AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages

TL;DR

This paper introduces AfriHate, a multilingual dataset of hate speech and abusive language in 15 African languages, created with native speaker annotations to improve understanding and moderation of such content in the region.

Contribution

The paper provides the first high-quality, culturally-aware hate speech datasets for 15 African languages, addressing data scarcity and moderation challenges.

Findings

Baseline classification results demonstrate the dataset's utility.

Including LLMs improves hate speech detection accuracy.

Challenges in dataset construction are discussed.

Abstract

Hate speech and abusive language are global phenomena that need socio-cultural background knowledge to be understood, identified, and moderated. However, in many regions of the Global South, there have been several documented occurrences of (1) absence of moderation and (2) censorship due to the reliance on keyword spotting out of context. Further, high-profile individuals have frequently been at the center of the moderation process, while large and targeted hate speech campaigns against minorities have been overlooked. These limitations are mainly due to the lack of high-quality data in the local languages and the failure to include local communities in the collection, annotation, and moderation processes. To address this issue, we present AfriHate: a multilingual collection of hate speech and abusive language datasets in 15 African languages. Each instance in AfriHate is annotated by native speakers familiar with the local culture. We report the challenges related to the construction of the datasets and present various classification baseline results with and without using LLMs. The datasets, individual annotations, and hate speech and offensive language lexicons are available on https://github.com/AfriHate/AfriHate