How do eyewitness social media reports reflect socio-economic effects of natural hazards?

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10540)


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).


Hazard analytics Socio-economics Data mining Eyewitness media Flickr 


  1. 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. 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. 3.
    UNISDR: Global Assessment Report on Disaster Risk Reduction. World Risk Report. Bndniss Entwicklung Hilft, Berlin, Germany (2009)Google Scholar
  4. 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
  5. 5.
    Gallina, V., Torresan, S., Critto, A., Sperotto, A., Glade, T., Marcomini, A.: A review of multi-risk methodologies for natural hazards: consequences and challenges for a climate change impact assessment. J. Env. Manag. 168, 123–132 (2016)CrossRefGoogle Scholar
  6. 6.
    Tkachenko, N., Jarvis, S., Procter, R.: Predicting floods with Flickr tags. PLOS ONE (2017). doi: 10.1371/journal.pone.0172870
  7. 7.
    Tkachenko, N., Procter, R., Jarvis, S.: Predicting the impact of urban flooding using open data. RSOS (2016). doi: 10.1098/rsos.160013
  8. 8.
    Guthrie, R.: The catastrophic nature of humans. Nat. Geo. 8(6), 421–422 (2015)CrossRefGoogle Scholar
  9. 9.
    Wachinger, G., Renn, O., Begg, C., Kuhlicke, C.: The risk perception paradox - implications for governance and communication of natural hazards. Risk Anal. 33(6), 1049–1065 (2013)CrossRefGoogle Scholar
  10. 10.
    Earle, P.: Earthquake Twitter. Nat. Geo. 3, 221–222 (2010)CrossRefGoogle Scholar
  11. 11.
    Acar, A., Muraki, Y.: Twitter for crisis communication: lessons learned from Japan’s tsunami disaster. Int. J. Web. Based Comm. 7(3), 392–402 (2011)CrossRefGoogle Scholar
  12. 12.
    Tang, Z., Zhang, L., Xu, F., Vo, H.: Examining the role of social media in California’s drought risk management in 2014. Nat. Hazards 79(1), 171–193 (2015)CrossRefGoogle Scholar
  13. 13.
    Al-Saggaf, Y., Simmons, P.: Social media in Saudi Arabia: exploring its use during two natural disasters. Tech. Forec. Soc. Change 95, 3–15 (2015)CrossRefGoogle Scholar
  14. 14.
    Thomee, B., Shamma, D.A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., Li, L.J.: YFCC100M: the new data in multimedia research. ACM. Comms. 59(2), 64–73 (2016)CrossRefGoogle Scholar
  15. 15.
    Crooks, A., Croitoru, A., Stefanidis, A., Radzikowski, J.: Earthquake: twitter as a distributed sensor system. GIS. Trans. 17(1), 124–147 (2013)CrossRefGoogle Scholar
  16. 16.
    Granger, C.W.J.: Some properties of time series data and their use in econometric model specification. J. Econometr. 16, 121–130 (1981)CrossRefGoogle Scholar
  17. 17.
    Flach, P.: Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, Cambridge (2012)CrossRefzbMATHGoogle Scholar
  18. 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
  19. 19.
    Teigland, J.: Mega-events and impacts on tourism; the predictions and realities of the Lillehammer Olympics. Imp. Asses. Proj. Appr. 17(4), 305–317 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Warwick Institute for the Science of CitiesUniversity of WarwickCoventryUK
  2. 2.Department of Computer ScienceUniversity of WarwickCoventryUK
  3. 3.The Alan Turing InstituteThe British LibraryLondonUK

Personalised recommendations