ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (7): 1425-1435.doi: 10.7544/issn1000-1239.2021.20200806

所属专题: 2021虚假信息检测专题

• 信息处理 • 上一篇    下一篇



  1. 1(大连民族大学信息与通信工程学院 辽宁大连 116600);2(浙江大学传媒与国际文化学院 杭州 310058);3(杭州师范大学阿里巴巴复杂科学研究中心 杭州 311121) (
  • 出版日期: 2021-07-01
  • 基金资助: 

Research on Spreading Mechanism of False Information in Social Networks by Motif Degree

Xu Mingda1, Zhang Zike2,3, Xu Xiaoke1   

  1. 1(College of Information and Communication Engineering, Dalian Minzu University, Dalian, Liaoning 116600);2(College of Media and International Culture, Zhejiang University, Hangzhou 310058);3(Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121)
  • Online: 2021-07-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61773091, 61673151), the Liaoning Revitalization Talents Program (XLYC1807106), the Natural Science Foundation of Liaoning Province (2020-MZLH-22), and the Zhejiang Provincial Natural Science Foundation of China (LR18A050001).

摘要: 社交媒体作为信息传播的载体,既可使人们快捷地分享信息流和获取时事新闻,也可能成为虚假信息泛滥蔓延的重要渠道.现有的虚假信息检测研究多基于对微博内容的机器学习或深度学习的识别模型,忽略了真假信息传播网络的结构差异.基于复杂网络的模体理论,提出了广度模体度与深度模体度的概念来量化传播网络的结构重要指标.研究表明:基于模体度的重要性计算方法是对传统网络结构重要性指标的一种创新与拓展,能够更全面地测度传播网络结构特性.通过构建的二维模体度量化指标,分析和揭示了微博、Twitter网络中虚假信息的结构特性与传播机制:虚假信息在广度传播与深度传播共同作用下扩散,广度模体度主要作用于网络传播规模,而深度模体度影响网络结构的复杂性.基于模体度的网络特征分析,可以应用于社交媒体信息传播的早期从源头上检测虚假信息,为虚假信息检测提供了一种新颖可行的途径.

关键词: 信息传播, 模体度, 虚假信息, 谣言检测, 网络结构分析, 在线社交网络

Abstract: In online social networks, massive amounts of information are transmitted and diffused through users’ interaction and reposting behavior. As the carrier of information diffusion, social media can not only make people share information flow and get current affairs news quickly, but also facilitate the exchange of ideas and information between people. At the same time, it may become an important channel for the spread of false information. Most of the existing researches on false information detection are based on the recognition models of machine learning and deep learning of Weibo content, while ignoring the structural differences between true and false information networks. Therefore, based on the motif theory of complex networks, this paper puts forward the concepts of breadth and depth motif degree to quantify the structural importance of the network. The research shows that the importance calculation method based on motif degree is an innovation and expansion of traditional network structure importance index, which can measure the specificity of communication network structure more comprehensively. This paper analyzes and reveals the structure characteristics and propagation mechanism of false information in microblog network by constructing the two-dimensional motif measurement index, that is, the false information is diffused under the joint action of breadth and depth propagation, and the breadth motif mainly affects the network spread scale, while the depth motif degree affects the complexity of the network structure. Even in the early stage of information diffusion, the false news detection method based on motif features has a high prediction accuracy. The network feature analysis based on motif degree can be applied to detect false information from the source in the early stage of social media information diffusion, which provides a novel and feasible way for false information detection.

Key words: information diffusion, motif degree, false information, rumor detection, network structure analysis, online social network