Phase transition in infection scale and the impact of user characteristics under the herd effect in information propagation

Abstract

This study explores the phase transition phenomenon in information dissemination, focusing on how the herd effect influences the infection scale and the role of individual user characteristics in network propagation. First, we define two key components of the herd effect and analyze its impact on dissemination dynamics. Then, we investigate how individual user characteristics-user influence, cognitive level, and information demand affect the spread of information and construct a dynamic propagation model based on the SI propagation model. Through numerical simulations under various network conditions, we demonstrate that the herd effect can cause abrupt shifts in infection scale, significantly accelerating information propagation. Furthermore, we validate the model’s accuracy by comparing simulated data with the actual data using the Pearson correlation coefficient. The findings provide insights into the mechanisms driving rapid information spread and offer implications for controlling misinformation and optimizing information diffusion strategies.