Application of Cross-Modal Content Consistency Verification in Social Media Misinformation Detection

Abstract

The proliferation of multimedia misinformation on social media platforms necessitates automated detection systems capable of analyzing content consistency across modalities. This research presents a cross-modal consistency verification framework that systematically examines alignment patterns between textual and visual content to identify manipulated social media posts. The framework implements a three-stage pipeline: multimodal feature extraction using BERT and ResNet-50 encoders, cross-attention-based feature alignment, and ensemble classification combining semantic, temporal, and spatial consistency scores. Experimental evaluation on 45,000 social media posts demonstrates that the proposed framework achieves 87.3% accuracy and 0.912 AUC-ROC, representing 7.0% and 5.8% improvements over vision-language pre-training baselines, respectively. Ablation studies confirm that cross-modal consistency features contribute 13.7% improvement over unimodal approaches, with temporal verification providing the strongest individual signal. The framework processes 145 samples per second on GPU hardware, demonstrating practical feasibility for large-scale deployment. These results establish cross-modal consistency verification as an effective approach for automated misinformation detection in social media environments.