A Compact GPT-Based Multimodal Fake News Detection Model with Context-Aware Fusion

Abstract

With the rapid development of social networks, online news has gradually surpassed traditional paper media and become a main channel for information dissemination. However, the proliferation of fake news also poses a serious threat to individuals and society. Since online news often involves multimodal content such as text and images, multimodal fake news detection has become increasingly important. To address the challenges of feature extraction and cross-modal fusion in this task, this study presents a new multimodal fake news detection model. The model uses a GPT-style encoder to extract text semantic features, a ResNet backbone to extract image visual features, and dynamically captures correlations between modalities through a context-aware multimodal fusion module. In addition, a joint optimization strategy combining contrastive loss and cross-entropy loss is designed to enhance modal alignment and feature discrimination while optimizing classification performance. Experimental results on the Weibo and PHEME datasets show that the proposed model outperforms baseline methods in accuracy, precision, recall, and F1-score, effectively captures correlations between modalities, and improves the quality of feature representation and overall model performance. This study suggests that the proposed approach may serve as a useful approach for fake news detection on social platforms.