eversvd,+5 (15)

This review paper discusses the critical issue of hate speech detection in social media, where the proliferation of hateful communications can have severe consequences for individuals and groups, including mental health challenges and even self-harm. The objective of this research is to highlight the necessity of automating the identification of hate speech through advanced machine learning and deep learning methodologies. It explores various studies and techniques employed in detecting hate speech across different text contexts. The methodology involves a comprehensive review of existing literature pertaining to detection techniques such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Multilayer Perceptron (MLP), Multiple Kernel Clustering (MKC), and Lagrangian Support Vector Machines (LSVM). The findings illustrate that effective hate speech detection can significantly contribute to healthier social media environments by mitigating the risks associated with hate speech. This review emphasizes the importance of continuous research to refine detection methodologies and enhance the overall safety of all users on social platforms.