A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management

Abstract

In recent years, social media emerged as a potential resource to improve the management of crisis situations such as disasters triggered by natural hazards. Although there is a growing research body concerned with the analysis of the usage of social media during disasters, most previous work has concentrated on using social media as a stand-alone information source, whereas its combination with other information sources holds a still underexplored potential. This article presents an approach to enhance the identification of relevant messages from social media that relies upon the relations between georeferenced social media messages as Volunteered Geographic Information and geographic features of flood phenomena as derived from authoritative data (sensor data, hydrological data and digital elevation models). We apply this approach to examine the micro-blogging text messages of the Twitter platform (tweets) produced during the River Elbe Flood of June 2013 in Germany. This is performed by means of a statistical analysis aimed at identifying general spatial patterns in the occurrence of flood-related tweets that may be associated with proximity to and severity of flood events. The results show that messages near (up to 10 km) to severely flooded areas have a much higher probability of being related to floods. In this manner, we conclude that the geographic approach proposed here provides a reliable quantitative indicator of the usefulness of messages from social media by leveraging the existing knowledge about natural hazards such as floods, thus being valuable for disaster management in both crisis response and preventive monitoring.

Keywords:

• Volunteered Geographic Information
• social media
• crisis
• disaster
• emergency management
• Twitter
• flood
• Germany

Acknowledgements

The authors would like to thank the German Federal Waterways and Shipping Administration and the German Federal Institute for Hydrology for providing the water level data. João Porto de Albuquerque is grateful for FAPESP (grant no. 2012/18675-1), CAPES (grant no. 12065-13-7) and Heidelberg University (Excellence Initiative II/Action 7) for providing funding for his research stay and visiting professorship at Heidelberg University. Alexander Brenning is grateful to the Alexander von Humboldt Foundation for a research fellowship at Heidelberg University, which supported his contribution to this research. The authors are grateful to the anonymous reviewers for their helpful suggestions.

Notes

1. https://dev.twitter.com/docs/platform-objects/tweets, accessed on 15 August 2014.

2. http://www.pegelonline.wsv.de, accessed on 15 October 2013.

3. http://www.geofedia.com, accessed on 15 July 2014.

4. http://twitcident.com/, accessed on 15 July 2014.