Intelligent Fake News Detection Leveraging Semantic and Context-Driven Analysis

With the rise of fake news as a societal threat, misinformation detection has become crucial in natural language processing. Traditional methods struggle with inadequate unimodal feature extraction, weak text-image fusion, and limited integration of user context. To address these issues, we suggest an intelligent Fake News Detection Leveraging Semantic and Context-Driven Analysis. Our model extracts text and image features via DeBERTa and CLIP-ViT, while a collaborative attention module enhances cross-modal interactions. Additionally, a graph convolutional network (GCN) captures user dissemination behaviors and social influence within the Semantic Web. By integrating structured user knowledge and multimodal content, the model constructs a holistic, context-aware news representation. Experimental results show that IFN-SC achieves ACC scores of 0.943, 0.963, and 0.911 on Weibo, Twitter, and GossipCop, outperforming state-of-the-art methods and demonstrating the effectiveness of Semantic Web-enhanced multimodal fusion in fake news detection.