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
张鑫昕 1潘善亮 1茅琴娇2
作者信息
- 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
- 折叠
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 cap-ture 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
谣言检测/传播结构/图卷积网络/注意力机制/自然语言处理Key words
rumor detection/propagation structure/graph convolutional network/attention mechanism/natural lan-guage processing引用本文复制引用
基金项目
浙江省公益性技术应用研究计划项目(2017C33001)
出版年
2024
国家科技期刊平台
NSTL
万方数据