Information Spread across Social Network Services with Users’ Information Indifference Behavior

This paper investigates the dynamics of information spread across social network services (SNSs) such as Twitter using the susceptible-infected-recovered (SIR) model. In most practical applications, an exact analytic solution is not available for the SIR model, so previous studies have largely been based on the assumption that the probability of a node having the target information is independent of whether or not its neighbors have that information. In contrast, we herein propose a different approach based on an assumption called the “strong correlation assumption”, in which the probability of a node having the target information is strongly correlated with whether its neighboring nodes have that information. We then analyze information spread for the SIR model and show that the use of the strong correlation assumption makes it possible to analyze the spread of information with far greater accuracy than the node independence assumption.