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An opinion evolution model for online social networks considering higher-order interactions
Quan Liu1,2, Yuekang Yao1, Meimei Jia1
1School of Artificial Intelligence and Software Engineering, Nanyang Normal University, Nanyang, China.
Plos One
|April 16, 2025
Summary
This study introduces a new online social network model incorporating higher-order interactions to better understand opinion dynamics. Findings show these interactions speed up information spread and improve consensus formation, especially with larger group sizes.
Area of Science:
- Complex Networks
- Social Network Analysis
- Computational Social Science
Background:
- Traditional complex network models struggle to capture the intricate patterns of information and opinion diffusion in large online social networks.
- The increasing user base necessitates more sophisticated models to accurately represent network characteristics and dynamics.
Purpose of the Study:
- To propose an online social network opinion evolution model that integrates higher-order interactions.
- To analyze the impact of group interactions and social judgment theory dimensions on opinion dynamics.
- To investigate the influence of hyperedge characteristics on information propagation and consensus formation.
Main Methods:
- Development of an opinion evolution model incorporating higher-order interactions and social judgment theory dimensions (acceptance, non-commitment, rejection).
- Simulation of opinion exchange dynamics using acceptance, neutrality, and contrastive rejection strategies between neighbors.
- Numerical simulations to analyze the effects of hyperedge size and node involvement on information propagation and consensus.
Main Results:
- Higher-order interactions significantly accelerate the speed and broaden the coverage of information propagation.
- Increasing the average size of hyperedges effectively promotes consensus formation when interaction dimensions are optimized.
- Simply increasing the number of hyperedges nodes participate in has a limited effect on achieving consensus.
Conclusions:
- Higher-order interactions are crucial for accurately modeling opinion evolution in online social networks.
- The proposed model provides a theoretical and empirical foundation for understanding complex social dynamics.
- Optimizing group interaction structures, rather than just connectivity, is key for consensus building in online communities.
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