Discovery and tracking of temporal topics of interest based on belief-function and aging theories
Résumé
Content-based microblogging networks like Twitter have a significant and fast evolution of the information amount. In addition to textual content and member activities, analyzing structural information concerning user neighborhood and communities is an opportunity for several applications such as user interest modeling. In this context, this work proposes a new approach for temporal topics of interest derivation and tracking in the Twitter micro-blogging network. The challenge of this approach is to take into consideration the different dimensions of the user’s interest such as personal, social and temporal aspect. We propose a new definition of an ego network that incorporates friendship reliability and dependency between members. The belief function theory that allows combining evidence from dependent or independent sources in an uncertain context is introduced. This theory has been widely utilized in various decision-making systems. To model the topics of interest evolution, we introduce the aging theory suggested mainly for event life-cycle detection and modeling in a micro-blogging network. We present an experimental study performed on the actual Twitter users, validating our approach. This model is compared to other methods using the same database.