Understanding of diffusion dynamics in networks has high importance for different purposes like politics, marketing, influencing and rumor spreading. In this study, we examined information spreading dynamics in real social network data. We used a widely known epidemic model, SIR, and Fermi distribution function. We tested these parameters with social network dynamics such as active times in social networks, information attractiveness, etc. The study showed that high infection with a low recovery rate can cause an infected network. However, we observed that the network tends to recover most of the time.
