Deep learning techniques for hate speech detection

• Main Author: Teng, Yen Fong
• Other Authors: Luu Anh Tuan
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Computer and Information Science; Engineering; Machine learning; Large language model; Text classification; NLP
• Online Access: https://hdl.handle.net/10356/181151
• Institution: Nanyang Technological University

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. In conclusion, this comprehensive study undertakes a rigorous evaluation of existing deep learning techniques, specifically focusing on their application in hate speech classification. By systematically analysing the strengths, weaknesses, and limitations of various models, this research aims to advance our understanding of these technologies. The findings of this study will facilitate the creation of a vibrant, open-source community where researchers and developers can collaboratively contribute to the development of more sophisticated, accurate, and efficient hate speech detection systems. Ultimately, this research aims to promote a safer, more secure, and inclusive online environment, protecting netizens from the harmful effects of hate speech and fostering a culture of respect, empathy, and free expression.