Deep learning techniques for hate speech detection - Teng, Yen Fong (Nanyang Technological University, 2024)
The rampant spread of hate speech on social media poses severe consequences for social cohesion and individual well-being. To tackle this problem, we need accurate and efficient detection methods which are vital to address this growing concern. This comprehensive review aims to examine the effectiveness of deep learning-based approaches for hate speech detection, encompassing traditional classifiers, such as Logistic Classifiers, Support Vector Classifier, followed by Deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and state-of-the-art Natural Language Processing (NLP) techniques, including BERT and Large Language Models. A systematic evaluation of these models will be conducted, assessing their performance using metrics such as precision, recall, F1-score. This provides insights into the strengths and limitations of existing methods, identifying avenues for future research and contributes to the development of more effective hate speech detection systems.