HERDPhobia: A Dataset for Hate Speech against Fulani in Nigeria

Autor:	Aliyu, Saminu Mohammad, Wajiga, Gregory Maksha, Murtala, Muhammad, Muhammad, Shamsuddeen Hassan, Abdulmumin, Idris, Ahmad, Ibrahim Said
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: To appear in the Proceedings of the Sixth Workshop on Widening Natural Language Processing at EMNLP2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.15262 Zobrazit plný text záznamu View this record from Arxiv