Detection and Analysis of Offensive Online Content in Hausa Language

TL;DR

This paper addresses the challenge of detecting offensive online content in Hausa, a low-resource language, by creating a new dataset and developing detection systems that outperform baseline models, emphasizing cultural and linguistic nuances.

Contribution

The study introduces the first offensive term dataset for Hausa and develops detection models that better capture linguistic nuances compared to multilingual baselines.

Findings

Detection system identified over 70% of offensive content.

Baseline models often mistranslated offensive terms.

Offensive language is prevalent in discussions on religion and politics.

Abstract

Hausa, a major Chadic language spoken by over 100 million people mostly in West Africa is considered a low-resource language from a computational linguistic perspective. This classification indicates a scarcity of linguistic resources and tools necessary for handling various natural language processing (NLP) tasks, including the detection of offensive content. To address this gap, we conducted two set of studies (1) a user study (n=101) to explore cyberbullying in Hausa and (2) an empirical study that led to the creation of the first dataset of offensive terms in the Hausa language. We developed detection systems trained on this dataset and compared their performance against relevant multilingual models, including Google Translate. Our detection system successfully identified over 70% of offensive, whereas baseline models frequently mistranslated such terms. We attribute this discrepancy to the nuanced nature of the Hausa language and the reliance of baseline models on direct or literal translation due to limited data to build purposive detection systems. These findings highlight the importance of incorporating cultural context and linguistic nuances when developing NLP models for low-resource languages such as Hausa. A post hoc analysis further revealed that offensive language is particularly prevalent in discussions related to religion and politics. To foster a safer online environment, we recommend involving diverse stakeholders with expertise in local contexts and demographics. Their insights will be crucial in developing more accurate detection systems and targeted moderation strategies that align with cultural sensitivities.