Robust Detection of Extreme Events Using Twitter: Worldwide Earthquake Monitoring

• Published in: IEEE transactions on multimedia 2018-10, Vol.20 (10), p.2551-2561
• Main Authors: Poblete, Barbara, Guzman, Jheser, Maldonado, Jazmine, Tobar, Felipe
• Format: Article
• Language: English
• Subjects: Adaptation models; Computational modeling; Digital media; Disaster management; Earthquakes; Emergency management; Event detection; Natural disasters; Real-time systems; Social networks; Time signals; Tracking; Twitter
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2018.2855107

Timely detection and accurate description of extreme events, such as natural disasters and other crisis situations, are crucial for emergency management and mitigation. Extreme-event detection is challenging, since one has to rely upon reports from human observers appointed to specific geographical areas, or on an expensive and sophisticated infrastructure. In the case of earthquakes, geographically dense sensor networks are expensive to deploy and maintain. Therefore, only some regions-or even countries-are able to acquire useful information about the effects of earthquakes in their own territory. An inexpensive and viable alternative to this problem is to detect extreme real-world events through people's reactions in online social networks. In particular, Twitter has gained popularity within the scientific community for providing access to real-time "citizen sensor" activity. Nevertheless, the massive amount of messages in the Twitter stream, along with the noise it contains, underpin a number of difficulties when it comes to Twitter-based event detection. We contribute to address these challenges by proposing an online method for detecting unusual bursts in discrete-time signals extracted from Twitter. This method only requires a one-off semisupervised initialization and can be scaled to track multiple signals in a robust manner. We also show empirically how our proposed approach, which was envisioned for generic event detection, can be adapted for worldwide earthquake detection, where we compare the proposed model to the state of the art for earthquake tracking using social media. Experimental results validate our approach as a competitive alternative in terms of precision and recall to leading solutions, with the advantage of implementation simplicity and worldwide scalability.