GCCN

HCIG: Hierarchical Cross-Modal Incongruity Graph Network for Multimodal Sarcasm and Cyberbullying Detection

Researchers propose HCIG, a graph attention network framework that models cross-modal incongruity between text and images at token, phrase, and global levels for detecting sarcasm and cyberbullying in social media. A complementary architecture, GCCN, uses contradiction-aware pooling for efficient multimodal reasoning. HCIG achieves 85.74% accuracy on the MMSD sarcasm benchmark and 69.62% accuracy on the MultiBully cyberbullying dataset, outperforming conventional fusion-based approaches. The work addresses a narrow but practically relevant NLP task in content moderation.