Towards safer online communities: Deep learning and explainable AI for hate speech detection and classification

The internet and social media facilitate widespread idea sharing but also contribute to cybercrimes and harmful behaviors, notably the dissemination of abusive and hateful speech, which poses a significant threat to societal cohesion. Hence, prompt and accurate detection of such harmful content is crucial. To address this issue, our study introduces a fully automated end-toend model for hate speech detection and classification using Natural Language Processing and Deep Learning techniques. The proposed architecture comprising embedding, Convolutional, bidirectional Recurrent Neural Network, and bidirectional Long Short Term Memory layers, achieved the highest accuracy of 98.5%. Additionally, we employ explainable AI techniques, such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), to gain insights into the performance of the proposed framework. This comprehensive approach meets the pressing demand for swift and precise detection and categorization of harmful online content.