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| Social media such as Twitter has gained tremendous popularity in information dissemination. Modeling information spreading in online social networks has become a challenging problem. Most of dynamical models arising from social media only involve ordinary differential equations which describe static or collective social processes over time. Building on intuitive friendship hops in social media, we recently propose to use partial differential equation models to describe the temporal and spatial characteristics of information diffusion in online social networks. In this talk, I will examine a diffusive logistic equation based on cyber-distance and geocoded data in Twitter to model diffusion of health related information in Twitter. We demonstrate that it can be used to real-timely monitor spread of flu related information in social media, and help control spread of influenza. |
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