Avalanche Dynamics of Multi-Information Propagation Modelling

Abstract

Abstract With the rapid development of social network information technology, some information can spread in a wide range while others are difficult to diffuse and quickly vanish. Previous researches have carried out some empirical analysis and theoretical methods on the information dissemination such as news and rumours, but few of them study multiple information dissemination process based on empirical data. Here, we study the online true and false information diffusion based on a Twitter dataset. We analyse their difference on size, depth, annual generation and annual transmission, and find that the spreading ability of false information is significantly stronger than that of true information. Then, we propose a novel multi-information propagation model, which considers the probability of information generation and transmission. With limited attention of users, different true and false information will compete for attention and ultimately lead to the heterogeneity of information size. Extensive numerical simulations demonstrate that the competition of these two information will transit from insufficient state to sufficient state, which will introduce the criticality of the information dissemination system. Furthermore, we study the impact of information generation and transmission probability to the spreading ability, and find that they can significantly affect the distribution of information size, which is consistent with the empirical results. This work helps to understand the dissemination characteristics of the true and false information on online social networks and would promote further studies on the critical phenomenon of information dissemination.