Does the spatiotemporal distribution of tweets match the spatiotemporal distribution of flood phenomena? A study about the River Elbe Flood in June 2013

Abstract

In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This approach is applied to examine the River Elbe Flood in Germany in June 2013. Although recent research has shown that social media platforms like Twitter can be complementary information sources for achieving situation awareness, previous work is mostly concentrated on the classification and analysis of tweets without resorting to existing data related to the disaster, e.g. catchment borders or sensor data about river levels. Our results show that our approach based on geographical relations can help to manage the high volume and velocity of social media messages and thus can be valuable for both crisis response and preventive flood monitoring.