管理评论 ›› 2022, Vol. 34 ›› Issue (9): 147-157.

• 电子商务与信息管理 • 上一篇    下一篇

基于改进Deffuant模型的舆情观点演化解析

魏静1,2, 张耀曾1, 朱恒民1, 洪小娟1, 林萍1   

  1. 1. 南京邮电大学管理学院, 南京 210003;
    2. 南京邮电大学信息产业融合创新与应急管理研究中心, 南京 210003
  • 收稿日期:2019-12-12 出版日期:2022-09-28 发布日期:2022-10-28
  • 作者简介:魏静,南京邮电大学管理学院副教授,硕士生导师,博士;张耀曾,南京邮电大学管理学院硕士研究生;朱恒民,南京邮电大学管理学院教授,硕士生导师,博士;洪小娟,南京邮电大学管理学院副教授,硕士生导师,硕士;林萍,南京邮电大学管理学院副教授,硕士生导师,博士。
  • 基金资助:
    国家自然科学基金资助项目(71704085;71874088);教育部人文社会科学基金资助项目(17YJA870021)。

An Analysis of the Evolvement of Public Opinion Based on the Improved Deffuant Model

Wei Jing1,2, Zhang Yaozeng1, Zhu Hengmin1, Hong Xiaojuan1, Lin Ping1   

  1. 1. School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003;
    2. Research Center of Information Industry Integration Innovation and Emergency Management, Nanjing University of Posts and Telecommunications, Nanjing 210003
  • Received:2019-12-12 Online:2022-09-28 Published:2022-10-28

摘要: 针对当前大多数关于Deffuant模型的研究未考虑网络当中个体之间的联系强度演化问题以及不同个体对待观点的差异性和观点传播的普遍性,本文解析了个体之间的观点传播和联系强度的演化特点,提出一种基于有权网络的边权演化Deffuant模型。通过对仿真实验结果分析表明,信任程度较高的情况下有利于群体关系稳定和观点趋向一致,缩小观点分散的可能性;同时,政府和专家群体在引导舆论作用中具有明显的不同特征。构建的改进Deffuant模型的观点演化和网络的边权演化符合现实社会网络中的观点和关系演化。

关键词: 复杂网络, Deffuant模型, 关系演化, 观点演化

Abstract: In view of the fact that most current studies on Deffuant model do not take into account the evolution of the strength of individual relationships in the network, the differences of views among different individuals, and the universality of view dissemination, this paper analyses the characteristics of the evolution of view dissemination and strength of connections between individuals, and presents a new edge-weight evolution Deffuant model based on a weighted network. The results of simulation experiments show that a higher degree of trust is conducive to the stability of group relations and the consistency of opinions, and reduces the possibility of divergence of opinions. At the same time, government and expert groups have distinct characteristics in guiding public opinions. The viewpoint evolution of the improved Deffuant model and the edge weight evolution of the network conform to the viewpoint and relationship evolution in the real social network.

Key words: complex network, Deffuant model, relationship evolution, opinion evolution