A Machine Learning Based Approach for Hate Speech Detection

In recent years, the proliferation of social media and digital communication platforms has led to an exponential increase in user-generated content. While these platforms offer numerous benefits, they have also become breeding grounds for hate speech. Identifying and mitigating hate speech is crucial to maintaining a safe and inclusive online environment. Traditional methods of content moderation, which rely heavily on human moderators, are increasingly inadequate due to the sheer volume of content and the nuanced nature of hate speech. The proposed work presents an artificial intelligence based technique for detection of hate speech. The proposed approach uses the Deep BayesNet for identifying potential hate speech. It has been shown that the proposed approach attains higher classification accuracy compared to existing work in the domain.