计算机工程与应用2026，Vol.62Issue(9)：145-158,14.DOI:10.3778/j.issn.1002-8331.2510-0249
基于图数据增强与多模态特征对齐的虚假新闻检测
Fake News Detection Based on Graph Data Augmentation and Multimodal Feature Alignment
摘要
Abstract
Although researchers have conducted extensive research on multimodal fake news detection,there are still two major challenges:knowledge illusion in large language model(LLM)and cross modal semantic inconsistency.To address the above issues,a fake news detection model GDAFMA based on graph data augmentation and multimodal feature align-ment is proposed.Firstly,the LLM is used to construct multi-dimensional prompt templates for news document style,source authority,and image description,achieving fine-grained semantic deconstruction.Secondly,semantic information is transformed into graph structures,and cross modal deep inference is achieved through graph knowledge enhancement and graph encoding decoding mechanisms.Finally,a multimodal local and global feature alignment strategy is designed to achieve semantic consistency verification and comprehensive credibility evaluation.The experimental results on three publicly available datasets,GossipCop,Weibo,and PolitiFact,show that the classification accuracy of GDAFMA is as high as 0.868,0.916,and 0.898,respectively,far higher than all baseline methods,proving that it can significantly improve the multimodal fake news detection results.关键词
多模态虚假新闻检测/多维度提示模板/图数据增强/图编码解码/多模态特征对齐Key words
multimodal fake news detection/multi-dimensional prompt templates/graph data augmentation/graph encoding decoding/multimodal feature alignment分类
信息技术与安全科学引用本文复制引用
李洁,孙国营,段伊晴,王海龙,柳林..基于图数据增强与多模态特征对齐的虚假新闻检测[J].计算机工程与应用,2026,62(9):145-158,14.基金项目
国家自然科学基金地区科学基金(62567004). （62567004）
