Hate Speech Detection Using Machine Learning and Deep Learning Techniques

This paper delves into the pressing issue of hate speech in the digital era, which undermines inclusive online conversations. It investigates various methods for detecting hate speech, utilizing both conventional machine learning techniques and state-of-the-art deep learning architectures. The study focuses on deep learning models such as Convolutional Neural Networks, Recurrent Neural Networks, and Transformers, assessing their effectiveness in identifying textual patterns. Additionally, the practicality of machine learning algorithms, including ensemble methods, is examined. To ensure fairness, class inequality and ethical considerations are thoroughly taken into account when evaluating the efficiency of hate speech detection systems. Furthermore, the report addresses emerging challenges like context-dependent hate speech and evolving linguistic patterns. It places great importance on the ongoing necessity for research efforts and moral obligations to combat hate speech on online platforms.