A Methodology for enhancing Emergency Situational Awareness through Social Media

Abstract

Social media are a valuable source of information during emergency situations. First responders and rescue teams can further improve their situation awareness and be able to act more effectively, when using information available in the form of social media posts made from the public. This work proposes a methodology supported by a toolflow, which combines machine learning techniques for identifying informative Twitter posts about ongoing incidents of various types, with a semi-automated way of dispatching information to first responders. Evaluation results show that the accuracy of detecting informative text and images posted on Twitter about ongoing emergency situations, exceeds 80%, while analysis performance is near real-time.