Advancements in Hate Speech Detection: A Comprehensive Analysis of NLP Models and Techniques

In today’s digitally connected world, the proliferation of hate speech and the amplifying of prejudices through online social networks are serious concerns. Expressions of hate, which target individuals or groups based on a range of qualities, pose major societal problems. In this study, we specifically focus on Twitter to identify hate speech in online social networks. We use a publicly available dataset on Hugging Face. We examine state-of-the-art techniques for identifying offensive speech, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Bidirectional Long Short-Term Memory networks (BiLSTMs), XGBoost, and Support Vector Machines (SVMs), in order to solve this. We use a dataset of 85 K tweets to study the designs, performances, and ethical concerns of these models. Our extensive research strives to mitigate the negative consequences of hateful speech on social media platforms.