Diffusion prediction of competitive information with time-varying attractiveness in social networks

The ubiquity of social media has facilitated the simultaneous dissemination of large-scale information within online social networks. By assuming that information attractiveness is static, numerous studies have been devoted to the analysis of multiple information dissemination. However, real-world information attractiveness often exhibits variations throughout the dissemination process. This paper delves into the study of how time-varying information attractiveness influences the diffusion process, particularly in the context of competitive information dissemination regarding multiple products. First, we propose a Markov multi-information diffusion model, incorporating three critical parameters: the attractiveness degree, the information-boom time, and the information prosperity index, to address the dynamic nature of attractiveness. The basic reproduction number is derived, and the accuracy of the proposed model is verified. Furthermore, our numerical simulation results illustrate that both the maximal attractiveness value and the information prosperity duration significantly enhance information competitiveness, while delayed information boom time may undermine this competitiveness. In addition, it is indicated that improving the peak attractiveness is the key for time-varying information to achieve a better spreading effect. Moreover, we find that the growth of information attractiveness can even mitigate the impact of blocking the spread caused by information discarding behavior, highlighting intrinsic quality as the paramount determinant of information promotion outcomes.