@inproceedings{moreira-etal-2026-anatomy,
    title = "Anatomy of Data Repositories for the Analysis and Detection of Toxicity in {P}ortuguese",
    author = "Moreira, Lorena Souza  and
      Gibrim, Paula Teresa M.  and
      Rocha, Leonardo  and
      Reis, Julio C. S.",
    editor = "Souza, Marlo  and
      de-Dios-Flores, Iria  and
      Santos, Diana  and
      Freitas, Larissa  and
      Souza, Jackson Wilke da Cruz  and
      Ribeiro, Eug{\'e}nio",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
    month = apr,
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-dnd/2026.propor-1.45/",
    pages = "456--466",
    ISBN = "979-8-89176-387-6",
    abstract = "The proliferation of online hate speech requires a rigorous examination of the datasets used to train detection models. In this work, we analyze six Brazilian Portuguese datasets annotated for hate speech or toxicity to investigate how their lexical ``anatomy'' and domain characteristics affect cross-domain generalization. We combine HurtLex-based lexical profiling with cross-dataset evaluation in a feature-based transfer-learning setup, using BERTimbau embeddings and an XGBoost classifier. Our analysis shows that, although the datasets share a broadly similar macro-level focus, they diverge substantially in how specific terms are used and labeled across platforms and topics. Results indicate that lexical breadth and annotation practices strongly predict transferability: datasets with broader and more heterogeneous toxic vocabulary yield better cross-domain performance, whereas resources with narrow, profanity-centered labeling lead to severe generalization gaps, even when lexical overlap is high. These findings underscore the impact of collection and labeling strategies on the curation and evaluation of Portuguese hate speech datasets. Warning! This work and the referenced datasets contain examples of offensive and hateful language."
}