  Social networks are an important infrastructure for information, viruses and
innovations propagation. Since users behavior has influenced by other users
activity, some groups of people would be made regard to similarity of users
interests. On the other hand, dealing with many events in real worlds, can be
justified in social networks; spreading disease is one instance of them. People
manner and infection severity are more important parameters in dissemination of
diseases. Both of these reasons derive, whether the diffusion leads to an
epidemic or not. SIRS is a hybrid model of SIR and SIS disease models to spread
contamination. A person in this model can be returned to susceptible state
after it removed. According to communities which are established on the social
network, we use the compartmental type of SIRS model. During this paper, a
general compartmental information diffusion model would be proposed and
extracted some of the beneficial parameters to analyze our model. To adapt our
model to realistic behaviors, we use Markovian model, which would be helpful to
create a stochastic manner of the proposed model. In the case of random model,
we can calculate probabilities of transaction between states and predicting
value of each state. The comparison between two mode of the model shows that,
the prediction of population would be verified in each state.
