计算机工程与科学2026，Vol.48Issue(6)：1119-1128,10.DOI:10.3969/j.issn.1007-130X.2026.06.014
基于双尺度图分层池化的微博谣言检测模型
A microblog rumor detection model based on dual-scale graph hierarchical pooling
李金鑫 1王维盛 1郭思阳1
作者信息
- 1. 西北师范大学计算机科学与工程学院,甘肃 兰州 730070
- 折叠
摘要
Abstract
How to accurately identify rumors on social networks has become a hot research issue.In the event-level rumor detection task based on graph neural network,the expressiveness of the graph convolution operator and graph pooling significantly affects the classification results.By comprehensive-ly considering the textual and user information within posts as well as the propagation structure between posts,and modeling various features using an event graph as the carrier,we propose a novel rumor de-tection model named GATv2-DSAPool.The model uses hierarchical pooling as the infrastructure.A dy-namic attention mechanism is introduced in graph attention network to capture the spatial structure of post propagation.A dual-scale attention is designed in graph pooling to compute the node scores.The set of nodes are selected to generate a coarsened graph based on the score ranking,which effectively en-codes the global features of events.Experimental results on two real microblog datasets show that the model outperforms the state-of-the-art benchmark models in terms of F1-score and accuracy.关键词
谣言检测/图神经网络/分层池化Key words
rumor detection/graph neural network/hierarchical pooling分类
信息技术与安全科学引用本文复制引用
李金鑫,王维盛,郭思阳..基于双尺度图分层池化的微博谣言检测模型[J].计算机工程与科学,2026,48(6):1119-1128,10.
