Extraction of Reliable and Actionable Information from Social Media During Emergencies

Abstract

Communities have been shown to coordinate among themselves using public forums such as Twitter and Facebook during disastrous events, and utilize even sparse telecommunication infrastructure to communicate. In this work we design a framework for text mining that will extract crisis situation and location information from tweets about individuals in need. Using these extracted data, we develop a community source map of the disaster. There have been previous efforts to develop real-time disaster maps, but these are limited to manual entry whereas we use AI to create such maps. While much of the prior work focuses on densely populated urban scenarios, we also consider sparsely populated rural contexts where social network updates may be infrequent. We have experimented our proposed solution on around 20 different disaster related tweet databases from different part of the world. The primary results show ~81 percent accuracy in crisis situation information extraction and ~92 percent accuracy in location extraction.