HERDPhobia: A Dataset for Hate Speech against Fulani in Nigeria
This addresses hate speech detection for the Fulani community in Nigeria, but it is incremental as it applies existing methods to a new dataset.
The paper tackles the problem of hate speech against the Fulani ethnic group in Nigeria by introducing HERDPhobia, the first annotated hate speech dataset in English, Nigerian-Pidgin, and Hausa, and reports that the XML-T model achieved 99.83% weighted F1 in classification experiments.
Social media platforms allow users to freely share their opinions about issues or anything they feel like. However, they also make it easier to spread hate and abusive content. The Fulani ethnic group has been the victim of this unfortunate phenomenon. This paper introduces the HERDPhobia - the first annotated hate speech dataset on Fulani herders in Nigeria - in three languages: English, Nigerian-Pidgin, and Hausa. We present a benchmark experiment using pre-trained languages models to classify the tweets as either hateful or non-hateful. Our experiment shows that the XML-T model provides better performance with 99.83% weighted F1. We released the dataset at https://github.com/hausanlp/HERDPhobia for further research.