基于传播树的多特征谣言检测方法

张鑫昕; 潘善亮; 茅琴娇

doi:10.12263/DZXB.20220616

您当前的位置：

首页 >

文章列表页 >

基于传播树的多特征谣言检测方法

学术论文 | 更新时间：2025-12-11

- 基于传播树的多特征谣言检测方法
- A Rumor Detection Approach Based on Multi-Feature Propagation Tree
- 电子学报 2024年52卷第5期页码：1609-1618
- 作者机构：
  
  1.宁波大学信息科学与工程学院，浙江宁波 315211
  2.宁波工程学院网络空间安全学院，浙江宁波 315211
- 作者简介：
  
  [ "张鑫昕女，1998年出生，甘肃白银人.现为宁波大学信息科学与工程学院硕士研究生.主要研究方向为深度学习、自然语言处理、谣言检测等. E-mail: 2011082349@nbu.edu.cn" ]
  [ "潘善亮男，1970年出生，浙江台州人.现为宁波大学教授、硕士生导师.主要研究方向为服务计算、自然语言处理、数据挖掘等. E-mail: panshanliang@nbu.edu.cn" ]
  [ "茅琴娇女，1983年出生，浙江天台人.博士，现为宁波工程学院网络空间安全学院（计算机学院）讲师.主要研究方向为机器学习，智能信息处理. E-mail: maoqinjiao@163.com" ]
- 基金信息：
  
  浙江省公益性技术应用研究计划项目(2017C33001)
- DOI：10.12263/DZXB.20220616
  中图分类号： TP391;
- 收稿：2022-05-27，
  
  修回：2023-02-24，
  
  纸质出版：2024-05-25
- 稿件说明：
移动端阅览
张鑫昕, 潘善亮, 茅琴娇. 基于传播树的多特征谣言检测方法[J]. 电子学报, 2024, 52(05): 1609-1618.

ZHANG Xin-xin, PAN Shan-liang, MAO Qin-jiao. A Rumor Detection Approach Based on Multi-Feature Propagation Tree[J]. Acta Electronica Sinica, 2024, 52(05): 1609-1618.
张鑫昕, 潘善亮, 茅琴娇. 基于传播树的多特征谣言检测方法[J]. 电子学报, 2024, 52(05): 1609-1618. DOI：10.12263/DZXB.20220616

ZHANG Xin-xin, PAN Shan-liang, MAO Qin-jiao. A Rumor Detection Approach Based on Multi-Feature Propagation Tree[J]. Acta Electronica Sinica, 2024, 52(05): 1609-1618. DOI：10.12263/DZXB.20220616

摘要

目前网络谣言的检测方法主要是从传播路径中寻找信息，大多只采用文本信息作为初始传播特征，因此难以捕捉到丰富的传播结构表示.本文根据谣言的传播路径，提取文本和用户可信度特征，构建一种基于传播树的多特征谣言检测模型.模型通过图卷积网络聚合文本传播信息，使用多头注意力机制挖掘文本传播树的层内依赖关系，同时对用户传播树中的每个用户构建可信度序列，并采用M-Attention模块捕获有效的用户可信度特征.实验结果表明，本文提出的方法在Twitter15、Twitter16和Weibo数据集上的检测准确率达到89.3%、91.7%和96.4%，相比当前最优的传播树模型Bi-GCN（Binary Graph Convolutional Network）分别提升4.8%、4.2%和3%.

Abstract

At present

rumor detection methods on social platforms mainly focus on obtaining information from the propagation path

most of these methods only use text information as the initial propagation feature

which is difficult to capture the rich propagation structure representation. In this paper

according to the propagation path of rumors

text and user credibility features are extracted

and a multi-feature rumor detection model based on propagation tree is constructed. This model aggregates text propagation features through a graph convolutional network

and uses a multi-head attention module to mine the intra-layer dependencies of the text propagation tree. At the same time

a credibility sequence is constructed for each user in the user propagation tree

and the M-Attention module is used to capture effective user credibility features. The experimental results show that the experimental accuracy of Twitter15

Twitter16 and Weibo datasets reaches 89.3%

91.7% and 96.4%

which are 4.8%

4.2% and 3% higher than the current optimal propagation tree model Bi-GCN (Binary Graph Convolutional Network) accuracy respectively.

关键词

Keywords

references

郭家炜 , 朱云霞 . 重大突发事件中谣言传播与舆论引导研究——以新冠肺炎疫情期间部分典型谣言为例 [J ] . 科技传播 , 2021 , 13 ( 17 ): 6 .

GUO J W , ZHU Y X . A study on the spread of rumors and the guidance of public opinion in major emergencies—Taking some typical rumors during the COVID-19 epidemic as an example [J ] . Public Communication of Science & Technology , 2021 , 13 ( 17 ): 10 - 15 . (in Chinese)

CASTILLO C , MENDOZA M , POBLETE B . Information credibility on twitter [C ] // Proceedings of the 20th International Conference on World Wide Web . New York : ACM , 2011 : 675 - 684 .

SHU K , SLIVA A , WANG S , et al . Fake news detection on social media: A data mining perspective [J ] . ACM SIGKDD Explorations Newsletter , 2017 , 19 ( 1 ): 22 - 36 .

HORNE B , ADALI S . This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news [J ] . Proceedings of the International AAAI Conference on Web and Social Media , 2017 , 11 ( 1 ): 759 - 766 .

POTTHAST M , KIESEL J , REINARTZ K , et al . A stylometric inquiry into hyperpartisan and fake news [EB/OL ] . ( 2017 )[2022 ] . https://arxiv.org/abs/1702.05638 https://arxiv.org/abs/1702.05638 .

GIUDICE K D . Crowdsourcing credibility: The impact of audience feedback on Web page credibility [J ] . Proceedings of the American Society for Information Science and Technology , 2010 , 47 ( 1 ): 1 - 9 .

YANG Z , DAI Z , YANG Y , et al . XLNet: Generalized autoregressive pretraining for language understanding [EB/OL ] . ( 2019 )[2022 ] . https://arxiv.org/abs/1906.08237 https://arxiv.org/abs/1906.08237 .

MA J , GAO W , MITRA P , et al . Detecting rumors from microblogs with recurrent neural networks [C ] // IJCAI International Joint Conference on Artificial Intelligence . New York : ACM , 2016 : 3818 - 3824 .

MA J , GAO W , WONG K F . Detect rumor and stance jointly by neural multi-task learning [C ] // Companion Proceedings of the Web Conference 2018 . New York : ACM , 2018 : 585 - 593 .

CHEN T , LI X , YIN H , et al . Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection [C ] // Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer : Cham , 2018 : 40 - 52 .

夏鑫林 , 许亮 . 基于注意力机制的谣言检测算法研究 [J ] . 现代计算机 , 2020 , 37 ( 8 ): 47 - 51 .

XIA X L , XU L . Research on rumor detection algorithm based on attention mechanism modern computer [J ] . Modern Computer , 2020 , 37 ( 8 ): 47 - 51 . (in Chinese)

DOU Y , SHU K , XIA C , et al . User preference-aware fake news detection [C ] // Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM , 2021 : 2051 - 2055 .

LU Y J , LI C T . GCAN: Graph-aware co-attention networks for explainable fake news detection on social media [EB/OL ] . ( 2020 )[2022 ] . https://arxiv.org/abs/2004.11648 https://arxiv.org/abs/2004.11648 .

KIPF T N , WELLING M . Semi-supervised classification with graph convolutional networks [EB/OL ] . ( 2016 )[2022 ] . https://arxiv.org/abs/1609.02907 https://arxiv.org/abs/1609.02907 .

YUAN C , MA Q , ZHOU W , et al . Early detection of fake news by utilizing the credibility of news, publishers, and users based on weakly supervised learning [EB/OL ] . ( 2020 )[2022 ] . https://arxiv.org/abs/2012.04233 https://arxiv.org/abs/2012.04233 .

KWON S , CHA M , JUNG K , et al . Prominent features of rumor propagation in online social media [C ] // 2013 IEEE 13th International Conference on Data Mining . Piscataway : IEEE , 2013 : 1103 - 1108 .

WU K , YANG S , ZHU K Q . False rumors detection on sina weibo by propagation structures [C ] // 2015 IEEE 31st International Conference on Data Engineering . Piscataway : IEEE , 2015 : 651 - 662 .

HUANG Q , YU J , WU J , et al . Heterogeneous graph attention networks for early detection of rumors on Twitter [C ] // 2020 International Joint Conference on Neural Networks (IJCNN) . Piscataway : IEEE , 2020 : 1 - 8 .

SHU K , WANG S , LIU H . Beyond news contents: The role of social context for fake news detection [C ] // Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining . New York : ACM , 2019 : 312 - 320 .

KANG Z , CAO Y , SHANG Y , et al . Fake news detection with heterogenous deep graph convolutional network [C ] // Pacific-Asia Conference on Knowledge Discovery and Data Mining . Cham : Springer , 2021 : 408 - 420 .

BIAN T , XIAO X , XU T Y , et al . Rumor detection on social media with Bi-directional graph convolutional networks [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2020 , 34 ( 1 ): 549 - 556 .

WEI L , HU D , ZHOU W , et al . Towards propagation uncertainty: Edge-enhanced bayesian graph convolutional networks for rumor detection [EB/OL ] . ( 2021 )[2022 ] . https://arxiv.org/abs/2107.11934 https://arxiv.org/abs/2107.11934 .

RONG Y , HUANG W B , et al . DropEdge: Towards deep graph convolutional networks on node classification [EB/OL ] . ( 2019 )[2022 ] . https://arxiv.org/abs/1907.10903 https://arxiv.org/abs/1907.10903 .

MONTI F , FRASCA F , EYNARD D , et al . Fake news detection on social media using geometric deep learning [EB/OL ] . ( 2019 )[2022 ] . https://arxiv.org/abs/1902.06673 https://arxiv.org/abs/1902.06673 .

MA J , GAO W , WONG K F . Rumor detection on twitter with tree-structured recursive neural networks [C ] // Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics . Stroudsburg : Association for Computational Linguistics , 2018 : 1 - 12 .

YANG F , LIU Y , YU X , et al . Automatic detection of rumor on sina weibo [C ] // Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics . New York : ACM , 2012 : 1 - 7 .

MA J , GAO W , WEI Z , et al . Detect rumors using time series of social context information on microblogging websites [C ] // Proceedings of the 24th ACM International on Conference on Information and Knowledge Management . New York : ACM , 2015 : 1751 - 1754 .

ZHAO Z , RESNICK P , MEI Q . Enquiring minds: Early detection of rumors in social media from enquiry posts [C ] // Proceedings of the 24th International Conference on World Wide Web . Republic and Canton of Geneva : International World Wide Web Conferences Steering Committee , 2015 : 1395 - 1405 .

KWON S , CHA M , JUNG K . Rumor detection over varying time windows [J ] . PloS One , 2017 , 12 ( 1 ): e0168344 .

LIU Y , WU Y F B . Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks [C ] // 32nd AAAI Conference on Artificial Intelligence . New York : AAAI Press , 2018 : 354 - 361 .

YUAN C , MA Q , ZHOU W , et al . Jointly embedding the local and global relations of heterogeneous graph for rumor detection [C ] // 2019 IEEE International Conference on Data Mining (ICDM) . Piscataway : IEEE , 2019 : 796 - 805 .

HUANG Q , YU J S , WU J , et al . Heterogeneous graph attention networks for early detection of rumors on twitter [EB/OL ] . ( 2020 )[2022 ] . https://arxiv.org/abs/2006.05866 https://arxiv.org/abs/2006.05866 .

TU K , CHEN C , HOU C , et al . Rumor2vec: A rumor detection framework with joint text and propagation structure representation learning [J ] . Information Sciences , 2021 , 560 : 137 - 151 .

CHEN X , ZHOU F , TRAJCEVSKI G , et al . Multi-view learning with distinguishable feature fusion for rumor detection [J ] . Knowledge-Based Systems , 2022 , 240 : 108085 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

SMGN：用于对话状态跟踪的状态记忆图网络

双路注意力引导图卷积网络的关系抽取

基于邻域与超图协作的会话推荐