Information Spread across Social Network Services with Non-Responsiveness of Individual Users

This paper investigates the dynamics of information spread across social network services (SNSs) such as Twitter using the susceptible-infected-recovered (SIR) model. In the analysis, the non-responsiveness of individual users is taken into account; a user probabilistically spreads the received information, where not spreading (not responding) is equivalent to that the received information is not noticed. 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 an SNS user having the target information is independent of whether or not its neighbors have that information. In contrast, we propose a different approach based on a “strong correlation assumption”, in which the probability of an SNS user having the target information is strongly correlated with whether its neighboring users have that information. To account for the non-responsiveness of individual users, we also propose the “representative-response-based analysis”, in which some information spreading patterns are first obtained assuming representative response patterns of each user and then the results are averaged. Through simulation experiments, we show that the combination of this strong correlation assumption and the representative-response-based analysis makes it possible to analyze the spread of information with far greater accuracy than the traditional approach.