A Information Propagation Model Based on Various Emotions and Heterogeneous Mean Field in Social Networks

Information propagation in social networks can be effected by various factors, such as connected relationships, interactions between users, and so on. Previous studies mainly focused on analyzing the impact of physical connections on information propagation, and rarely studied the effects of different emotions on information propagation. Aiming to solve this problem, this paper proposes an emotion-based susceptible-infected-recovered information propagtion model(E-SIR). The model primarily researches the impacts of different emotions and diverse connections on the information propagtion process. We introduced the emotional transmissibility and the information transmissibility to describe the infection abilities of different emotions and the possibility of information transmission between different users,respectively. In addition, the dynamic equations are established based on the heterogeneous mean field. The burst threshold and spreading scale are theoretically analyzed and verified by experiments. In general, this paper focuses on the impacts of various emotions and different connections on information propagation.