A Rumor Detection Approach Based on Multi-Feature Propagation Tree

ZHANG Xin-xin; PAN Shan-liang; MAO Qin-jiao

doi:10.12263/DZXB.20220616

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A Rumor Detection Approach Based on Multi-Feature Propagation Tree

PAPERS | 更新时间：2025-12-11

- A Rumor Detection Approach Based on Multi-Feature Propagation Tree
- ACTA ELECTRONICA SINICA Vol. 52, Issue 5, Pages: 1609-1618(2024)
- 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：
- 基金信息：
  
  Zhejiang Province Public Welfare Technology Application Research Program Project(2017C33001)
- DOI：10.12263/DZXB.20220616
  CLC： TP391;
- Received：27 May 2022，
  
  Revised：2023-02-24，
  
  Published：25 May 2024
- 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

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