基于观点差异敏感性和意见领袖信任度的观点动力学分析

doi:10.11896/jsjkx.250100007

摘要/Abstract

摘要： 在社会网络中,个体属性对群体观点演化起着重要的作用,为了深入理解这一现象,基于传统的HK(Hegselmann-Krause)模型,引入个体对观点差异的敏感性和个体对意见领袖的信任度,提出一种新的观点演化模型。个体对观点差异的敏感性是指个体在更新自己的观点时,对其他个体观点差异的敏感程度。这种敏感性通过一个敏感性系数来量化,系数越高,表明个体越倾向于与自己观点接近的其他个体进行交流和互动。这种机制可能导致观点的极化,因为个体更可能与观点相似的人交流,从而加强已有的观点。个体对意见领袖的信任度描述了个体在形成观点时对意见领袖的依赖程度,在模型中,每个个体可能以不同的信任度接受意见领袖的观点影响。首先对模型进行简要理论分析,通过在无标度网络中的仿真模拟,探讨这两种属性对观点演化的影响。研究结果表明,个体对观点差异越敏感,观点值的发散程度越大,收敛时间增长。个体对意见领袖的信任度越高,群体观点会越快趋向意见领袖的观点。随后增加意见领袖数量,构建包含两个意见领袖的改进HK模型,通过仿真实验,分析接收到意见领袖观点的个体比例以及个体对意见领袖的信任度对观点演化的影响。实验结果表明,个体对意见领袖的信任度越高,群体观点越容易向意见领袖的观点靠拢,且群体观点的收敛速度更快。同时,接收意见领袖观点的个体比例越高,群体观点的演化过程越容易受到意见领袖观点的主导,群体观点的最终稳定状态也更接近意见领袖的观点。

关键词: 观点动力学, Hegselmann-Krause模型, 观点差异, 个体敏感性, 意见领袖

Abstract: In social networks,individual attributes exert a pivotal influence on the evolution of group opinions.To delve into this phenomenon,this paper extends the traditional Hegselmann-Krause(HK) model by incorporating two parameters:individual sensitivity to opinion divergence and the degree of trust in opinion leaders,proposes a new model for opinion dynamics.Individual sensitivity to opinion disparity refers to the degree to which individuals are sensitive to the opinion differences of others when updating their own views.This sensitivity is quantified by a sensitivity coefficient,with a higher coefficient indicating a greater propensity for individuals to communicate and interact with others whose views are close to their own.Such a mechanism may precipitate opinion polarization,as individuals are more inclined to interact with those sharing similar perspectives,thereby reinforcing their existing opinions.The trust individuals place in opinion leaders describe the degree to which individuals rely on opinion leaders when forming their opinions.In the model,each individual may accept the influence of opinion leaders’ opinions with different levels of trust.The paper first conducts a brief theoretical analysis of the model and then explores the impact of these two attributes on opinion evolution through simulation experiments in scale-free networks.The results show that the higher the sensitivity of individuals to opinion differences,the greater the divergence of opinion values and the longer the convergence time.The higher the trust individuals place in opinion leaders,the faster the group opinions will converge towards the opinions of the opi-nion leaders.Subsequently,the paper increases the number of opinion leaders and constructs an improved HK model with two opinion leaders.Through simulation experiments,the paper analyzes the impact of the proportion of individuals receiving opinions from opinion leaders and the trust individuals place in opinion leaders on opinion evolution.The experimental results indicate that the higher the trust individuals place in opinion leaders,the more easily the group opinions will align with the opinions of the opinion leaders,and the faster the convergence speed of group opinions.Meanwhile,the higher the proportion of individuals recei-ving opinions from opinion leaders,the more easily the evolution process of group opinions will be dominated by the opinions of the opinion leaders,and the final stable state of group opinions will be closer to the opinions of the opinion leaders.

Key words: Opinion dynamics, Hegselmann-Krause model, Opinion divergence, Individual sensitivity, Opinion leaders

中图分类号:

TP391

张维婧, 高彦平. 基于观点差异敏感性和意见领袖信任度的观点动力学分析[J]. 计算机科学, 2025, 52(11A): 250100007-9. https://doi.org/10.11896/jsjkx.250100007

ZHANG Weijing, GAO Yanping. Analysis of Opinion Dynamics Based on Sensitivity to Opinion Disparity and Trust in Opinion Leaders[J]. Computer Science, 2025, 52(11A): 250100007-9. https://doi.org/10.11896/jsjkx.250100007

参考文献

[1]ACEMOGLU D,OZDAGLAR A.Opinion Dynamics and Lear-ning in Social Networks[J].Dynamic Games and Applications,2011,1(1):3-49.
[2]GALAM S.Minority Opinion Spreading in Random Geometry[J].European Physical Journal B,2002,25:403-406.
[3]SZNAJD-WERON K,SZNAJD J.Opinion Evolution in Closed Community[J].HSC Research Reports,2000,11(6):1157-1165.
[4]SOOD V,REDNER S.Voter Model on Heterogeneous Graphs[J].Physical Review Letters,2005,94(17):178701.
[5]DEGROOT M H.Reaching a Consensus[J].Journal of theAmerican Statistical Association,1974,69(345):118-121.
[6]DEFFUANT G,NEAU D,AMBLARD F,Weisbuch G.Mixing Beliefs Among Interacting Agents[J].Advances in Complex Systems,2000,3(1):87-98.
[7]HEGSELMANN R,KRAUSE U.Opinion Dynamics and Boun-ded Confidence:Models,Analysis and Simulation[J].Journal of Artificial Societies and Social Simulation,2002,5(3):1-33.
[8]MEI W,BULLO F,CHEN G,et al.Micro-Foundation of Opi-nion Dynamics:Rich Consequences of the Weighted-Median Mechanism[J].Physical Review Research,2022,4(2):023213.
[9]LI G J,PORTER M A.Bounded-Confidence Model of Opinion Dynamics with Heterogeneous Node-Activity Levels[J].Physical Review Research,2023,5(2):023179.
[10]ZHANG S Q,LIU B,CHAI L.Improved Hegselmann-Krauseopinion dynamics model based on conformity behavior[J].Control and Decision,2024,39(3):965-974.
[11]CHEN Y X,CHEN X Y,LV Y.Research on Public Opinion Reversal on Micro Blog Based on Improved Hegselmann-Krause Model[J].Information studies:Theory & Application,2020,43(1):82-89.
[12]XI X M,LIU Q S,CHAI L.Opinion Dynamics Analysis of Nucleus Hegselmann-Krause Model in Social Networks[J].IFAC-PapersOnLine,2022,55(3):25-30.
[13]WANG Y Q,YOU X Q,JIAO J,et al.Opinion adoption:reasoning based on self and individual differences[J].Acta Psychologica Sinica,2015,47(8):1039-1049.
[14]HE C,ZENG J,ZHANG G,et al.Generalized Opinion Dynamics Model for Social Trust Networks[J].Journal of Combinatorial Optimization,2022,44:3641-3662.
[15]BAMAKAN S,NURGALIEV I,QU Q.Opinion Leader Detection:A Methodological Review[J].Expert Systems with Applications,2019,115:200-222.
[16]QIU Z J.The impact of opinion leader characteristics in short videos on consumer trust and participation[J].Frontiers of Social Sciences,2020,9(8):1235-1245.
[17]KANG M,LIANG T,SUN B,et al.Detection of Opinion Lea-ders:Static vs.Dynamic Evaluation in Online Learning Communities[J].Heliyon,2023,9(4):e14844.
[18]HE J J,ZHANG Y N.Group opinion evolution model based on relationship degree and simulation[J].Application Research of Computers,2017,34(6):5.
[19]CHI Y X,LIU Y J.Research on the mechanism of online public opinion communication under reverse psychology[J].Systems Engineering and Engineering Management,2019,34(5):610-620.
[20]CALVELLI M,CROKIDAKIS N,PENNA T J P.Phase Transitions and Universality in theSznajd Model with Anticonformity[J].Physica A:Statistical Mechanics and Its Applications,2019,513:518-523.
[21]CHEN T,WANG Y,YANG J,et al.Modeling Public Opinion Reversal Process with the Considerations of External Intervention Information and Individual Internal Characteristics[J].Healthcare,2020,8(2):160.
[22]LIU Q S,XI X M,CHAI L.Analysis and application of bounded confidence opinion dynamics with gravitational-like attraction[J].Acta Automatica Sinica,2023,49(9):1967-1975.
[23]BARABÁSI A L,ALBERT R.Emergence of Scaling in Random Networks[J].Science,1999,286(5439):509-512.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed