Identification of Microblogs Prominent Users during Events by Learning Temporal Sequences of Features.

During specific real-world events, some users of microblogging platforms could provide exclusive information about those events. The identification of such prominent users depends on several factors such as the freshness and the relevance of their shared information. This work proposes a probabilistic model for the identification of prominent users in microblogs during specific events. The model is based on learning and classifying user behavior over time using Mixture of Gaussians Hidden Markov Models. A user is characterized by a temporal sequence of feature vectors describing his activities. The features computed at each time-stamp are designed to reflect both the on- and off-topic activities of users, and they are computationally feasible in real-time. To validate the efficacy of our proposed model, we have conducted experiments on data collected from Twitter during the Herault floods that have occurred in France. The achieved results show that learning the time-series of users' actions is better than learning just those actions without temporal information.