基于自监督异质子图注意力网络的虚假新闻检测  

Fake News Detection Based on Self-supervised Heterogeneous Subgraph Attention Network

作　　者：李铭伟 陈浩鹏 李风环 陈宸 LI Ming-Wei;CHEN Hao-Peng;LI Feng-Huan;CHEN Chen(School of Computer Science and Technology,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学计算机学院,广州510006

出　　处：《计算机系统应用》2025年第2期237-245,共9页Computer Systems & Applications

基　　金：广东省自然科学基金(2021A1515012290);广州市黄埔区国际科技合作项目(2022GH01)。

摘　　要：由于虚假新闻检测任务的现有工作往往忽略了新闻文本的语义稀疏性及丰富信息之间的潜在联系,限制了模型对虚假新闻的理解和识别能力,本文提出了一种基于异质子图注意力网络的虚假新闻检测方法.针对新闻样本的文本、所属党派、主题等多种信息,构建了异质图,以建模虚假新闻的丰富特征.在特征层采用异质图注意力网络捕获不同类型信息之间的关系,在样本层引入子图注意力网络挖掘新闻样本间的交互.同时基于自监督对比学习的互信息机制关注全局图结构中的判别性子图表征,以捕获新闻样本的特异性.实验结果表明,本文提出的方法在Liar数据集上相比现有方法在准确率与F1值分别取得了约9%和12%的提升,显著提升了虚假新闻检测的性能.Since existing work on the task of fake news detection frequently ignores the semantic sparsity of news text and the potential relationships between rich information,which limits the model’s capacity to understand and recognize fake news,this study proposes a fake news detection method based on heterogeneous subgraph attention networks.Heterogeneous graphs are constructed to model the abundant features of fake news,such as text,party affiliation,and topic of news samples.The heterogeneous graph attention network is constructed at the feature layer to capture the correlations between different types of information,and a subgraph attention network is constructed at the sample layer to mine the interactions between news samples.Moreover,the mutual information mechanism based on self-supervised contrastive learning focuses on discriminative subgraph representations within the global graph structure to capture the specificity of news samples.Experimental results demonstrate that the method proposed in this study achieves about 9%and 12%improvement in accuracy and F1 score,respectively,compared with existing methods on the Liar dataset,which significantly improves the performance of fake news detection.

关 键 词：虚假新闻检测 异质图 子图 图注意力网络 自监督对比学习 

分 类 号：G21[文化科学—新闻学]