AI Driven Comment Risk Detection &Prevention System for Safer Social Media Environments

The extensive application of electronic communication platforms has led to an excessive and alarming increase in hate speech, thereby causing severe risks to online safety, mental health, and social cohesion. Conventional moderation techniques are not equipped to detect and react to abusive content within a timely response, resulting in delayed interventions. To combat this, a real-time hate speech detection and intervention system using artificial intelligence has been suggested. The solution utilizes Natural Language Processing (NLP), sentiment analysis, and deep learning patterns to effectively identify and manage harmful content. The objective is to minimize the dissemination of toxic language while ensuring scalability across platforms and languages. The proposed system utilizes contextual feature extraction using fine-tuned transformer models such as BERT and RoBERTa, which are optimized using ONNX to keep latency at a minimum. The novelty lies in the integration of transformer-based architectures, ONNX-level optimization, and a dynamic feedback loop, which allows for over 95% accuracy in detection with real-time responsiveness. The system is scalable and adaptable for application across social media platforms, public forums, and online communities.