Detection Of Cyberbullying On Social Media Using Machine Learning

The explosion of social media activity has extended the reach and impact of cyberbullying, presenting significant psychological and social risks. Effective moderation is challenged by platform-specific slang, the use of mixed media, complex context, and high user volume. This research introduces a cross-platform system for cyberbullying detection that leverages text, images, and conversational context using graph-based neural networks. The work includes the creation and annotation of a diverse, fine-grained multi-platform dataset. The proposed system utilizes a hybrid model integrating transformers, visual encoders, and relationship-focused neural networks. Results demonstrate improvements in generalization across platforms, multimodal understanding, and ethical fairness, enabling responsible and scalable deployment.