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 used 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 curated 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 speech in political discussions. We developed 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 provided 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 three publicly available offensive datasets (OLID, HateUS2020, and FountaHate), generalizing to political discussions in other regions like the US.

翻译：尼日利亚人拥有显著的在线存在，并积极参与政治及热点话题的讨论。这在2023年大选期间尤为明显，推特被用于竞选宣传、事实核查与验证，甚至包括正面与负面言论。然而，针对尼日利亚语境下辱骂性语言和仇恨言论的检测工作却鲜有涉及或尚未开展。本文聚焦尼日利亚人口最多、经济最具活力的拉各斯州，收集了针对该州州长选举“三巨头”的语码转换推特数据，旨在检测政治讨论中的攻击性言论。我们构建了EkoHate数据集——一个面向三位候选人及其支持者之间政治讨论的辱骂性语言与仇恨言论数据集，采用了二元（正常vs攻击性）和细粒度四标签标注方案。我们对数据集进行了分析，并基于监督学习和跨语言迁移学习两种场景，对当前最优方法进行了实证评估。在监督学习场景下，我们的二标签和四标签标注方案分别取得了95.1和70.3的F1分数。此外，我们证明该数据集能够良好地迁移至三个公开的攻击性数据集（OLID、HateUS2020和FountaHate），并泛化到美国等其他地区的政治讨论中。