Robust Implementation of a Hate Speech Detection System

Abstract

Abstract - A hate speech detection system leverages Machine Learning and Natural Language Processing to automatically identify and flag abusive or offensive language in digital content, promoting safer online environments and aiding content moderation. The system involves data preprocessing techniques such as tokenization, stemming, and stop-word removal, followed by training on labeled datasets containing examples of hate and non-hate speech. Common algorithms include Support Vector Machines (SVM), Naive Bayes, Recurrent Neural Networks (RNNs), and transformers like BERT. Performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. This robust solution can be integrated into content platforms to curb harmful language, enhance user safety, and ensure compliance with content regulation standards. Key Words: Hate Speech Detection, Machine Learning, Natural Language Processing, Content Moderation, Data Preprocessing, Support Vector Machines, Naive Bayes, Recurrent Neural Networks, BERT, Evaluation Metrics, Online Safety, Regulatory Compliance.