Agent‐based modeling approach for group polarization behavior considering conformity and network relationship strength

Zhang, Yizhou; Wang, Yibao; Chen, Tinggui; Shi, Jiawen

doi:10.1002/cpe.5707

Cited by 6 publications

(6 citation statements)

References 41 publications

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“…Compared with the research of traditional group behavior, group behavior in online social networks has various forms and spread more widely. The research on group behavior mainly includes group behavior identification, 14,15 group behavior modeling and prediction, [16][17][18] social network retweeting behavior prediction. [19][20][21] An important content of behavior research is the spread characteristics of behavior.…”

Section: Related Workmentioning

confidence: 99%

Modeling social network behavior spread based on group cohesion under uncertain environment

Deng

Jin

et al. 2022

Concurrency and Computation

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Behavior is autonomous, convergent, and uncertain, which brings challenges to the modeling of social network behavior spread. In this article, we propose a behavior spread model based on group cohesion under uncertain environments. First, for behavioral convergence, we define group cohesion to quantify the convergent effects of group. Second, based on the game theory to model the autonomy of behavior, according to the characteristics of the game payoffs changing with time and the depth of spread, and integrating group cohesion, a dynamic game payoffs calculation method is designed. Finally, aiming at the uncertainty of behavior, a group behavior spread model based on random utility theory is established. Experiments on multiple real social network behavior spread datasets demonstrate the effectiveness of the proposed model in modeling and predicting behavior spread processes under uncertain environments.

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Section: Related Workmentioning

confidence: 99%

Modeling social network behavior spread based on group cohesion under uncertain environment

Deng

Jin

et al. 2022

Concurrency and Computation

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“…ey found that extreme information results in segregation and had a limited effect on the population, while milder messages have better success and a cohesion effect. Zhang et al [33] integrated the influence factors such as conformity and network relationship strength into the polarization model, and the simulation results indicated that different information interaction modes had essential influence on group attitude polarization. However, in their study, the information propagation process of the polarization phenomenon was not considered.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Public Opinion Polarization in Group Behavior by Integrating SIRS-Based Information Diffusion Process

et al. 2020

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Currently, China is in the period of social transformation. Such transformation continuously results in high group polarization behaviors, which attracts many attentions. In order to explore the evolutionary mechanism and formation process of group polarization behavior, this paper proposes a group polarization model which is integrated into the Susceptible-Infected-Recovered-Susceptible (SIRS) epidemic model. In this paper, firstly, the SIRS epidemic model and the factors of relationship strength are introduced based on the J-A model (proposed by Jager and Amblard) to enhance the information transmission and interaction among individuals. In addition, the BA network (proposed by Barabasi and Albert) model is used as the agent adjacency model due to its closeness to the real social network structure. After that, the Monte Carlo method is applied to conduct experimental simulation. Subsequently, this paper analyzes the simulation results in threefold: (1) comparison of polarization processes with and without integration of the SIRS epidemic model; (2) adjusting the immune recovery parameter γ and the relationship strength z to explore the role of these two parameters in the polarization process; and (3) comparing the polarization effects of different network structures. Through the experiments, we find that BA network is more polarized than small-world network in the same scale. Finally, corresponding measures are proposed to prevent and mitigate the occurrence of group polarization.

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“…Meanwhile, the evolution of opinions on different dimensions shows relevance under specific conditions. Zhang et al extended the factors such as conformity and relationship strength to the polarization model, and analyzed the main factors influencing group polarization. Chen et al considered the heterogeneity and dynamic consistency of individuals involved in the polarization of public opinion.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling and simulation analysis of public opinion polarization in a dynamic network environment

Yuan

Shi

Che

et al. 2020

Concurrency and Computation

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Summary At present, there are many group incidents on the Internet, which has caused a high level of public opinion. Hot issues on the Internet can often trigger intense discussions among netizens and finally evolve toward bipolarization or multipolarization. This article focuses on the phenomenon of public opinion polarization under the dynamic network. First, the single‐dimensional evaluation system for the JA model was expanded to multidimensional and analyzes the main factors that cause the polarization of public opinion of network groups from multiple dimensions of hot events. Second, the strength of the relationship between individuals and its dynamic changes were taken into account in the model. The strength of the relationship between individuals and its changes will affect the interaction of opinions, and thus affect the degree and speed of group polarization. Third, combining the changing process of the strength of relationship, the dynamic connection mechanism of network nodes was set to make the network structure more consistent with the social network in real life. Finally, we verify the reliability and scientificity of the model by combining practical cases.

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Agent‐based modeling approach for group polarization behavior considering conformity and network relationship strength

Cited by 6 publications

References 41 publications

Modeling social network behavior spread based on group cohesion under uncertain environment

Modeling social network behavior spread based on group cohesion under uncertain environment

Modeling Public Opinion Polarization in Group Behavior by Integrating SIRS-Based Information Diffusion Process

Modeling and simulation analysis of public opinion polarization in a dynamic network environment

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