Deep learning techniques for hate speech detection - Chang, Timothy Zu'En (Nanyang Technological University, 2024)
With the rapid growth of the Internet and continuous expansion of online content, the proliferation of hate speech also increases. Hate speech has severe implications on social polarization, as well as physical and mental safety, warranting an urgent need for effective automated detection. In this study, we analyze current efforts by the scientific community in developing automated methods for detecting online hate speech. This led us to the discovery of machine learning-based approaches for automatic hate speech detection, in particular deep learning approaches which were popularized for their robustness and ability to learn newly evolving slang. In doing so, we also investigate the challenges faced, including language nuances, varying definitions of hate speech, and data constraints.