  Online social networks play a major role in the spread of information at very
large scale and it becomes essential to provide means to analyse this
phenomenon. In this paper we address the issue of predicting the temporal
dynamics of the information diffusion process. We develop a graph-based
approach built on the assumption that the macroscopic dynamics of the spreading
process are explained by the topology of the network and the interactions that
occur through it, between pairs of users, on the basis of properties at the
microscopic level. We introduce a generic model, called T-BaSIC, and describe
how to estimate its parameters from users behaviours using machine learning
techniques. Contrary to classical approaches where the parameters are fixed in
advance, T-BaSIC's parameters are functions depending of time, which permit to
better approximate and adapt to the diffusion phenomenon observed in online
social networks. Our proposal has been validated on real Twitter datasets.
Experiments show that our approach is able to capture the particular patterns
of diffusion depending of the studied sub-networks of users and topics. The
results corroborate the "two-step" theory (1955) that states that information
flows from media to a few "opinion leaders" who then transfer it to the mass
population via social networks and show that it applies in the online context.
This work also highlights interesting recommendations for future
investigations.
