Migrating Social Event Recommendation Over Microblogs

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Abstract

Real applications like crisis management require the real time awareness of the critical situations. However, the services using traditional methods like phone calls can be easily delayed due to busy lines, transfer delays or limited communication ability in disaster areas. Existing social event analysis solutions enhanced the situation awareness of systems. Unfortunately, they cannot recognize the complex migrating social events that are first observed in social media at a specific time, place and state, but have further moved in space and time, which may affect the comprehension of the system. While the discussion on events appears in microblogs, their movement over different contexts is unavoidable. So far, the problem of migrating social event analysis from big media is not well investigated yet. To address this issue, we propose a novel framework to monitor and deliver the migrating events in big social media data, which fully exploits the information of social media over multiple attributes and their inherent interactions among events. Specifically, we first propose a Concept TF/IDF model to capture the content that is constrained by the time and location of social posts without costly learning process. Then, we construct a novel Maximal User Influence Graph (MUIG) to extract the social interactions. With MUIG, the event migrations over space and time are well identified. Finally, we design efficient query strategies over Apache Spark for recommending events in real time. Extensive tests over big media are conducted to prove the high effectiveness and efficiency of our approach.