document: Earthquake shakes Twitter users : real-time event detection by social sensors Twitter , a popular microblogging service , has received much attention recently . An important characteristic of Twitter is its real-time nature . For example , when an earthquake occurs , people make many Twitter posts ( tweets ) related to the earthquake , which enables detection of earthquake occurrence promptly , simply by observing the tweets . As described in this paper , we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event . To detect a target event , we devise a classifier of tweets based on features such as the keywords in a tweet , the number of words , and their context . Subsequently , we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location . We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering , which are widely used for location estimation in ubiquitous\/pervasive computing . The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons . As an application , we construct an earthquake reporting system in Japan . Because of the numerous earthquakes and the large number of Twitter users throughout the country , we can detect an earthquake with high probability ( 96 % of earthquakes of Japan Meteorological Agency ( JMA ) seismic intensity scale 3 or more are detected ) merely by monitoring tweets . Our system detects earthquakes promptly and sends e-mails to registered users . Notification is delivered much faster than the announcements that are broadcast by the JMA .
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