goal of this curated, high-quality dataset is to support research in areas such as discourse and argumentation analysis, stance and sentiment detection, and polarization modeling within natural language processing tasks, with significant potential for future expansion.

Key takeaway

For NLP researchers and computational social scientists studying political discourse, CoDEl-BR provides a valuable, high-quality dataset for analyzing Brazilian municipal elections. Your work can leverage this corpus to explore nuanced linguistic choices, thematic shifts, and the impact of race and party affiliation on debate content. Consider integrating CoDEl-BR into projects focused on argumentation analysis or polarization modeling to gain deeper insights into electoral dynamics.

Key insights

CoDEl-BR offers a high-quality corpus of Brazilian electoral debates for NLP research.

Principles

• Electoral debates reflect regional and ideological variations.
• Corpus analysis can reveal thematic differences by candidate and voter.

Method

The CoDEl-BR corpus was constructed from 22 second-round mayoral debate transcripts from 13 Brazilian state capitals during the 2024 municipal elections.

In practice

• Use CoDEl-BR for stance and sentiment detection.
• Apply CoDEl-BR to model political polarization.

Topics

• CoDEl-BR Corpus
• Brazilian Municipal Elections
• Electoral Debates
• Discourse Analysis
• Polarization Modeling

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.