Exploring Textual Hate Speech Detection Methods and Datasets: A Comprehensive Literature Review

Although so-called hate speech is very common in the media, it poses major problems in terms of detection and control, given the density of the content and the difficulty of interpretation and context. This paper makes a critical review of key research into hate speech detection, bringing in innovative methodologies and some valuable insights. Progress toward enhancing the accuracy of detection. It is best to use advanced neural network architectures to detect speech discrimination in communication networks. Furthermore, research on spreading extremist content on platforms like Twitter and the psychological effect of hate speech also underscores the well-rounded nature of the problem and that more inter- disciplinary approaches should be taken. This review stresses the importance of precision, awareness of context, and transparency of algorithms in hate speech detection frameworks. Through Using leading-edge technologies and interdisciplinary expertise, researchers are attempting to develop resilient systems that can efficiently fight hate speech without compromising free expression and societal harmony.