BiaSWE: An Expert Annotated Dataset for Misogyny Detection in Swedish

TL;DR

This paper presents BiaSWE, a carefully annotated Swedish dataset for misogyny detection, developed through expert collaboration to ensure cultural relevance and linguistic accuracy for low-resource language bias detection.

Contribution

The paper introduces BiaSWE, a novel expert-annotated dataset for misogyny detection in Swedish, with a rigorous annotation process tailored to cultural and linguistic nuances.

Findings

Dataset is publicly available for research.

Annotation process ensures cultural and linguistic relevance.

Supports bias detection in low-resource languages.

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.