GeSoDeck: a geo-social event detection and tracking system

Authors:
Xingyu Gao;Juan Cao;Zhiwei Jin;Xin Li;Jintao Li
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Department of Computer Science and Technology, Tsinghua University, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
Proceedings of the 21st ACM international conference on Multimedia
Year:
2013

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Abstract

This demonstration presents a novel geo-social event detection and tracking system based on geographical pattern mining and content analysis, called "GeSoDeck". A user can capture what events happened by our system. Unlike most existing social event detection applications, GeSoDeck aims to detect events with high accuracy and efficiency, and track them as well. Given a geographical area, the system can not only detect diverse social events in this area using the geographical pattern mining and density-based K-means clustering, but also track the representative tweets of the detected event in real time, mining geographical diffusion trajectory on the map and temporal pattern of retweeting process. On a realistic dataset collected from Sina Weibo, the system can outperform the state-of-the-art methods.