Hate Speech Detection on Twitter: A Comparative Evaluation of Different Machine Learning Techniques

The necessity for robust and fast detection techniques has become critical as social media hate speech has grown. This study investigates ways to identify hate speech comments present on Twitter using language processing methods. In this work, we suggest a cutting-edge method for effectively identify hate speech in tweets that combines linguistic elements and machine learning techniques. Using a sizable dataset of annotated tweets, we test our model, and we get good F1-score and accuracy. The findings of this study present the possibilities of using techniques for processing natural language to identify hateful speech on Twitter and can assist in direct the creation of efficient regulations and interventions to lessen the negative consequences of hate speech on social media sites.