基于Bert-GNNs异质图注意力网络的早期谣言检测

欧阳祺; 陈鸿昶; 刘树新; 王凯; 李星

doi:10.12263/DZXB.20220882

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基于Bert-GNNs异质图注意力网络的早期谣言检测

学术论文 | 更新时间：2026-04-10

- 基于Bert-GNNs异质图注意力网络的早期谣言检测
- Early Rumor Detection Based on Bert-GNNs Heterogeneous Graph Attention Network
- 电子学报 2024年52卷第1期页码：311-323
- 作者机构：
  
  中国人民解放军战略支援部队信息工程大学，河南郑州 450001
- 作者简介：
  
  [ "欧阳祺男，1999年生，江西吉安人.中国人民解放军战略支援部队信息工程大学研究生.主要研究方向为复杂网络、数据挖掘. E-mail: oyq126126@126.com" ]
  [ "陈鸿昶男，1964生，河南新密人.中国人民解放军战略支援部队信息工程大学博士研究生教授、博士生导师.主要研究方向为未来网络体系结构、人工智能等. E-mail: chenhongchang@ndsc.com.cn" ]
  [ "刘树新男，1987年生，山东潍坊人．中国人民解放军战略支援部队信息工程大学助理研究员.研究方向为复杂网络、移动通信网安全． E-mail: liushuxin11@gmail.com" ]
  [ "王凯男，1980年生，河南许昌人.国家数字交换系统工程技术研究中心副研究员.主要研究方向为电信网信息关防. E-mail: wangkai@ndsc.com.cn" ]
  [ "李星男，1981年生，河南新乡人.博士.中国人民解放军战略支援部队信息工程大学助理研究员.主要研究方向为链路预测、社团挖掘.E-mail: lixing@ndsc.com.cn" ]
- 基金信息：
  
  中原英才计划(212101510002)
- DOI：10.12263/DZXB.20220882
  中图分类号： TP393.1;
- 收稿：2022-07-25，
  
  修回：2023-03-29，
  
  纸质出版：2024-01-25
- 稿件说明：
移动端阅览
欧阳祺,陈鸿昶,刘树新,等.基于Bert-GNNs异质图注意力网络的早期谣言检测[J].电子学报,2024,52(01):311-323.

OUYANG Qi, CHEN Hong-chang, LIU Shu-xin, et al.Early Rumor Detection Based on Bert-GNNs Heterogeneous Graph Attention Network[J].Acta Electronica Sinica, 2024, 52(01): 311-323.
欧阳祺,陈鸿昶,刘树新,等.基于Bert-GNNs异质图注意力网络的早期谣言检测[J].电子学报,2024,52(01):311-323. DOI：10.12263/DZXB.20220882

OUYANG Qi, CHEN Hong-chang, LIU Shu-xin, et al.Early Rumor Detection Based on Bert-GNNs Heterogeneous Graph Attention Network[J].Acta Electronica Sinica, 2024, 52(01): 311-323. DOI：10.12263/DZXB.20220882

摘要

网络谣言的广泛传播已经造成了很大的社会危害，因此早期谣言检测任务已成为重要的研究热点.现有谣言检测方法主要从文本内容、用户配置和传播结构中挖掘相关特征，但没有同时利用到文本全局语义关系和局部上下文语义关系.为了克服以上局限性，充分利用到谣言数据中的文本全局-局部上下文语义关系、文本语义内容特征和推文传播的结构特征，本文提出了一种基于Bert-GNNs异质图注意力网络的早期谣言检测算法（Bert-GNNs Heterogeneous Graph Attention Network，BGHGAN）.该方法根据历史谣言集和用户特征构建一个推文-词-用户异质图，通过采用预训练语言模型Bert和图卷积神经网络（Graph Convolutional Network，GCN）结合的方法进行特征学习，以挖掘谣言的文本语义特征和文本之间的关系，并将异质图分解为推文-词子图和推文-用户子图，采用图注意力网络（Graph Attention network，GAT）的方式分别进行特征学习，从而更充分利用文本全局-局部上下文语义关系和传播图的全局结构关系以加强特征表达；最后，通过子图级注意力机制将不同模块的学习集成进行最终的谣言检测.所提算法在真实的Twitter15和Twitter16数据上进行实验，验证了该算法在检测准确率上分别为91.4%和91.9%，较现有最佳模型分别提高了1%和1.4%，也具备在早期阶段对谣言的检测能力；同时，本文通过实验探讨了不同特征对谣言检测的重要性、对异质图构建质量的重要性.

Abstract

The widespread spread of network rumors has caused great harm to the society

so the task of early rumor detection has become an important research focus. The majority of existing methods for rumor detection focus on mining effective features from text contents

user profiles

and patterns of propagation

but these methods do not take full advantage of both global semantic relationship of text and local context semantic relationship. In order to overcome the above limitations and make full use of the text global-local context semantic relationship

text semantic content feature and the structural feature of tweet propagation in the rumor data

this paper puts forward a kind of early rumors detection algorithm based on Bert-GNNs heterogeneous graph attention network (BGHGAN).This method constructs a tweet-word-user heterogeneous graph according to historical rumor sets and user characteristics

using the method of combining Bert and GCN (Graph Convolutional Network) for feature learning to mine the relationship between the text semantic features and the text of rumors. And by decomposing the heterogeneous graph into tweet-word subgraph and tweet-user subgraph

the method uses GAT (Graph Attention network) to perform feature learning respectively

so as to make full use of the global-local context semantic relationship of the text and the global structure relationship of the propagation graph to strengthen the feature expression. Finally

the learning integration of different modules is carried out through the subgraph-level attention mechanism for final rumor detection. The proposed algorithm is experimented on real Twitter15 and Twitter16 data

and verifies that the detection accuracy of the algorithm is 91.4% and 91.9%

respectively

which is 1% and 1.4% higher than the existing best model

and also has the ability to detect rumors in the early stage. And this paper discusses the importance of different features to rumor detection and the importance of the quality of heterogeneous graph construction.

关键词

Keywords

references

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