United Against Hate: A Deep Learning Ensemble Approach for Robust Hate Speech and Offensive Language Detection

The rise of hate speech on social media has major consequences for people, communities, and society. To combat this issue, we propose United Against Hate, a deep learning ensemble approach that leverages the strengths of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to detect hate speech and offensive language. Our method achieves state-of-the-art performance on a benchmark dataset, demonstrating significant improvements over existing work. Specifically, our approach yields a validation accuracy of 0.8802, F1-score of 0.8805, precision of 0.8816, and recall of 0.879, outperforming previous work by 1.4% in accuracy, 1.5% in F1-score, 1.6% in precision, and 1.4% in recall. By showcasing the potential of deep learning ensembles, our research contributes to the development of improved solutions for the detection and mitigation of the spread of hate speech over the internet.