BiaSWE: An Expert Annotated Dataset for Misogyny Detection in Swedish
This addresses the problem of bias detection in low-resource languages like Swedish for researchers in NLP and social sciences, but it is incremental as it applies existing annotation methods to a new language context.
The researchers tackled the lack of culturally relevant data for misogyny detection in Swedish by creating BiaSWE, an expert-annotated dataset, which is publicly available for further research.
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.