Complex networks for event detection in heterogeneous high volume news streams
This addresses the need for automated real-time event detection in online news for applications like media monitoring, but it is incremental as it builds on existing network and NLP methods.
The paper tackles event detection in high-volume news streams by developing a network-based approach that tracks named entity co-occurrence networks over time, using change-point detection on node degree to locate events and community detection on KeyGraphs to characterize them, with promising results reported.
Detecting important events in high volume news streams is an important task for a variety of purposes.The volume and rate of online news increases the need for automated event detection methods thatcan operate in real time. In this paper we develop a network-based approach that makes the workingassumption that important news events always involve named entities (such as persons, locationsand organizations) that are linked in news articles. Our approach uses natural language processingtechniques to detect these entities in a stream of news articles and then creates a time-stamped seriesof networks in which the detected entities are linked by co-occurrence in articles and sentences. Inthis prototype, weighted node degree is tracked over time and change-point detection used to locateimportant events. Potential events are characterized and distinguished using community detectionon KeyGraphs that relate named entities and informative noun-phrases from related articles. Thismethodology already produces promising results and will be extended in future to include a widervariety of complex network analysis techniques.