Detection of Hate Speech Using Techniques in NLP and ML

This paper provides an overview of state-of-the-art approaches toward the identification of hate speech through Natural Language Processing and Machine Learning methods. These models that have been adopted include Bi-LSTM, CNN, and GPT-2, highlighting the merits and demerits. The best detection accuracy for hate speech was obtained using the model GPT-2 with a 97.12% rate because it understands the context as well as generates words in sequences. More profound challenges the paper deals with include linguistic ambiguity, dataset bias, and scalability. New contributions involve developing preprocessing pipelines from scratch, hybrids between models, and real-time deployment techniques. The end includes the ethical implication of the deployed automated hate speech detection systems and directions for future work in generating more culturally adaptable, multilingual, and computationally efficient models.