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Opinion evolution in different social acquaintance networks.

Xi Chen1, Xiao Zhang1, Zhan Wu1

  • 1School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China.

Chaos (Woodbury, N.Y.)
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Summary

Social culture and policy shape opinion dynamics. Different social networks, like kinship-priority and hybrid, influence how public opinion evolves, with hybrid networks promoting consensus. This research offers insights into predicting opinion shifts and guiding public discourse.

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Area of Science:

  • Computational Social Science
  • Sociology
  • Network Science

Background:

  • Social acquaintance networks, shaped by culture and policy, significantly impact public opinion evolution.
  • Understanding how different network structures influence opinion dynamics is crucial for social analysis.

Purpose of the Study:

  • To propose and analyze three distinct social acquaintance network models: kinship-priority, independence-priority, and hybrid.
  • To investigate the effects of heredity (p_h) and variation (p_v) proportions on opinion evolution within these networks.
  • To demonstrate the applicability of network models incorporating socio-cultural and policy factors.

Main Methods:

  • Development of three social acquaintance network models (kinship-priority, independence-priority, hybrid) with heredity (p_h) and variation (p_v) parameters.
  • Utilization of the Deffuant model for numerical experiments on opinion evolution.
  • Analysis of network topology and opinion cluster formation under different network conditions.

Main Results:

  • Kinship-priority and independence-priority networks lead to opinion fragmentation, forming multiple clusters.
  • Hybrid networks show a tendency towards large-scale consensus, with fewer opinion clusters.
  • A threshold curve (p_v + 2p_h = 2.05) was identified, above which consensus is more easily achieved.

Conclusions:

  • Social culture and policy significantly drive opinion dynamics through interpersonal relationship networks.
  • The proposed network models accurately reflect real-world opinion evolution phenomena.
  • Findings are valuable for predicting opinion shifts and formulating culturally sensitive policies.