Microblogs Information Retrieval for Disaster Management

Abstract

This chapter proposes a new approach for microblog information retrieval during unexpected disasters. This approach consists of identifying prominent microblog users who are susceptible to share relevant and exclusive information during a specific disaster. By tracking these users, emergency first responders would benefit from a direct access to the valuable information shared in real time in microblogs. In order to identify such users, we represent each microblog user according to his behavior at each particular disaster phase. Through the proposed users' representation, different prediction models are learned in order to identify prominent users at an early stage of each disaster phase. We experimented with different user representations, taking into account both the microblog user behavior and disaster context specificities. We also analyzed the importance of the different microblog users' features categories according to the disaster phase context. The achieved experimental results show the efficiency of our phase-aware-user characterization approach.