Efficient Cyberbullying Detection with using Distil BERTa and GLOVE net

Cyberbullying is agrowing concern in online communities, often leading to severe emotional distress and social isolation. This project presents a cyberbullying detection system that employs GLOVEnet and DistillBERTa to analyze usergenerated text and calculate a bullying percentage for each sentence. The system continuously monitors user behavior, dynamically reducing a reputation score based on detected bullying content. When a user's reputation score falls below a predefined threshold, they are automatically blocked from further interaction on various platforms. By combining deep learning and a reputation-based penalty mechanism, this system aims to mitigate cyberbullying incidents while maintaining a fair and proactive moderation process. The solution provides a scalable and effective approach for promoting healthier online environments.