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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.

Twitter is a popular microblogging service that acts as a ground-level information news flashes portal where people with different background, age, and social condition provide information about what is happening in front of their eyes. This characteristic makes Twitter probably the fastest information service in the world. In this article, we recognize this role of Twitter and propose a novel, user-aware topic detection technique that permits to retrieve, in real time, the most emerging topics of discussion expressed by the community within the interests of specific users. First, we analyze the topology of Twitter looking at how the information spreads over the network, taking into account the authority/influence of each active user. Then, we make use of a novel term aging model to compute the burstiness of each term, and provide a graph-based method to retrieve the minimal set of terms that can represent the corresponding topic. Finally, since any user can have topic preferences inferable from the shared content, we leverage such knowledge to highlight the most emerging topics within her foci of interest. As evaluation we then provide several experiments together with a user study proving the validity and reliability of the proposed approach.