Work

Regret Psychology-Driven Information Propagation and Behavioral Adoption in Complex Social Networks

Year: 2025

Type: article

Abstract: In recent years, information propagation on social networks has attracted extensive attention, with psychological characteristics of individuals exerting a significant influence on the diffusion process. Our study investigates the role of regret psychology and its impact on information spreading and behavioral adoption. We categorize individuals into regretful and non-regretful groups and introduce regret intensity together with the proportion of regretful individuals as dynamic variables. Based on this, we construct a two-layer interactive model consisting of a psychological layer and a behavioral layer. Then we establish the behavioral adoption model for the heterogeneous population and study the propagation characteristics of the regretful individuals on social networks. Furthermore, we derive the propagation dynamics using edge-based compartmental theory to examine the transmission mechanism. Numerical simulations, which coincide nicely with our theoretical analyses, reveal the crossover phenomena in phase transitions: as the regret threshold increases, adoption dynamics shift from second-order continuous to first-order discontinuous transitions. More importantly, for a given propagation probability, there exists an optimal regret threshold that maximizes the final adoption size. These findings highlight the crucial role of regret psychology in reshaping the propagation mechanism and provide a new theoretical perspective for understanding symmetry transformations and group heterogeneity in social contagion dynamics.

Source: Symmetry

DOI: 10.3390/sym17112008

Authors: Nana Li, Yajuan Cui, Yang Tian, Chenxi Hu, Xuzhen Zhu, Simin Hu

Institutions: Zhengzhou University of Light Industry, Beijing Information Science & Technology University, Beijing University of Posts and Telecommunications, State Key Laboratory of Networking and Switching Technology

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: Zhengzhou University of Light Industry, Zhengzhou University, Henan Provincial Science and Technology Research Project