Information evolution modeling and tracking in social media

Nowadays, User Generated Content is the main source of real time news and opinions on the world happenings. Social Media, which serves as an environment for the creation and spreading of User Generated Content, is, therefore, representative of our culture and constitutes a potential treasury of knowledge. In this paper we propose a fully automatic approach for modeling and tracking the information evolution in Social Media. In particular, we propose to model a Social Media stream as a text graph. A graph degeneracy technique is used to identify the temporal sequence of the core units of information streams represented by graphs. Furthermore, as the major novelty of this work, we propose a set of measures to track and evaluate the evolution of information in time. An experimental evaluation on the crawled datasets from one of the most popular Social Media platforms proves the validity and applicability of the proposed approach.