Three-Stage Cascade Information Attenuation for Opinion Dynamics in Social Networks

Abstract

In social network analysis, entropy quantifies the uncertainty or diversity of opinions, reflecting the complexity of opinion dynamics. To enhance the understanding of how opinions evolve, this study introduces a novel approach to modeling opinion dynamics in social networks by incorporating three-stage cascade information attenuation. Traditional models have often neglected the influence of second- and third-order neighbors and the attenuation of information as it propagates through a network. To correct this oversight, we redefine the interaction weights between individuals, taking into account the distance of opining, bounded confidence, and information attenuation. We propose two models of opinion dynamics using a three-stage cascade mechanism for information transmission, designed for environments with either a single or two subgroups of opinion leaders. These models capture the shifts in opinion distribution and entropy as information propagates and attenuates through the network. Through simulation experiments, we examine the ingredients influencing opinion dynamics. The results demonstrate that an increased presence of opinion leaders, coupled with a higher level of trust from their followers, significantly amplifies their influence. Furthermore, comparative experiments highlight the advantages of our proposed models, including rapid convergence, effective leadership influence, and robustness across different network structures.