A mean-field-theoretic model for dual information propagation in networks

Abstract

Abstract The Internet is a place where a vast amount of information is flowing. With the deeper penetration of social media, everybody is participating in spreading information. Often we find ourselves confused with competing information on the same topic. In this work, we present a novel model for competitive information diffusion on the scale-free network. The proposed model is an extension of the classical DK model of rumour spreading. Most of previous competitive information diffusion models consider a different type of stiflers to be similar. In our model we have two separate compartments for different types of stiflers. We present a detailed analysis about the effect of infection rate on the prevalence of rumour in the network. To capture the large chunk of population one requires relatively higher spreading rate. Relative impact of spreading rate and stifler rate on the final population in different compartments is also presented. In our analysis, we find that if stifler rate is higher than the spreading rate, a large portion of population remains unaware of rumours. We also find that if the information source is a popular person than people have a bias towards that information and information coming from less popular persons lose its grip on the network and lose the competition. This analysis illustrates that why big companies hire famous celebrities to promote their products. We also demonstrate rumour spreading analysis with numerical solution, network simulation and real network topology of Facebook.