计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (11): 54-64.doi: 10.11896/jsjkx.240300189

• 社交媒体虚假信息检测 • 上一篇    下一篇

用于谣言检测的图卷积时空注意力融合与图重构方法

陈鑫, 荣欢, 郭尚斌, 杨彬   

  1. 南京信息工程大学人工智能(未来技术)学院 南京 210044
  • 收稿日期:2024-03-29 修回日期:2024-07-24 出版日期:2024-11-15 发布日期:2024-11-06
  • 通讯作者: 荣欢(1227558210@qq.com)
  • 作者简介:(2409749761@qq.com)
  • 基金资助:
    国家自然科学基金(62102187);江苏省自然科学基金(省基础研究计划)(BK20210639)

Graph Convolution Spatio-Temporal Attention Fusion and Graph Reconstruction Method forRumor Detection

CHEN Xin, RONG Huan, GUO Shangbin, YANG Bin   

  1. School of Artificial Intelligence(School of Future Technology),Nanjing University of Information Science and Technology,Nanjing 210044,China
  • Received:2024-03-29 Revised:2024-07-24 Online:2024-11-15 Published:2024-11-06
  • About author:CHEN Xin,born in 2003,undergra-duate.His main research interests include data mining and machine lear-ning.
    RONG Huan,born in 1990,Ph.D,associate professor.His main research intere-sts include knowledge graph and know-ledge engineering,social network multimedia mining,and complex machine learning theory.
  • Supported by:
    National Natural Science Foundation of China(62102187) and Natural Science Foundation of Jiangsu Province,China(Basic Research Program)(BK20210639).

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摘要： 互联网的快速发展给人们带来了便利的社交,同时也为谣言的产生和传播创造了条件。谣言的传播速度之快、影响之恶劣引起了广泛的关注。为了及时识别出谣言以采取截断措施,谣言检测变得尤为重要。然而,在复杂的社交网络中,谣言传播状态动态变化、传播过程中干扰信息的存在,以及传播的不确定性等均为谣言检测带来了困难。为了解决上述问题,提出了一种用于谣言检测的图卷积时空注意力融合与图重构方法(STAFRGCN)。该方法对所有待检测言论进行两次检测以降低误判概率,首先使用一种时间渐进卷积模块(TPC)在时间维度上整合待测言论传播状态信息;然后分别在时间和空间两个方面使用注意力提取其主要传播特征信息并融合,对融合结果进行第一次谣言检测;随后基于LSTM预测和图重构方法调整待测言论传播总图结构,将其与第一次检测结果结合进行第二次检测。实验结果表明,STAFRGCN在Twitter15,Twitter16和Weibo数据集上的检测准确率分别为92.2%,91.8%和96.5%,与SOTA模型(KAGN)相比,准确率在3个数据集上分别提升了3.0%,1.5%和1.4%。

关键词: 谣言检测, 图神经网络, 图卷积, 注意力机制

Abstract: The rapid development of the Internet has brought convenience to people’s social life,but it also creates conditions for the generation and spread of rumors.The fast propagation speed and bad impact of rumors have attracted wide social attention.However,in complex social networks,the dynamic change of rumor propagation state,the existence of interference information in the propagation process,and the uncertainty of propagation all bring difficulties to rumor detection.In order to solve the above problems,this study proposes a graph convolution spatio-temporal attention fusion and graph reconstruction method(STAFRGCN) for rumor detection,and all the speeches to be detected are detected twice to reduce the probability of misjudgment.Firstly,a temporal progressive convolution module(TPC) is used to integrate the propagation status information of the speeches to be detected in the time dimension.Then,attention is used to extract and fuse the main propagation feature information in two aspects of time and space respectively,and the fusion result is used for the first rumor detection.After that,the total graph structure of the detected speech propagation is adjusted based on long short-term memory(LSTM)prediction and graph reconstruction method.It is combined with the first detection results for the second detection.Experiments show that the detection accuracy of STAFRGCN on Twitter15,Twitter16 and Weibo datasets is 92.2%,91.8% and 96.5%,respectively.Compared with SOTA model(KAGN),the accuracy is increased by 3.0%,1.5% and 1.4% on the 3 datasets, respectively.

Key words: Rumor detection, Graph neural network, Graph convolution, Attention mechanism

中图分类号: 

  • TP389.1
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