Abstract
With the proliferation of social media data reporting on all aspects of human activity, being able to automatically identify events is becoming increasingly important. Identifying events from data streams is very challenging, as those streams are of unbounded size and their data elements arrive in real-time and at an unpredictable rate. Even more, it is not possible to backtrack over past arrived data elements or maintain and review the entire stream history. In this paper, we present an approach for detecting the daily occurring events in social network streams related to major events using a Frequent Pattern Mining method. In addition, we introduce dynamic support definition method to replace the fixed, a priori given one. As the number of text posts streamed each day is not fixed, a dynamic support, can adapt to the nature of data streams and can improve the identification of events. Experiments were employed on two streams related to two different major events. The first stream was related to the UK General Elections 2015, with more than 1.1M tweets, and the other was related to the Greece Crisis 2015, with more than 150K tweets. Results showed that using a daily dynamic defined support can better identify events from social network streams rather than fixed ones, as it can cope with the dynamic and changing aspect of data streams.