Adjustable Time-Window-Based Event Detection On Twitter

Twitter has become an important platform for reporting breaking news and instant events. However, it is almost impossible to detect events on Twitter manually due to the large volume of data and the noise in them. Though automatic event detection has been studied a lot, most works can only detect events in a fixed time window. In this paper, we propose an efficient system that can detect events in adjustable time windows. We detect terms with unusual frequency and group them into events. We further modify a segment tree data structure to support adjustable time window based event detection, which can efficiently aggregate statistics of terms of varied-sized time windows and is both space and time saving. We finally validate the effectiveness and efficiency of our proposed techniques through extensive experiments on real datasets.