基于双向图注意力网络的潜在热点话题谣言检测  

Rumor Detection on Potential Hot Topics with Bi-directional Graph Attention Network

作　　者：李劭 蒋方婷 杨鑫岩 梁刚 LI Shao;JIANG Fangting;YANG Xinyan;LIANG Gang(School of Cyber Science and Engineering,Sichuan University,Chengdu 610211,China)

机构地区：[1]四川大学网络空间安全学院,成都610211

出　　处：《计算机科学》2025年第3期277-286,共10页Computer Science

基　　金：国家自然科学基金重大项目(62162057)。

摘　　要：现有社交网络谣言检测方法大多将社交网络中的单个帖子视为检测目标,存在因数据量不足而导致的检测冷启动问题,影响检测性能。另外,现有方法没有对海量社交网络信息中与检测无关的信息进行过滤,导致检测时延较长,性能较差。在分析谣言的传播特征时,现有方法大多侧重于谣言传播过程中的静态特征,难以充分利用节点间的动态关系对复杂的传播过程进行表征,导致性能提升存在瓶颈。针对以上问题,文中提出了一种基于潜在热点话题和图注意力神经网络的谣言检测方法,该方法采用神经主题模型和潜在热点话题发现模型进行话题级别的谣言检测以克服冷启动问题,并设计了一个基于双向图注意力神经网络的检测模型TPC-BiGAT,分析谣言话题传播过程中的动态特征以进行谣言真实性检测。在3个公开数据集上进行了多次实验证明,该方法在准确率上较现有方法取得了3%~5%的显著提升,验证了所提方法的有效性。Most of the existing methods for detecting rumors on social media networks typically focus on individual posts as the target of detection,which leads to a cold start problem due to insufficient data,adversely affecting the detection performance.Moreover,these methods do not filter out the vast amount of irrelevant information in social media networks,resulting in longer detection latency and poorer performance.Additionally,current methods tend to emphasize static features during the spread of rumors when analyzing the characteristics of rumor propagation,making it difficult to fully leverage the dynamic relationships between nodes to model the complex propagation process.To address these issues,a rumor detection method based on potential hot topics and graph attention neural networks is proposed.The method employs a neural topic model and a potential hot topic discover model for topic-level rumor detection to overcome the cold start problem.Furthermore,a detection model named TPC-Bi-GAT is designed to analyze the dynamic features of rumor topic propagation for authenticity detection.Experiments on 3 public datasets show that the proposed method achieves a significant improvement of 3%~5% in accuracy compared with the existing methods,which verifies the effectiveness of the proposed method.

关 键 词：谣言检测 社交网络 潜在热点话题 图神经网络 主题聚类 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]