An Explainable DistilBERT-BiLSTM-Attention Framework for Binary and Multi-Class Hate Speech Detection
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Abstract
This study proposes a multilevel and explainable hate speech detection framework that integrates DistilBERT embeddings with a Bi-LSTM model and an attention mechanism to enhance detection accuracy and explainability.
Reality Card
The proposed model achieves F1-scores of 96.78% and 99.53% for binary classification on the Davidson and SMHS datasets, respectively, and outperforms existing baseline approaches in both binary and multi-class settings.
F1-scores of 96.78% on the Davidson dataset and 99.53% on the SMHS dataset for binary classification.
Limited insight into decision-making processes in existing studies may affect real-world applicability.
Paper to code
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