@inproceedings{kukk-etal-2025-biaswe,
    title = "{BiaSWE}: {An} Expert Annotated Dataset for Misogyny Detection in {Swedish}",
    author = {Kukk, K{\"a}triin  and
      Petrelli, Danila  and
      Casademont, Judit  and
      Orlowski, Eric J. W.  and
      Dzielinski, Michal  and
      Jacobson, Maria},
    editor = "Johansson, Richard  and
      Stymne, Sara",
    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.nodalida-1.33/",
    pages = "307--312",
    ISBN = "978-9908-53-109-0",
    abstract = "In this study, we introduce the process for creating BiaSWE, an expert-annotated dataset tailored for misogyny detection in the Swedish language. To address the cultural and linguistic specificity of misogyny in Swedish, we collaborated with experts from the social sciences and humanities. Our interdisciplinary team developed a rigorous annotation process, incorporating both domain knowledge and language expertise, to capture the nuances of misogyny in a Swedish context. This methodology ensures that the dataset is not only culturally relevant but also aligned with broader efforts in bias detection for low-resource languages. The dataset, along with the annotation guidelines, is publicly available for further research."
}