Advanced Cyberbullying Detection System using ML with Gen-Z Slang and Emoji Analysis

Cyberbullying has emerged as a severe challenge in the digital age, especially among adolescents and young adults who are highly active on social media platforms. Existing detection systems often fail when faced with dynamic and evolving communication patterns, particularly those adopted by Generation-Z. This paper presents an advanced detection framework that integrates Gen-Z slang interpretation, emoji sentiment mapping, and machine learning classifiers. Unlike traditional models, the proposed system accounts for context-rich linguistic variations. Experimental evaluation demonstrates improved accuracy, recall, and reliability, thereby contributing to safer digital spaces.