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山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (4): 89-98.doi: 10.6040/j.issn.1672-3961.0.2021.296

• • 上一篇    

基于混合深度模型的虚假信息早期检测

黄皓1,周丽华1*,黄亚群1,姜懿庭2   

  1. 1.云南大学信息学院, 云南 昆明 650000;2.云南师范大学信息学院, 云南 昆明 650000
  • 发布日期:2022-08-24
  • 作者简介:黄皓(1994— ),男,四川成都人,硕士研究生,主要研究方向为数据挖掘、信息扩散. E-mail:840670997@qq.com. *通信作者简介:周丽华(1968— ),女,云南昆明人,教授,博士,CCF会员,主要研究方向为数据挖掘、社会网络分析、人工智能. E-mail: lhzhou@ynu.edu.cn
  • 基金资助:
    国家自然科学基金项目(61762090,62062066,61966036和61662086);云南省大学创新计划(IRTSTYN);国家社会科学基金项目(18XZZ005)

Early detection of fake news based on hybrid deep model

HUANG Hao1, ZHOU Lihua1*, HUANG Yaqun1, JIANG Yiting2   

  1. 1. School of Information, Yunnan University, Kunming 650000, Yunnan, China;
    2. School of Information, Yunnan Normal University, Kunming 650000, Yunnan, China
  • Published:2022-08-24

摘要: 针对一种信息特征进行检测方法在信息传播早期阶段提取的特征信息往往不充分,导致传播早期阶段检测准确率较低的问题,提出一个新颖的混合深度模型EGSI,模型由EXTRACT、GRU、SCORE和INTERATE 4个模块组成。EXTRACT通过卷积神经网络提取信息的传播路径特征,GRU通过门控循环单元捕获信息的文本特征和反馈特征,SCORE基于用户行为挖掘用户特征,INRERATE整合以上特征并预测出信息事件类标。EGSI通过整合信息最基本的4种特征(文本、用户、反馈、传播路径),从而可以在信息传播的早期阶段充分提取可用特征信息,进而较准确地检测出虚假信息。真实数据集的试验结果表明,模型在信息传播60 min内的准确率达到95.9%。相比基准方法,EGSI模型在检测虚假信息的准确率和时效性之间取得了较好的平衡。

关键词: 虚假信息早期检测, 混合深度模型, 神经网络, 时序分析, 信息特征

中图分类号: 

  • TP181
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