Hypergraph generation python code.

As the number of users in online social networks increases, the diffusion of information and users’ opinions on events become more complex, making it difficult for traditional complex networks to accurately capture their characteristics and patterns. To address this, this paper proposes an online social network opinion evolution model that accounts for higher-order interactions. The model incorporates the higher-order effects of group interactions and introduces the acceptance, non-commitment, and rejection dimensions from social judgment theory. Different approaches, such as acceptance, neutrality, and contrastive rejection, are adopted when individuals exchange opinions with their neighbors. Through numerical simulations, it is shown that higher-order interactions significantly enhance the speed and coverage of information propagation. When the interaction dimensions are appropriate, increasing the average size of hyperedges significantly contributes to the formation of consensus. In contrast, simply increasing the number of hyperedges that nodes are involved in has a limited impact on consensus formation. This work provides a theoretical and model-based foundation for better understanding the dynamics of opinion evolution in social networks.