Abstract
The volume of data keeps growing rapidly, especially with the arrival and the frequent access to social networks. The spread of these networks provides users the opportunity to share their social, geographical and temporal information through geo-localized tweets and check-ins. The challenge is to exploit these data leads to a decision in favour of different situations encountered by these users. Thus, if we successfully analyze their trends according to the models of users' movements, we can then draw conclusions about the evolution of their instantaneous behavior and accomplished activities. But, the problem is that the use of such data decrees the provision of a representative formalism that combines spatial data and user information. In this paper, we propose an approach for a semantic modeling of social network users' trajectories. To do so, ontology seems to be a promising solution that allows us to annotate raw trajectories with semantic information to give birth to semantic trajectories. Such semantic trajectories are then analyzed in order to detect user behavior in a dynamic way.