EkoHate: Abusive Language and Hate Speech Detection for Code-switched Political Discussions on Nigerian Twitter
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.
Code (1)
Tasks
Abusive LanguageCross-Lingual TransferFact CheckingHate Speech DetectionTransfer LearningSimilar Papers 제목 키워드 기반
L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language
Hate speech and abusive language have become a common phenomenon on Arabic social media. Automatic hate speech and abusive detection systems can facilitate the prohibition of toxic textual contents. The complexity, infor…
Abusive LanguageHate Speech DetectionMulti-label Hate Speech and Abusive Language Detection in Indonesian Twitter
Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen. Moreover, hate speech has a target, category, and level that also needs to be detected to he…
Abuse DetectionAbusive LanguageHate Speech DetectionMulti Label Text Classification+3HateBERT: Retraining BERT for Abusive Language Detection in English
In this paper, we introduce HateBERT, a re-trained BERT model for abusive language detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit comments in English from communities banned for bei…
Abusive LanguageHate Speech DetectionLanguage ModelingLanguage ModellingIntroducing an Abusive Language Classification Framework for Telegram to Investigate the German Hater Community
Since traditional social media platforms continue to ban actors spreading hate speech or other forms of abusive languages (a process known as deplatforming), these actors migrate to alternative platforms that do not mode…
Abusive LanguageClassificationHate Speech DetectionCode-Mixed Telugu-English Hate Speech Detection
Hate speech detection in low-resource languages like Telugu is a growing challenge in NLP. This study investigates transformer-based models, including TeluguHateBERT, HateBERT, DeBERTa, Muril, IndicBERT, Roberta, and Hin…
Hate Speech DetectionMultilingual NLPTranslation