On a keyword-lifecycle model for real-time event detection in social network data

Social networks like Twitter and Facebook have gained a significant popularity with people from all parts of the society in the past decade, providing a new kind of data source for novel social-aware applications. A great majority of the users are online all the time, posting real-time information on various topics including unpredicted events. An accident or a natural disaster is often posted on social networks hours before appearing in traditional news. In this paper, we outline a framework for real-time event detection in Twitter data. In contrast to prior works where the absolute or relative changes in the frequencies of some predefined keywords are taken into account, we introduce a lifecycle for each keyword to be observed, expressing their average behavior (e.g. average frequency changes) over time. As a motivation, we show that some keywords exhibit periodic behavior that can be handled by our model. The proposed lifecycle model enables us to define novel temporal features used by our framework in real-time event detection.