Challenger at MultiPRIDE: Is It Hate Speech or Reclaimed?

2026-05-31

AI Summary

This work addresses the challenge of distinguishing hate speech from reclaimed language used by marginalized communities on social media—a task complicated by strong context dependence, severe class imbalance, and substantial label noise. The authors propose a concise and interpretable approach that first generates dense semantic text embeddings, then employs Cleanlab to filter noisy labels, followed by classification using logistic regression and multilayer perceptrons. By integrating these components, the method effectively mitigates the adverse effects of extreme data imbalance and annotation errors while preserving model interpretability. Evaluated on the MultiPRIDE shared task, the approach demonstrates robust performance, achieving notably high macro-averaged F1 scores, precision, and recall, thereby offering a novel and effective strategy for identifying reclaimed language in online discourse.

Abstract

The spread of hate speech has become increasingly harmful in modern digital environments, particularly on social networking platforms. While recent advances have shown promising results in automatic hate speech detection, a key challenge remains: distinguishing genuine hate speech from reclaimed language. Accurate labeling is difficult due to the nuanced and context-dependent nature of reclaimed expressions. In this paper, we present a simple and interpretable approach for distinguishing hate speech from reclaimed language, developed for the MultiPride Shared Task. Our method generates dense semantic text embeddings and incorporates a label-noise filtering stage using Cleanlab with logistic regression, followed by a Multi-layer Perceptron (MLP) neural network for final classification. The system is designed to operate under limited computational resources while maintaining strong performance. We evaluate our approach using precision, recall, and F1-score, including macro-averaged metrics. Experimental results demonstrate robust performance despite extreme class imbalance in the dataset. Overall, the findings highlight the potential for further improvements through larger embedding models and more advanced preprocessing techniques while preserving interpretability.

Problem

• hate speech
• reclaimed language
• context-dependent
• automatic detection
• labeling ambiguity

Innovation

• hate speech detection
• reclaimed language
• label-noise filtering
• interpretable AI
• text embeddings