Real-Time Hate Speech Recognition Along with Educational Feedback and Automatic Reporting

In today's digital era, the rise of toxicity, discrimination, and harm on online communication platforms has become a significant concern, necessitating effective moderation strategies. Existing methods for hate speech detection often lack real-time capabilities and fail to provide constructive feedback to users. Natural Language Processing (NLP) is used to identify the intent of the speech and bidirectional Long Short Term Memory (LSTM) machine learning algorithm classifies text into multiple hate speech categories. The designed system also displays charts, providing a detailed assessment and understanding of the nature and severity of the content. With a user-friendly interface, users receive real-time educational feedback on the nature and impact of different types of hate speech, promoting awareness and discouraging toxic behavior.