电讯技术2025，Vol.65Issue(6)：913-920,8.DOI:10.20079/j.issn.1001-893x.241011004
基于异构知识融合的多模态虚假新闻识别
Multimodal Fake News Detection Based on Heterogeneous Knowledge Fusion
摘要
Abstract
To address the challenges faced by current models in detecting logical inconsistencies and common-sense errors in multimodal fake news detection tasks,a multimodal fake news detection method based on a heterogeneous knowledge fusion network is proposed.This approach initially introduces algorithms for extracting logical reasoning and common-sense knowledge,enabling the extraction of relevant information from news articles while also identifying pertinent evidence from reasoning and common-sense knowledge graphs according to the extracted knowledge.Subsequently,a heterogeneous knowledge fusion network is designed,which first encodes the features of both knowledge and news.The method employs a heterogeneous graph attention network to aggregate features from the knowledge graph and utilizes a knowledge comparison module to contrast the knowledge obtained from the graph with the news knowledge,thus deriving deep comparative features.A collaborative attention module effectively fuses image and text features to generate multimodal features.Finally,the comparative knowledge features are adaptively fused with the multimodal features,further enhancing the model̓s recognition accuracy.Experimental results on the Weibo public dataset and a self-constructed dataset demonstrate that the proposed method achieves accuracy rates of 91.6%and 96.9%respectively,outperforming current mainstream methods.关键词
多模态虚假新闻识别/异构知识融合/知识图谱/跨模态融合Key words
multimodal fake news detection/heterogeneous knowledge fusion/knowledge graph/cross modal fusion分类
计算机与自动化引用本文复制引用
刘鑫,戴礼灿,张海瀛,王胜泽..基于异构知识融合的多模态虚假新闻识别[J].电讯技术,2025,65(6):913-920,8.基金项目
国家自然科学基金面上项目(62176171) （62176171）
