O que torna uma frase tóxica? Uma análise crítica de modelos especialistas em detecção de toxicidade - Melo, Gabriel; Figueredo, Flavio (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2025: Proceedings of the 31st Brazilian Symposium on Multimedia and the WebDO - 10.5753/webmedia.2025.15684, 2025)
This study examines the performance of specialized Machine Learning models in the task of online toxicity detection, under the hypothesis that these systems disproportionately focus on isolated lexical items. We conduct a comparative analysis of the Detoxify and Perspective API models using the HateXplain (English) and ToLDE-Br (Portuguese) datasets. To assess model behavior, we employ the SHAP explainability framework, which enables the interpretation of feature importance in individual predictions. Our findings reveal a misalignment between model outputs and the nuanced, evolving nature of language on social media platforms. Furthermore, the results demonstrate an overreliance on negatively connoted keywords, which compromises the models’ classification accuracy and raises concerns regarding their robustness and fairness in real-world applications.