Geographical prioritization of social network messages in near real-time using sensor data streams: An application to floods

Abstract

Social networks have been used to overcome the problem of incomplete official data, and provide a more detailed description of a disaster. However, the filtering of relevant messages on-the-fly remains challenging due to the large amount of misleading, outdated or inaccurate information. This paper presents an approach for the automated geographic prioritization of social networks messages for flood risk management based on sensor data streams. It was evaluated using data from Twitter and monitoring agencies of different countries. The results revealed that the proposed approach has a potential to identify valuable flood-related messages in near real-time.