GeoBurst: Real-Time Local Event Detection in Geo-Tagged Tweet Streams Chao Zhang1, Guangyu Zhou1, Quan Yuan1, Honglei Zhuang1, Yu Zheng2;3, Lance Kaplan4, Shaowen Wang1, and Jiawei Han1 1Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA 2Microsoft Research, Beijing, China 3Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 4U.S. Army Research Laboratory, Adelphi, MD, USA 1{czhang82, gzhou6, qyuan, hzhuang3, shaowen, hanj}@illinois.edu
[email protected] [email protected] ABSTRACT to the lack of timely and reliable data sources, yet the recent ex- The real-time discovery of local events (e.g., protests, crimes, dis- plosive growth in geo-tagged tweet data brings new opportunities asters) is of great importance to various applications, such as crime to it. With the ubiquitous connectivity of wireless networks and monitoring, disaster alarming, and activity recommendation. While the wide proliferation of mobile devices, more than 10 million geo- this task was nearly impossible years ago due to the lack of timely tagged tweets are created in the Twitterverse every day [1]. Each and reliable data sources, the recent explosive growth in geo-tagged geo-tagged tweet, which contains a text message, a timestamp, and tweet data brings new opportunities to it. That said, how to extract a geo-location, provides a unified 3W (what, when, and where) quality local events from geo-tagged tweet streams in real time re- view of the user’s activity. Even though the geo-tagged tweets ac- mains largely unsolved so far. count for only about 2% of the entire tweet stream [16], their sheer size, multi-faceted information, and real-time nature make them an We propose GEOBURST, a method that enables effective and real-time local event detection from geo-tagged tweet streams.