Published Paper Details:

Social media systems offer a space for open verbal exchange but they are also susceptible to the unfold of toxic content material cyberbullying and hate speech this undertaking introduces an ai-driven system that leverages recurrent neural networks RNN and natural language processing NLP to discover and mitigate harmful language in social media interactions the device constantly monitors user sports along with posting and liking feedback making sure actual-time identity of toxic content whilst a user posts a remark containing dangerous language the system generates an automated warning message informing them of the violation the device keeps a records of violations and repeated offenses lead to stricter penalties including temporary suspensions and account blocking off this structured enforcement mechanism discourages users from undertaking harmful interactions even as fostering a more secure online environment the task entails education an RNN-primarily based version on a dataset containing toxic and non-poisonous language patterns permitting accurate class of harmful content material superior NLP techniques which include tokenization sentiment analysis and context-aware filtering enhance the detection accuracy the gadget operates seamlessly within a social media platform integrating an automated moderation framework that balances person freedom of expression with the need of keeping a deferential digital area by using implementing this ai-powered method the task pursuits to decrease the superiority of poisonous interactions mitigate the psychological effect of cyberbullying and sell a more healthy online discourse this initiative contributes to creating a more inclusive and accountable digital surroundings making sure that social media stays a platform for optimistic engagement instead of a breeding ground for harmful behaviour.