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Closest positive cluster loss: improving the generalization of implicit hate speech classifiers across social media datasets

Almohaimeed, Saad; Almohaimeed, Saleh; Turgut, Damla; Boloni, Ladislau (June 2025, IEEE)

Free, publicly-accessible full text available June 6, 2026
Transfer Learning and Lexicon-Based Approaches for Implicit Hate Speech Detection: A Comparative Study of Human and GPT-4 Annotation

https://doi.org/10.1109/ICSC59802.2024.00028

Almohaimeed, Saad; Almohaimeed, Saleh; Bölöni, Ladislau (February 2024, IEEE)

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THOS: A Benchmark Dataset for Targeted Hate and Offensive Speech

Almohaimeed, S; Almohaimeed, S; Shafin, A A; Carbunar, B; Bölöni, L (July 2023, Proc. of Data-centric Machine Learning Research (DMLR) Workshop at ICML 2023)

Detecting harmful content on social media, such as Twitter, is made difficult by the fact that the seemingly simple yes/no classification conceals a significant amount of complexity. Unfortunately, while several datasets have been collected for training classifiers in hate and offensive speech, there is a scarcity of datasets labeled with a finer granularity of target classes and specific targets. In this paper, we introduce THOS, a dataset of 8.3k tweets manually labeled with fine-grained annotations about the target of the message. We demonstrate that this dataset makes it feasible to train classifiers, based on Large Language Models, to perform classification at this level of granularity.
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