Implicit hate speech detection is challenging due to its subjectivity and context dependence, with existing models often struggling in outof-domain scenarios. We propose CONELA, a novel data refinement strategy that enhances model performance and generalization by integrating human annotation agreement with model training dynamics. By removing both easy and hard instances from the model’s perspective, while also considering whether humans agree or disagree and retaining ambiguous cases crucial for out-of-distribution generalization, CONELA consistently improves performance across multiple datasets and models. We also observe significant improvements in F1 scores and cross-domain generalization with the use of our CONELA strategy. Addressing data scarcity in smaller datasets, we introduce a weighted loss function and an ensemble strategy incorporating disagreement maximization, effectively balancing learning from limited data. Our findings demonstrate that refining datasets by integrating both model and human perspectives significantly enhances the effectiveness and generalization of implicit hate speech detection models. This approach lays a strong foundation for future research on dataset refinement and model robustness.

Implicit hate speech detection is challenging due to its subtlety and reliance on contextual interpretation rather than explicit offensive words. Current approaches rely on contrastive learning, which are shown to be effective on distinguishing hate and non-hate sentences. Humans, however, detect implicit hate speech by first identifying specific targets within the text and subsequently interpreting how these target relate to their surrounding context. Motivated by this reasoning process, we propose AmpleHate, a novel approach designed to mirror human inference for implicit hate detection. AmpleHate identifies explicit target using a pretrained Named Entity Recognition model and capture implicit target information via [CLS] tokens. It computes attention-based relationships between explicit, implicit targets and sentence context and then, directly injects these relational vectors into the final sentence representation. This amplifies the critical signals of target-context relations for determining implicit hate. Experiments demonstrate that AmpleHate achieves state-of-the-art performance, outperforming contrastive learning baselines by an average of 82.14% and achieve faster convergence. Qualitative analyses further reveal that attention patterns produced by AmpleHate closely align with human judgement, underscoring its interpretability and robustness.

The ever-growing presence of hate speech on social network services and other online platforms not only fuels online harassment but also presents a growing challenge for hate speech detection. As this task is akin to binary classification, one of the promising approaches for hate speech detection is the utilization of contrastive learning. Recent studies suggest that classifying hateful posts in just a binary manner may not adequately address the nuanced task of detecting implicit hate speech. This challenge is largely due to the subtle nature and context dependency of such pejorative remarks. Previous studies proposed a modified contrastive learning approach equipped with additional aids such as human-written implications or machine-generated augmented data for better implicit hate speech detection. While this approach can potentially enhance the overall performance by its additional data in general, it runs the risk of overfitting as well as heightened cost and time to obtain. These drawbacks serve as motivation for us to design a methodology that is not dependent on human-written or machine-generated augmented data for training. We propose a straightforward, yet effective, clustering-based contrastive learning approach that leverages the shared semantics among the data.