Microblogs have proved their potential to attract people from all over the world to express voluntarily what is happening around them during unexpected events. However, retrieving relevant information from the huge amount of data shared in real time in these microblogs remain complex. This paper proposes a new system named MASIR for real-time information retrieval from microblogs during unexpected events. MASIR is based on a decentralized and collaborative multi-agent approach analyzing the profiles of users interested in a given event in order to detect the most prominent ones that have to be tracked in real time. Real time monitoring of these users enables a direct access to valuable fresh information. Our experiments shows that MASIR simplifies the real-time detection and tracking of the most prominent users by exploring both the old and fresh information shared during the event and outperforms the standard centrality measures by using a time-sensitive ranking model.