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.

We propose an algorithm and system that detects earthquakes worldwide in real time based on reports of social media users, or "citizen-sensors." Earthquake detections are based on user postings in any language and from any region. This approach is unsupervised, adapting automatically to changes in the input data stream, and only requires a general list of keywords for each language. Our method is noise tolerant and simple, providing good results both in terms of precision and recall. This complements prior work that mostly consists of supervised approaches that focus on performing detections in a specific geographical area and are difficult to generalize to a global scope. We demonstrate the effectiveness of this approach by using it within a real-time on-line system, which is publicly available and currently in use at National Seismology Center in Chile and Oceanographic and Hydrological Office of the Chilean Army. The quantitative evaluation of our system, performed during a 9-month period, shows that our solution is competitive to the best state-of-the-art methods. Overall, our findings indicate that our approach is an effective low-cost alternative for earthquake monitoring at a global scale.