Feedback-driven model of dynamic information spreading in online social networks

Abstract

This paper constructs a spreading model driven by positive and negative feedback interactions to study the impact of inter-individual feedback interaction on message spreading in online social networks. By analysing the network topology and the social attributes of the propagators, we introduce feedback interaction rules. In addition, the individual’s spreading motivation comes from the direct feedback interactions of multilevel neighbours and the group-level pressure of the social environment. Therefore, we established a feedback efficiency function. Experimental results show that the model is effective in promoting the positive spread of messages. Meanwhile, it is found that the increased social distance between individuals weakens the feedback strength, the group feedback rate enhances the expected spreading ability of the upline nodes and the group feedback has a more significant incentive effect than the neighbour feedback. Finally, the reasonableness of the model is verified by comparing with real cases.