Context Matters, Bias Matters Too: Assessing Fairness in Profile Embeddings in the HateBR Dataset - Moitinho, Vinícius (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd.2026.249257, 2026)
Offensive language detection is challenged by discourse’s contextual nature, especially in Brazilian political contexts. This work examines how domain-specific information affects classifying offensiveness toward public figures, using the HateBR corpus. We compare three BERTimbau-based approaches: a domain-agnostic baseline, a model with textual metadata injection, and a hybrid with latent profile embeddings. Results show profile embeddings outperform other approaches in accuracy and convergence, indicating the model learns target-specific patterns. However, fairness analysis reveals a trade-off: specialization increases False Positive Rate variance across profiles, suggesting the model absorbs dataset biases. Findings reinforce the need for models integrating equity and context sensitivity in online debate.