How do eyewitness social media reports reflect socio-economic effects of natural hazards?
- First Online:
Recent years have seen a remarkable proliferation of studies attempting to establish relationships between observable online human behaviour and various types of crisis (social, political, economic and natural). Methods utilizing user generated content (UGC) have been already applied to various environmental hazards, such as floods, wildfires, earthquakes, tsunamis and other kinds of emergencies. However, what is currently lacking are more detailed insights into differences between the ways people use social media to report various natural hazard events. In this study we make use of the YFCC100M dataset in order to verify whether statistically robust relationships exist between the volumes of uploaded content during different natural hazards and estimated human and economic losses in the affected countries. Our findings demonstrate that Flickr reflect impacts of events with the highest frequency of occurrence (such as floods or storms) and/or with the recurring spatial structure (such as landslides or earthquakes).
KeywordsHazard analytics Socio-economics Data mining Eyewitness media Flickr
- 1.OECD: New Data for Understanding the Human Condition. OECD Global Science Forum Report on Data and Research Infrastructure for the Social Sciences (2013)Google Scholar
- 2.IPCC: Managing the risks of extreme events and disasters to advance climate change adaptation. Special Report of the Intergovernmental Panel on Climate Change (2012)Google Scholar
- 3.UNISDR: Global Assessment Report on Disaster Risk Reduction. World Risk Report. Bndniss Entwicklung Hilft, Berlin, Germany (2009)Google Scholar
- 4.Guha-Sapir, D., Hoyois, P.: Measuring the human and economic impact of disasters. Report produced for the Government Office of Science, Foresight project ’Reducing Risks of Future Disasters: Priorities for Decision Makers’, Foresight (2012)Google Scholar
- 6.Tkachenko, N., Jarvis, S., Procter, R.: Predicting floods with Flickr tags. PLOS ONE (2017). doi:10.1371/journal.pone.0172870
- 7.Tkachenko, N., Procter, R., Jarvis, S.: Predicting the impact of urban flooding using open data. RSOS (2016). doi:10.1098/rsos.160013
- 18.Popocatepetl Volcano (Mexico): largest eruption, 16 April 2012, heavy ash fall (2012). (Volcano Discovery: posted Monday 16 April, 2012, 21:45 PM)Google Scholar