Intelligent Fake News Detection Leveraging Semantic and Context-Driven Analysis

被引：0

作者：

Wang, Dongxiu ^{[1
]}

Chen, Zuxi ^{[2
]}

机构：

[1] Guangxi University of Science and Technology, China

[2] Huaqiao University, China

来源：

International Journal on Semantic Web and Information Systems | 2025年 / 21卷 / 01期

关键词：

Collaborative Attention; Cross-Modal Fusion; Fake News Detection; Graph Attention Mechanism; Graph Convolutional Networks; Semantic Web;

D O I：

10.4018/IJSWIS.378676

中图分类号：

学科分类号：

摘要：

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. © 2025 IGI Global. All rights reserved.

引用

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