网络舆情结构逆转建模与仿真:基于改进Deffuant模型

于同洋; 肖人彬; 侯俊东; 于同洋; 肖人彬; 侯俊东

doi:10.13306/j.1672-3813.2019.03.003

复杂系统与复杂性科学 >

2019 , Vol. 16 >Issue 3: 30 - 39

DOI: https://doi.org/10.13306/j.1672-3813.2019.03.003

网络舆情结构逆转建模与仿真:基于改进Deffuant模型

于同洋 ,
肖人彬 ,
侯俊东 ,
于同洋 ,
肖人彬 ,
侯俊东

展开

1.中南民族大学管理学院,武汉 430074;
2.华中科技大学人工智能与自动化学院,武汉 430074;
3.中国地质大学(武汉)经济管理学院,武汉 430074

于同洋(1980-),男,山东招远人,博士,讲师,主要研究方向为复杂系统建模与仿真。

收稿日期: 2019-05-28

网络出版日期: 2019-10-24

基金资助

国家自然科学基金(61540032, 71874163);中南民族大学中央高校基本科研业务费专项资金(CSQ18074)

收起

Modeling and Simulation of Online Public Opinion Structure Reversal: Based on An Improved Deffuant Model

YU Tongyang ,
XIAO Renbin ,
HOU Jundong ,
YU Tongyang ,
XIAO Renbin ,
HOU Jundong

Expand

1. School of Management, South-Central University for Nationalities, Wuhan 430074, China;
2. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;
3. School of Economics and Management, China University of Geosciences, Wuhan 430074, China

Received date: 2019-05-28

Online published: 2019-10-24

Fold

摘要

社会舆情从现实社会转移到网络空间,导致网络舆情事件的出现。网络舆情扩散使得群体舆论的支持与反对力量对比反转,最终改变利益集团最初的决策,该现象称为网络舆情结构逆转。为了探索这种现象背后的复杂系统机制,本文基于Deffuant模型建立了网络舆情扩散的结构逆转模型,该模型从两个方面对Deffuant模型改进,一是将个体自身的观点值转化为交互时的感知值,将连续观点的模型转化为离散观点模型,二是引入宏观的社会转型因素的影响。仿真结果显示,社会转型因素正向地影响网络舆情扩散,社会转型程度高,网络舆情结构逆转时的舆情扩散程度更高;群体异质性水平正向地延缓网络结构逆转现象,利益群体规模越大,网络舆情结构逆转越难以完成。本文提出的模型能一定程度上解释网络舆情扩散的结构逆转现象,可以为实践中网络舆情的管控提供理论参考与决策支持。

关键词： 网络舆情; 结构逆转; 扩散; Deffuant模型

本文引用格式

于同洋 , 肖人彬 , 侯俊东 , 于同洋 , 肖人彬 , 侯俊东 . 网络舆情结构逆转建模与仿真:基于改进Deffuant模型[J]. 复杂系统与复杂性科学, 2019 , 16(3) : 30 -39 . DOI: 10.13306/j.1672-3813.2019.03.003

Abstract

Social public opinions transfer from the real society to the network space, resulting in the emergence of network public opinion events. Online public opinion diffusion reverses the contrast between the support and opposition of public opinions, and finally changes the initial decision of interest groups, which is called the structure reversal of online public opinion. In order to explore the complex systems mechanism underlying this phenomenon, we established a network public opinion structure reversal model based on the Deffuant model. The Deffuant model is modified from two aspects: one is to transform the individual′s own opinion value into the perception opinion value in the interaction; the other is to introduce the influence of social transformation from a macro perspective. The simulation results show that social transformation factors positively influence the diffusion of online public opinion. The higher the degree of social transformation, the higher diffusion of public opinion in structure reversal. Heterogeneity level positively delays the public opinion structure reversal. The larger the size of interest groups, the more difficult it is to complete public opinion structure reversal. The model proposed in this paper can explain the structural reversal phenomenon of public opinion diffusion to a certain extent and can provide certain theoretical reference and decision support for the control of network public opinion in practice.

Key words： public opinion; structure reversal; diffusion; Deffuant model

参考文献

[1]Huang C, Hu B, Jiang G, et al. Modeling of agent-based complex network under cyber-violence[J]. Physica A Statistical Mechanics & Its Applications, 2016, 458: 399-411.
[2]Noelle-Neumann E. The spiral of silence a theory of public opinion[J]. Journal of Communication, 1974, 24(2): 43-51.
[3]Vande K C, Martin S, Gend P, et al. Modelling influence and opinion evolution in online collective behaviour[J]. Plos One, 2016, 11(6): e0157685.
[4]Jia P, Mirtabatabaei A, Friedkin N E, et al. Opinion dynamics and the evolution of social power in influence networks[J]. Siam Review, 2015, 57(3): 367-397.
[5]朱张祥, 刘咏梅. 在线社交网络谣言传播的仿真研究——基于聚类系数可变的无标度网络环境[J]. 复杂系统与复杂性科学, 2016, 13(2): 74-82.
Zhu Zhangxiang, Liu Yongmei. Simulation study of propagation of rumor in online social network based on scale-free network with tunable clustering[J]. Complex Systems and Complexity Science, 2016,13(2): 74-82.
[6]Xiao R B, Yu T Y. A multi-agent simulation approach to rumor spread in virtual community based on social network[J]. Intelligent Automation & Soft Computing, 2011, 17(7): 859-869.
[7]Dechêne A, Stahl C, Hansen J, et al. The truth about the truth: a meta-analytic review of the truth effect[J]. Personality & Social Psychology Review, 2010, 14(2): 238-257.
[8]Parsegov S E, Proskurnikov A V, Tempo R, et al. Novel multidimensional models of opinion dynamics in social networks[J]. IEEE Transactions on Automatic Control, 2017, 62(5): 2270-2285.
[9]Vicsek T, Czirók A, Ben-Jacob E, et al. Novel type of phase transition in a system of self-driven particles[J]. Physical Review Letters, 1995, 75(6): 1226.
[10] Friedkin N E, Johnsen E C. Social influence network theory: social positions in influence networks[J]. Social Networks, 2011, 19(3): 209-222.
[11] Albi G, Pareschi L, Zanella M. Opinion dynamics over complex networks: kinetic modelling and numerical methods[J]. Kinetic and Related Models, 2017, 10(1): 1-32.
[12] Castellano C, Fortunato S, Loreto V. Statistical physics of social dynamics[J]. Review of Modern Physics, 2007, 81(2): 591-646.
[13] Monica S, Bergenti F. Opinion dynamics in multi-agent systems: selected analytic models and verifying simulations[J]. Computational and Mathematical Organization Theory, 2017, 23(3): 423-450.
[14] Galam S, Gefen Y, Shapir Y. Sociophysics: a new approach of sociological collective behaviour. I. mean‐behaviour description of a strike[J]. Journal of Mathematical Sociology, 1982, 9(1): 1-13.
[15] Galam S, Moscovici S. Towards a theory of collective phenomena: consensus and attitude changes in groups[J]. European Journal of Social Psychology, 2010, 21(1): 49-74.
[16] Sznajd-weron K. Opinion evolution in closed community[J]. International Journal of Modern Physics C, 2000, 11(6): 1157-1165.
[17] Sznajd-weron K. Sznajd model and its applications[J]. Acta Physica Polonica, 2005, 36(8): 2537-2547.
[18] Galam S. Minority opinion spreading in random geometry[J]. The European Physical Journal B-Condensed Matter and Complex Systems, 2002, 25(4): 403-406.
[19] Zhu Y Y, Wang Q Q, Li W, et al. The formation of continuous opinion dynamics based on a gambling mechanism and its sensitivity analysis[J]. Journal of Statistical Mechanics-Theory and Experiment, 2017, 9(9): 093401.
[20] Krause U. A discrete nonlinear and non-autonomous model of consensus formation[C]∥Elaydi S, Ladas G, Popenda J, et al. Communications in Dierence Equations, Amsterdam: Gordon and Breach Science Publishers, 2000: 227-236.
[21] Hegselmann R, Krause U. Opinion dynamics and bounded confidence models, analysis and simulation[J]. Journal of Artificial Societies and Social Simulation, 2002, 5(3): 2.
[22] Weisbuch G, Deffuant G, Amblard F, et al. Meet, discuss, and segregate![J]. Complexity, 2017, 7(3): 55-63.
[23] Deffuant G, Neau D, Amblard F. Mixing beliefs among interacting agents[J]. Advances in Complex Systems, 2000, 3(1-4): 87-98.
[24] 赵奕奕, 寇纲, 彭怡, 等. 群体性突发事件中非一致信任水平舆论传播建模与分析[J]. 系统工程理论与实践, 2012, 32(5): 971-976.
Zhao Yiyi, Kou Gang, Peng Yi, et al. On modeling and analysis of opinion formation with heterogeneous confidence levels for emergences[J]. Systems Engineering-Theory & Practice, 2012, 32(5): 971-976.
[25] Mirtabatabaei A, Bullo F. opinion dynamics in heterogeneous networks: convergence conjectures and theorems[J]. Siam Journal on Control & Optimization, 2011, 50(5): 2763-2785.
[26] Shang Y L. An agent based model for opinion dynamics with random confidence threshold[J]. Communications in Nonlinear Science and Numerical Simulation, 2014, 19(10): 3766-3777.
[27] Wongkaew S, Caponigro M, Borzi A. On the control through leadership of the Hegselmann-Krause opinion formation model[J]. Mathematical Models & Methods in Applied Sciences, 2015, 25(3): 565-585.
[28] Shang Y. Deffuant model with general opinion distributions: first impression and critical confidence bound[J]. Complexity, 2013, 19(2): 38-49.
[29] Zhang J, Hong Y. Opinion evolution analysis for short-range and long-range Deffuant-Weisbuch models[J]. Physica A Statistical Mechanics & Its Applications, 2013, 392(21): 5289-5297.
[30] Li L, Scaglione A, Swami A, et al. Consensus, polarization and clustering of opinions in social networks[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(6): 1072-1083.
[31] Grauwin S, Jensen P. Opinion group formation and dynamics: structures that last from nonlasting entities[J]. Physical Review E Statistical Nonlinear & Soft Matter Physics, 2012, 85(2): 066113.
[32] Liu Q, Wang X. Social learning with bounded confidence and heterogeneous agents[J]. Physica A: Statistical Mechanics & Its Application, 2013, 392(10): 2303-2306.
[33] Kurmyshev E, Juárez H A, González-Silva R A. Dynamics of bounded confidence opinion in heterogeneous social networks: concord against partial antagonism[J]. Physica A Statistical Mechanics & Its Applications, 2011, 390(16): 2945-2955.
[34] 喻国明. 网络舆论的“后真相”辨析——以2018年年度争议新闻事件为例[J]. 新闻与写作, 2019, (5): 57-60.
Yu Guoming. Analysis of "post-truth" of online public opinion——take 2018 annual controversial news events as examples[J]. News and Writing, 2019, (5): 57-60.

Options

文章导航

模态框（Modal）标题

摘要

本文引用格式

Abstract

参考文献