A Multi-layer Event Detection Algorithm for Detecting Global and Local Hot Events in Social Networks

Abstract In this paper, we present a new approach to detect global hot events and local hot events. Unlike previous event detection algorithms which do not distinguish between global events and local events, we believe it is important that we make that distinction as certain events can only be meaningful if they are placed in specific context while other events may arouse the interests of general users. The main contribution of this paper is that we’ve customized hot events detection by employing local community detection mechanisms and established a very clear concept for global hot events and local hot events. We present in this paper a multi-layer event detection algorithm which constructs a four-stage event detection procedure that produces a relatively comprehensive description of events relevant to the unique makeup and different interest of microblog users. Both the global hot events and local hot events we gathered are represented by a key tweet which contains sufficient information to depict a complete event. As a result of our algorithm's ability to precisely describe events which outperforms existing event detection algorithms, it is now possible for people to better understand public sentiment towards hot issues on microblogs. Experiments have shown that our multi-layer hot event detection algorithm can produce promising results in mining the interests of different communities, generating relevant event clusters and presenting meaningful events to community users. The most allround evaluation indicator F-value, which takes both precision and recall rate into account, has demonstrated that our algorithm outperforms the other three traditional approaches in detecting hot events.