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Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors (2010)
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| Venue: | In Proceedings of the Nineteenth International WWW Conference (WWW2010). ACM |
| Citations: | 482 - 3 self |
BibTeX
@INPROCEEDINGS{Sakaki10earthquakeshakes,
author = {Takeshi Sakaki and Makoto Okazaki and Yutaka Matsuo},
title = {Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors},
booktitle = {In Proceedings of the Nineteenth International WWW Conference (WWW2010). ACM},
year = {2010}
}
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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 announcements that are broadcast by the JMA. 1.
Keyphrases
earthquake shake twitter user social sensor real-time event detection target event twitter user numerous earthquake intensity scale large number high probability event location location estimation important characteristic particle filter much attention real-time nature probabilistic spatiotemporal model popular microblogging service earthquake occurrence earthquake reporting system particle filtering many twitter post real-time interaction ubiquitous pervasive computing japan meteorological agency
