HDCR: Cross-Lingual Medical Misinformation Detection Through Contrastive Claim-Evidence Reasoning

The rapid dissemination of health information online enables dangerous distortions that threaten public health. We present HD² CR (health-information distortion detection with contrastive reasoning), a framework for fine-grained detection of medical misinformation. Through systematic analysis of health news patterns, we identify four primary distortion types: over-generalization, exaggeration, under-generalization, and false causality. Our contributions include: a cross-lingual corpus of 72,275 English and Chinese claim-evidence pairs with validated distortion labels; a dual-encoder architecture with contrastive cross-attention that explicitly models semantic divergence between claims and biomedical evidence; and extensive evaluations demonstrating HD²²CR′ s superior performance: 93.1% binary F₁ and 87.3% 5-class accuracy, with robust cross-lingual generalization (only 2.9% degradation between Chinese and English).