Information Diffusion Model Based On Privacy Setting In Online Social Networking Services

Social networking service(SNS) is one of the major technological applications based on Web 2.0,which can help users to express their views and share information with others. How to describe the information diffusion process in online SNS accurately has attracted considerable interest recently.However, almost all existing models focus on a single online SNS tool and face many challenges with multiple online SNS tools, And due to users' privacy preservation in online SNS tools, many privacy setting tools are employed, which inevitably change the patterns and dynamics of information dissemination. In this paper, we turn to users' privacy setting policies and propose a general stochastic model with multiple diffusion mechanisms in online SNS.Specifically, we first define a privacy protection mechanism based on information sharing in online SNS and classify nodes according to different privacy setting policies; then, we define the states of the nodes(susceptible state,receviced state,accepted state,immunized state,disseminated state,public disseminated state and privacy disseminated state)and information dissemination rules with dynamics of infectious disease; finally, we describe the evolution process of different nodes by dynamic evolution equations. On one hand, we analyse from privacy setting policies of source node, privacy setting polices in dissemination, the credibility of information and user's habits. Detailed simulations and analysis show that the models can precisely describe the diffusion process with multiple diffusion mechanisms and have the same characteristics as a diffusion process in real online SNS. On the other hand, extensive simulations and analyses in respective privacy settings of general users, privileged users and pure observers also are conducted with real-world network, and the results demonstrate that users' privacy settings can impact information differently.As a result, the models can be used to identify the underlying diffusion mechanism of information and forecast its trend in online SNS.