Event detection from social network streams using frequent pattern mining with dynamic support values

Detecting events from streams of data is challenging due to the characteristics of such streams: data elements arrive in real-time and at high velocity, and the size of the streams is typically unbounded while it is not possible to backtrack over past data elements or maintain and review the entire history. Social networks are a good source for event identification as they generate huge amount of timely information representing what users are posting and discussing. In this research, we are developing methods for event detection from streams of data. More specifically, we are presenting a framework for detecting the daily occurring events or topics occurring in social network streams related to major events. Our approach utilizes the Frequent Pattern Mining method to detect the daily occurring frequent patterns, which are going to be our detected events. In addition, we propose a dynamic support definition method to replace the fixed given one. An experiment was run on two streams relating to two different major events to examine the detected events and to test our support definition method. The UK General Elections 2015 stream holds more than one million tweets, and the Greece Crisis 2015 stream contains more than 150k tweets. The detected events were evaluated against news headlines published the same day the event was found. The results revealed that the higher the streaming level (bigger window size), the more accurate the detected events. We also show that for too small sized windows, a more strict support definition method is needed to avoid detecting false or insignificant events.