I See a Car Crash: Real-Time Detection of Small Scale Incidents in Microblogs

Abstract

Microblogs are increasingly gaining attention as an important information source in emergency management. Nevertheless, it is still difficult to reuse this information source during emergency situations, because of the sheer amount of unstructured data. Especially for detecting small scale events like car crashes, there are only small bits of information, thus complicating the detection of relevant information.We present a solution for a real-time identification of small scale incidents using microblogs, thereby allowing to increase the situational awareness by harvesting additional information about incidents. Our approach is a machine learning algorithm combining text classification and semantic enrichment of microblogs. An evaluation based shows that our solution enables the identification of small scale incidents with an accuracy of 89% as well as the detection of all incidents published in real-time Linked Open Government Data.KeywordsTraining DatasetSituational AwarenessIncident TypeLink Open DataSemantic EnrichmentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.