TAILIEUCHUNG - Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors

Given the new unusual and usual event models, both adapted from the general usual event model, the HMM topology is changed with one more state. Hence the cur- rent HMM has 2 states, one representing the usual events and one representing the first detected unusual event. The Viterbi algorithm is then used to find the best possible state sequence which could have emitted the observation sequence, according to the maximum likelihood (ML) cri- terion (Figure 2, step 3). Transition points, which define new segments, are detected using the current HMM topol- ogy and parameters. A new outlier is now identified by sorting the likelihood of all segments given the usual event model. | Earthquake Shakes Twitter Users Real-time Event Detection by Social Sensors Takeshi Sakaki The University of Tokyo Yayoi 2-11-16 Bunkyo-ku Tokyo Japan sakaki@ Makoto Okazaki The University of Tokyo Yayoi 2-11-16 Bunkyo-ku Tokyo Japan mokazaki@ Yutaka Matsuo The University of Tokyo Yayoi 2-11-16 Bunkyo-ku Tokyo Japan matsuo@ ABSTRACT 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 compared methods in 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 by monitoring tweets with high probability 96 of earthquakes of Japan Meteorological Agency JMA seismic intensity scale 3 or more are detected . Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the

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