Social networks and microblogging systems play a fundamental role in the diffusion of information. The information, from different sources, reaches each user through multiple connections, the study of which is indispensable for the sake of understanding the dynamics of its evolution and expansion. In this paper, we propose a system which enables to delve in the spread of information over a network along with the changes in the user relationships with respect to the domain of discussion. To cope up with the goal, considering Twitter as a case study, we analyse the tweets as the starting point or as the generators of the information which later flows through subsequent retweets. Furthermore, we integrate a N-Gram model classification approach for categorizing, under various domains, the information shared within the social network under consideration. We finally leverage this formalization to propose a domain-based model which aims to estimate the influence of a user, on a community, in the domain under consideration. In conclusion, using a sample of the Twitter network we then present a set of case studies and real case scenarios that show the validity of the proposed approach.