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多源特征融合增强的虚假新闻检测方法

胡泽 陈志南 杨宏宇

胡泽, 陈志南, 杨宏宇. 多源特征融合增强的虚假新闻检测方法[J]. 电子与信息学报. doi: 10.11999/JEIT250041
引用本文: 胡泽, 陈志南, 杨宏宇. 多源特征融合增强的虚假新闻检测方法[J]. 电子与信息学报. doi: 10.11999/JEIT250041
HU Ze, CHEN Zhinan, YANG Hongyu. A Fake News Detection Approach Enhanced by Multi-Source Feature Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250041
Citation: HU Ze, CHEN Zhinan, YANG Hongyu. A Fake News Detection Approach Enhanced by Multi-Source Feature Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250041

多源特征融合增强的虚假新闻检测方法

doi: 10.11999/JEIT250041 cstr: 32379.14.JEIT250041
基金项目: 国家自然科学基金(62201576, U2433205);国家自然科学基金配套基金(3122023PT10)
详细信息
    作者简介:

    胡泽:男,讲师,研究方向为人工智能、自然语言处理、网络空间安全、医学信息

    陈志南:男,硕士生,研究方向为人工智能、自然语言处理、信息安全

    杨宏宇:男,教授,博士生导师,研究方向为网络空间安全、软件安全、网络安全态势感知

    通讯作者:

    杨宏宇 hyyang@cauc.edu.cn

  • 中图分类号: TN915.08; TP393

A Fake News Detection Approach Enhanced by Multi-Source Feature Fusion

Funds: The National Natural Science Foundation of China (62201576, U2433205), The Supporting Fund of the National Natural Science Foundation of China (3122023PT10)
  • 摘要: 针对现有虚假新闻检测方法在提取和利用新闻多层次特征及捕获新闻传播高阶结构特征方面的局限性,该文提出一种多源特征融合增强(MSFFE)的虚假新闻检测方法。该方法利用多层次注意力机制,从结构、时序和内容3个维度提取新闻特征:首先,通过增强型超图神经网络提取新闻传播的结构特征;其次,利用多尺度时序模块捕获新闻传播的时序特征;最后,采用多头自注意力机制提取新闻内容特征。特别地,该方法设计了一种特征融合门控单元,用于动态调整不同特征维度的权重,从而实现多源异构特征的高效融合。在公开数据集Politifact和Gossipcop上的实验结果显示,该方法的检测性能较UPFD, HGNN, RTRUST(State-of-the-Art)等近年的基线方法有所提升。其中,与最先进的方法相比较,在Politifact数据集上,准确率提升了3.64%,F1分数提升了3.41%;在Gossipcop数据集上,准确率提升了0.55%,F1分数提升了0.56%。这些实验结果表明,该方法能够有效检测虚假新闻,为虚假新闻检测领域提供了新思路和技术支撑。
  • 图  1  传播树和超图编码结构图[32]

    图  2  MSFFE总体框架图

    图  3  结构特征提取结构图[32]

    图  4  时序特征提取结构图[39]

    图  5  新闻文本特征提取结构图

    图  6  多源特征融合结构图

    图  7  Hidden Size大小对实验结果影响

    图  8  算法在两个数据集的训练过程

    表  1  数据集的信息[22]

    数据集#图#假新闻#真新闻#节点#边
    Politifact31415715741,05450,740
    Gossipcop5,4642,7322,732314,262308,798
    注:“#”代表数量。
    下载: 导出CSV

    表  2  对比实验结果(%)[32,47]

    方法PolitifactGossipcop
    准确率F1分数准确率F1分数
    GNN-CL65.79±8.9665.02±9.4694.98±0.8094.94±0.80
    Bi-GCN74.16±3.5774.16±3.5788.04±0.4887.95±0.49
    UPFD-GCN80.27±4.3580.16±4.4195.55±0.6395.51±0.64
    UPFD-GAT79.09±3.7378.95±3.7996.03±0.6296.00±0.62
    UPFD-SAGE80.40±4.2280.13±4.6596.38±0.4896.36±0.48
    TGNF74.28±1.7474.09±1.8185.07±0.0885.07±0.08
    GTN81.67±4.1681.53±4.3592.41±0.9892.38±0.98
    HGAT81.53±1.1680.47±1.75
    GCNFN80.63±4.2380.31±4.5795.37±0.2195.33±0.21
    HGNN79.96±4.8979.28±5.1693.38±0.4993.38±0.49
    RTRUST (SOTA, 2024)90.1189.8697.4697.41
    MSFFE93.75±1.5693.27±1.6898.01±0.0897.97±0.08
    下载: 导出CSV

    表  3  消融实验结果(%)

    特征类型 Politifact Gossipcop
    S T C F 准确率 F1分数 准确率 F1分数
    84.62 84.56 97.23 97.22
    92.19 91.60 97.53 97.53
    89.94 89.93 97.72 97.71
    89.42 89.42 97.62 97.61
    92.90 92.86 97.89 97.89
    93.75 93.27 98.01 97.97
    下载: 导出CSV

    表  4  虚假新闻检测案例分析

    推文数量
    (传播数量)
    推文发布
    时间特征
    虚假新闻(英文原文)虚假新闻(中文译文)
    MSFFE
    敏感
    案例16304短时间内
    迅速扩散
    “SPECIAL REPORT: GEORGIA BECOMES
    FIRST STATE TO BAN MUSLIM CULTURE
    IN HISTORIC MOVE TO RESTORE
    WESTERN VALUES!”
    “特别报道:佐治亚州成为首个
    禁止穆斯林文化的州,历史性举措旨在
    恢复西方价值观!”
    案例25122短时间内
    迅速扩散
    “Snapchat is shutting down!”“Snapchat即将关闭!”
    MSFFE
    不敏感
    案例30无推文转发“Billy Bush”“比利·布什”
    案例40无推文转发“Alabama Secretary of State”“阿拉巴马州国务卿”
    下载: 导出CSV
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出版历程
  • 收稿日期:  2025-01-16
  • 修回日期:  2025-05-21
  • 网络出版日期:  2025-06-14

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