融合用户传播倾向信息的超图网络谣言检测模型  

Hypergraph-Based Rumor Detection Model Integrating User Propagation Bias Information

作　　者：彭竞杰 顾益军[1] 张岚泽 Peng Jingjie;Gu Yijun;Zhang Lanze(College of Information and Cyber Security,People’s Public Security University of China,Beijing 100038,China)

机构地区：[1]中国人民公安大学信息网络安全学院,北京100038

出　　处：《数据分析与知识发现》2024年第6期82-94,共13页Data Analysis and Knowledge Discovery

基　　金：中国人民公安大学网络空间安全执法技术双一流创新研究专项(项目编号:2023SYL07)的研究成果之一。

摘　　要：【目的】构造融合用户传播倾向信息的推文交互超图谣言检测模型,提高谣言检测准确率。【方法】提出一种名为UPBI_HGRD的谣言检测模型。该模型在获取推文节点嵌入表示时融合了用户传播倾向信息,并根据用户ID构造超边,形成能够反映推文交互关系的超图。此外,提出推文节点-用户超边级多层双级多头注意力机制关注重要的推文关系,从而有效学习节点的嵌入表示,最后将其输入分类器中判断是否是谣言。【结果】在三个公开数据集上的实验结果表明,所提模型的准确率分别达到了94.57%、97.82%和94.76%,优于基线模型,并具有优秀的谣言早期检测性能,证明了模型的有效性。【局限】获取融合用户传播倾向信息的推文嵌入表示以及构建超图的过程有一定时间开销,未来将从提高模型的时间效率等方面开展进一步研究。【结论】UPBI_HGRD模型可以有效提高谣言检测的准确率,为网络谣言的识别提供了新思路。[Objective]This paper aims to construct a tweet interaction hypergraph-based rumor detection model that integrates user propagation bias information to improve the accuracy of rumor detection.[Methods]A rumor detection model named UPBI_HGRD is proposed.The model integrates the user propagation bias information when obtaining the tweet node embedding representation,and constructs hyperedges based on user IDs to form a hypergraph that can reflect the interactive relationship of tweets.In addition,this paper proposes a tweet node-user hyperedge level multi-layer dual-level multi-head attention mechanism to focus on important tweet relationships,so as to effectively learn the embedding representation of nodes,and finally input it into a classifier to judge whether it is a rumor or not.[Results]The experimental results on three publicly available datasets show that the accuracy of the model reaches 94.57%,97.82%and 94.76%,respectively,which is better than the existing baseline model,and has an excellent performance in early detection of rumors,which proves the effectiveness of the model.[Limitations]The limitation of the model in this paper is that the process of obtaining the tweet embedding representation that integrates the user propagation bias information and constructing the hypergraph has a certain time overhead.In the future,further research will be done to improve the time efficiency of the model.[Conclusions]The proposed method effectively improves the accuracy of rumor detection and provides a novel approach to identifying online rumors.

关 键 词：谣言检测 节点嵌入 用户传播倾向信息 超图 多层双级多头注意力机制 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] G35[自动化与计算机技术—控制科学与工程]