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)
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.