Language Models and Detection Strategies for Political Disinformation in Brazil: A Comparative Applied Study - Mori, Adriel L. V.; Santos, Willgnner Ferreira; Lima, Eliomar Araújo; Graciano-Neto, Valdemar V.; Barbosa, Jacson R.; Carvalho, Rogerio R.; da Silva, Nádia F. F.; Galvão Filho, Arlindo R. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
The rapid spread of online political disinformation has increased the need for scalable detection beyond manual fact-checking. This study evaluates textual disinformation detection in Brazilian Portuguese by comparing BERTimbau-based supervised models with open-source LLMs under zero-shot, Chain-of-Thought, and RAG settings. Four public corpora were used for training, validation, and retrieval, while an external political dataset supported out-of-distribution testing. Results show that gemma2:9b in zero-shot achieved the best macro-F 1 (0.950), outperforming BERTimbau+SVM (0.837). BM25-based RAG reached macro-F 1 = 0.923 with grounded decisions. Overall, performance depends on inference strategy, architecture, and corpus characteristics.