Dataset Creation and Baseline Models for Sexism Detection in Hausa

TL;DR

This paper introduces the first Hausa sexism detection dataset, explores cultural and linguistic nuances through user studies, and evaluates machine learning models, highlighting challenges in capturing cultural context and reducing false positives.

Contribution

It presents the first Hausa sexism dataset, incorporates community insights, and assesses baseline models including few-shot learning for low-resource language sexism detection.

Findings

Challenges in capturing cultural nuances like idiomatic expressions.

Pre-trained models show promise but struggle with false positives.

Community engagement improves dataset relevance.

Abstract

Sexism reinforces gender inequality and social exclusion by perpetuating stereotypes, bias, and discriminatory norms. Noting how online platforms enable various forms of sexism to thrive, there is a growing need for effective sexism detection and mitigation strategies. While computational approaches to sexism detection are widespread in high-resource languages, progress remains limited in low-resource languages where limited linguistic resources and cultural differences affect how sexism is expressed and perceived. This study introduces the first Hausa sexism detection dataset, developed through community engagement, qualitative coding, and data augmentation. For cultural nuances and linguistic representation, we conducted a two-stage user study (n=66) involving native speakers to explore how sexism is defined and articulated in everyday discourse. We further experiment with both traditional machine learning classifiers and pre-trained multilingual language models and evaluating the effectiveness few-shot learning in detecting sexism in Hausa. Our findings highlight challenges in capturing cultural nuance, particularly with clarification-seeking and idiomatic expressions, and reveal a tendency for many false positives in such cases.