EkoHate: Offensive and Hate Speech Detection for Code-switched Political discussions on Nigerian Twitter

Comfort Eseohen Ilevbare, Jesujoba Oluwadara Alabi, David Ifeoluwa Adelani, Bakare Firdous Damilola, Abiola Oluwatoyin Bunmi, ADEYEMO Oluwaseyi Adesina

Keywords: Hate Speech, Code-switching, Offensive and Hate Speech Detection, Nigerian Twitter, Lagos election

Abstract: Nigerians have a notable online presence and actively discuss political and topical matters. This was particularly evident throughout the 2023 general election, where Twitter was utilized for campaigning, fact-checking and verification, and even positive and negative discourse. However, little or none has been done in the detection of abusive language and hate speech in Nigeria. In this paper, we curate code-switched Twitter data directed at three musketeers of the governorship election on the most populous and economically vibrant state in Nigeria; Lagos state, with the view to detect offensive and hate speech on political discussion. We develop EkoHate---an abusive language and hate speech dataset for political discussions between the three candidates and their followers using a binary (normal vs offensive) and fine-grained four-label annotation scheme. We analysed our dataset and provide an empirical evaluation of state-of-the-art methods across both supervised and cross-lingual transfer learning settings. In the supervised setting, our evaluation results in both binary and four-label annotation schemes show that we can achieve 95.1 and 70.3 F1 points respectively. Furthermore, we show that our dataset adequately transfers very well to two publicly available offensive datasets (OLID and HateUS2020) with at least 62.7 F1 points.

Submission Number: 23