Event detection using social sensors

Social media, such as Facebook and twitter, received much attention recently especially due to their real-time nature. For example, when an earthquake occurs, people immediately post information related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing these posts. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter, Facebook and other social media, and propose an algorithm to monitor tweets and to detect a target event. We devised a filter of data based on features such as the keywords, the number of times they are present, and their context. We consider each user feed as a sensor and the collection of such sensors creates a system, which can be used to promptly warn registered users. The posts triggering the detections also provided very short first-impression narratives from people who experienced the shaking. We will also show that the validity of such a process is not bound to a particular context or language but can be used on a variety of other subjects.