A Multi-Corpus Benchmark of Classical Fake News Classifiers with Contextual Portuguese Embeddings

• Adriel L. V. Mori UFG / AKCIT
• Eliomar Araújo Lima UFG / AKCIT
• Valdemar V. Graciano Neto UFG / AKCIT
• Jacson R. Barbosa UFG / AKCIT
• Rogerio Rodrigues Carvalho UFG / AKCIT
• Arlindo R. Galvão Filho UFG / AKCIT

DOI: https://doi.org/10.5753/wics.2026.23947

Resumo

This paper presents a systematic benchmark for fake news detection in Brazilian Portuguese, combining multiple datasets, four contextual embedding models, and eight classical supervised classifiers under a unified evaluation protocol. The results show that, when a strong classifier such as SVC-RBF is adopted, performance varies more across embedding models than across classifiers. Among the evaluated encoders, albertina_ptbr_900m achieved the best overall generalization on the test set, while bertimbau_large showed the strongest validation performance. Overall, the study highlights representation quality as a central factor for robust classical fake news classification in Brazilian Portuguese.

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