, Volume 80, Issue 4, pp 491–502 | Cite as

The limits of crisis data: analytical and ethical challenges of using social and mobile data to understand disasters

  • Kate CrawfordEmail author
  • Megan Finn


Social media platforms and mobile phone data are commonly mined to produce accounts of how people are responding in the aftermath of crisis events. Yet social and mobile datasets have limitations that, if not sufficiently understood and accounted for, can produce specific kinds of analytical and ethical oversights. In this paper, we analyze some of the problems that emerge from the reliance on particular forms of crisis data, and we suggest ways forward through a deeper engagement with ethical frameworks and a more critical questioning of what crisis data actually represents. In particular, the use of Twitter data and crowdsourced text messages during crisis events such as Hurricane Sandy and the Haiti Earthquake raised questions about the ways in which crisis data act as a system of knowledge. We analyze these events from ontological, epistemological, and ethical perspectives and assess the challenges of data collection, analysis and deployment. While privacy concerns are often dismissed when data is scraped from public-facing platforms such as Twitter, we suggest that the kinds of personal information shared during a crisis—often as a way to find assistance and support—present ongoing risks. We argue for a deeper integration of critical data studies into crisis research, and for researchers to acknowledge their role in shaping norms of privacy and consent in data use.


Critical data studies Crisis informatics Privacy Ethics Disasters 



Kate Miltner gave invaluable assistance in the preparation of this article. The authors also thank the journal editors and reviewers for insightful comments on the paper.


  1. boyd, d., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication, & Society, 15(5), 662–679.CrossRefGoogle Scholar
  2. Brenner, J. & Smith A. (2013a). 72% of online adults are social networking site users. Pew Research Center’s Internet and American Life Project. Accessed August 5, 2013.
  3. Burns, R. (2014a). Moments of closure in the knowledge politics of digital humanitarianism. Geoforum, 53(1), 51–62.CrossRefGoogle Scholar
  4. Burns, R. (2014b). Rethinking big data in digital humanitarianism: Practices, epistemologies, and social relations. Geojournal Online First,. Accessed October 27, 2014.
  5. Button, G. V. (2002). Popular media reframing of man-made disasters. In S. M. Hoffman & A. Oliver-Smith (Eds.), Catastrophe and culture: The anthropology of disaster (pp. 143–158). Santa Fe: School of American Research Press.Google Scholar
  6. Button, G. V. (2010). Disaster culture: Knowledge and uncertainty in the wake of human and environmental catastrophe. Walnut Creek, CA: Left Coast Press Inc.Google Scholar
  7. Calhoun, C. (2004). A world of emergencies: Fear, intervention, and the limits of cosmopolitan order. Canadian Review of Sociology/Revue canadienne de sociologie, 41(4), 373–395.CrossRefGoogle Scholar
  8. Clémenzo, J.Y. (2011) Ushahidi project and Mission 4636 in Haiti: Participation, representation and political economy (Thesis). Accessed October 27, 2014.
  9. Cohen, J. (2012). Configuring the Networked Self: Law, Code, and the Play of Everyday Practice. New Haven, CT: Yale University Press.Google Scholar
  10. Cote, M. (2014). Data motility: The materiality of big social data. Cultural Studies Review, 20(1), 121–149.CrossRefGoogle Scholar
  11. Crawford, K. (2010). Whispering news: From word of mouth to the ambient news network. In G. Meikle & G. Redden (Eds.), News online: Transformations and continuities. London: Palgrave Macmillan.Google Scholar
  12. Crawford, K. (2013). Hidden biases in big data. Harvard Business Review. April 1. Accessed April 2, 2013.
  13. Crawford, K., Meier, P., Perlich, C., Luers, A., Falieros, G., & Thorp, J. (2013). Big data, communities and ethical resilience: A framework for action. Bellagio white paper. Rockefeller Foundation. Accessed November 21, 2013.
  14. Crawford, K., & Schultz, J. M. (2014). Big data and due process: Toward a framework to redress predictive privacy harms. Boston College Law Review, 55(1), 93–128.Google Scholar
  15. Crutcher, M., & Zook, M. (2009). Placemarks and waterlines: Racialized cyberscapes in post Katrina Google Earth. Geoforum, 40(4), 523–534.CrossRefGoogle Scholar
  16. De Micheli, C. & Stroppa, A. (2013). Twitter and the underground market. 11th Nexa lunch seminar, Turin, Italy. Accessed July 22, 2013.
  17. Dixon, D. (2012). Analysis tool or research methodology? Is there an epistemology for patterns? In D. Berry (Ed.), Understanding digital humanities. London: Palgrave Macmillan.Google Scholar
  18. Dugdale, J., Van de Walle, B., & Koeppinghoff C. (2012). Social media and SMS in the Haiti Earthquake. In Proceedings of the 21st international conference companion on world wide web. ACM.Google Scholar
  19. Earle, P. S., Bowden, D. C., & Guy, M. (2011). Twitter earthquake detection: Earthquake monitoring in a social world. Annals of Geophysics, 54(6), 708–715.Google Scholar
  20. Elwood, S., Goodchild, M. F., & Sui, D. Z. (2012). Researching volunteered geographic information: Spatial data, geographic research, and new social practice. Annals of the Association of American Geographers, 102(3), 571–590.CrossRefGoogle Scholar
  21. Erikson, K. (1976). Everything in its path: Destruction of community in the Buffalo Creek Flood. New York: Simon & Schuster.Google Scholar
  22. Ford, H. (2012). Crowd wisdom. Index on Censorship, 41(4), 33–39.CrossRefGoogle Scholar
  23. Geiger, R. S. (2011). Lives of bots. In G. Lovink, & N. Tkacz, (Eds.), Critical point of view: A Wikipedia reader (pp. 78–89). Amsterdam: Institute of Network Cultures. Google Scholar
  24. Geiger, R. S. (2014). Bots, bespoke, code and the materiality of software platforms. New Media and Society, 17(3), 342–356.Google Scholar
  25. Geiger, R. S. & Ribes, D. (2010). The work of sustaining order in Wikipedia: The banning of a vandal. In Proceedings of the 2010 ACM conference on computer supported cooperative work (CSCW). New York: ACM.Google Scholar
  26. Gillespie, T. (2010). The politics of ‘Platforms’. New Media and Society, 12(3), 347–364.CrossRefGoogle Scholar
  27. Gillespie, T. (2014). The relevance of algorithms. In T. Gillespie, P. J. Boczkowski, & K. Foot (Eds.), Media technologies: Essays on communication, materiality and society (pp. 167–194). Cambridge, MA: MIT Press.Google Scholar
  28. Grinberg, N., Naaman, M., Shaw, B. & Lotan, G. (2013) Extracting diurnal patterns of real world activity from social media. In Proceedings of the seventh international AAAI conference on weblogs and social media (ICWSM–13). Accessed June 16, 2013.
  29. Ioannidis, J. P. A. (2013). Informed consent, big data, and the oxymoron of research that is not research. The American Journal of Bioethics, 13(4), 40–42.CrossRefGoogle Scholar
  30. Johns, A. (1999). Introduction. In A. Johns (Ed.), Dreadful visitations: Confronting natural catastrophe in the age of enlightenment. New York: Routledge.Google Scholar
  31. Lotan, G. (2012) #Sandy: Social media mapping. Social flow. Accessed July 11, 2014.
  32. Lotan, G., Graeff, E., Ananny, M., Gaffney, D., Pearce, I., & boyd, d. (2011). The revolutions were tweeted: Information flows during the 2011 Tunisian and Egyptian revolutions. International Journal of Communications, 5(1), 1375–1405.Google Scholar
  33. Mahrt, M., & Zharkov, M. (2013). The value of big data in digital media research. Journal of Broadcasting & Electronic Media, 57(1), 20–33.CrossRefGoogle Scholar
  34. Marwick, Alice, & Danah, B. (2011). To see and be seen: Celebrity practice on Twitter. Convergence, 17(2), 139–158.Google Scholar
  35. McCosker, A. (2013). De-framing disaster: Affective encounters with raw and autonomous media. Continuum: Journal of Media and Cultural Studies, 27(3), 382–396.CrossRefGoogle Scholar
  36. Meier, P. (2011). What is crisis mapping? An update on the field and looking ahead, iRevolution Blog, January 20, 2011. Accessed May 5, 2014.
  37. Meier, P. (2013a). Early results of MicroMappers response to Typhoon Yolanda (updated). IRevolution.
  38. Meier, P. (2013b). Digital humanitarians: From Haiti earthquake to Typhoon Yolanda. IRevolution.
  39. Morrow, N., Mock, N., Papendieck, A. & Kocmich, N. (2011). Independent evaluation of the Ushahidi Haiti Project. Accessed March 11, 2013.
  40. Munro, R. (2013). Crowdsourcing and the crisis-affected community: Lessons learned and looking forward from Mission 4636. Information Retrieval, 16(1), 210–266.CrossRefGoogle Scholar
  41. Nissenbaum, H. (2010). Privacy in context: Technology, policy and the integrity of social life. Stanford, CA: Stanford University Press.Google Scholar
  42. O’Connor, M. R. (2012). Two years later, Haitian earthquake death toll in dispute, Columbia Journalism Review. Accessed October 5, 2012.
  43. Oliver-Smith, A. (1986). Introduction. Disaster context and causation: An overview of changing perspectives in disaster research, in natural disasters and cultural responses. In A. Oliver-Smith (Ed.), Studies in third world Societies (Vol. 36). Williamsburg, VA: College of William Mary.Google Scholar
  44. Oliver-Smith, A, & Hoffman, S., (Eds.). (2002) Introduction. In Catastrophe and culture: The anthropology of disaster. Santa Fe, NM: School of American Research Press.Google Scholar
  45. Palen, L., Anderson, K. M., Mark, G., Martin, J., Sicker, D., Palmer, M., & Grunwald, D. (2010). A vision for technology-mediated support for public participation and assistance in mass emergencies and disasters. In Proceedings of the 2010 ACM-BCS visions of computer science conference. British Computer Society.Google Scholar
  46. Palen, L., Hiltz, S. M., & Liu, S. L. (2007). Online forums supporting grassroots participation in emergency preparedness and response. Communications of the ACM, 50(3), 54–58.CrossRefGoogle Scholar
  47. Papacharissi, Z. (2012). Without You I’m nothing: Performances of the self on Twitter. International Journal of Communications, 6(1), 1989–2006.Google Scholar
  48. Papacharissi, Z., & de Fatima Oliveira, M. (2011). Affective news and networked publics: The rhythms of news storytelling on #Egypt. Journal of Communication, 62(2), 266–282.CrossRefGoogle Scholar
  49. Price, M., & Ball, P. (2014). Big data, selection bias, and the statistical patterns of mortality in conflict. SAIS Review, XXXVI(1), 9–20.Google Scholar
  50. Puschmann, C. & Burgess, J. (2013). The politics of Twitter data. In K. Weller, A. Bruns, J. Burgess, M. Mahrt & C. Puschmann (Eds.) Twitter and society. New York: Peter Lang. Accessed June 6, 2013.
  51. Rainie, L. (2012). Smartphone ownership update: 2012. Accessed September 5, 2013.
  52. Rose-Redwood, R. (2006). Governmentality, geography, and the geo-coded world. Progress in Human Geography, 30(4), 469–486.CrossRefGoogle Scholar
  53. Sangari, K. (2009) Conjunction and flow: The gendered temporalities of (media) disaster. E-Media Studies 2(1). Accessed June 21, 2013.
  54. Shanley, L., Burns, R., Bastian, Z., & Robson, F. (2013). Tweeting up a storm: The promise and perils of crisis mapping. Photogrammetric Engineering & Remote Sensing, 79(10), 865–879.Google Scholar
  55. Shaw, F., Burgess, J., Crawford, K., & Bruns, A. (2013). Sharing news, making sense, saying thanks: Patterns of talk on Twitter during the Queensland floods. Australian Journal of Communication, 40(1), 23–40.Google Scholar
  56. Sheller, M. (2013). The Islanding effect: Post-disaster mobility systems and humanitarian logistics in Haiti. Cultural Geographies, 20(2), 185–204.CrossRefGoogle Scholar
  57. Shih, G. (2012). Over 20 million tweets sent as Sandy struck. Reuters. Accessed November 3, 2013.
  58. Solove, D. J. (2012). Privacy self-management and the consent dilemma. The George Washington University Law School. Public Law and Legal Theory Paper & Legal Studies Research Paper No. 2012-141. Accessed January 9, 2013.
  59. Sutherlin, G. (2013). A voice in the crowd: Broader implications for crowdsourcing translation during crisis. Journal of Information Science, 39(3), 397–409.CrossRefGoogle Scholar
  60. Tanaka, Y., Sakamoto, Y., & Matsuka, T. (2012). Transmission of rumor and criticism in Twitter after the great Japan earthquake. In Proceedings of the Annual Meeting of the Cognitive Science Society, 2387–2392.Google Scholar
  61. Tene, O., & Polonetsky, J. (2012). Privacy in the age of big data: A time for big decisions. Stanford Law Review Online, 64(63), 63–69.Google Scholar
  62. Tierney, K. (2007). From the margins to the mainstream? Disaster research at the crossroads. Annual Review of Sociology, 33(1), 504–525.CrossRefGoogle Scholar
  63. Tierney, K., Bevc, C., & Kuligowski, E. (2006). Metaphors matter: Disaster myths, media frames, and their consequences in Hurricane Katrina. The Annals of the American Academy of Political and Social Science, 604(1), 57–81.CrossRefGoogle Scholar
  64. Tufekci, Z. (2014). Big Questions for social media big data: Representativeness, validity and other methodological pitfalls, In Proceedings of the international AAAI conference on weblogs and social media. Google Scholar
  65. Van Dijck, J. (2013). The culture of connectivity. Oxford: Oxford University Press.CrossRefGoogle Scholar
  66. Verma, S., Vieweg, S., Corvey, W., Palen, L., Martin, J., Palmer, M., Schram, A., & Anderson, K. (2011). Natural language processing to the rescue?: Extracting "situational awareness" tweets during mass emergency. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, (pp. 385–392).Google Scholar
  67. Vis, F. (2013a). Twitter as a reporting tool for breaking news. Digital Journalism, 1(1), 27–47.CrossRefGoogle Scholar
  68. Vis, F. (2013b). A critical reflection on Big Data: Considering APIs, researchers and tools as data markers. First Monday, 18(10). Accessed October 22, 2013.
  69. Watts, M. (1983). On the poverty of theory: Natural hazards research in context. In K. Hewitt (Ed.), Interpretations of calamity from the viewpoint of human ecology. Boston, MA: Allen & Unwin Inc.Google Scholar
  70. World Bank. (2010). Internet users (per 100 people). Accessed December 11, 2013.
  71. Zickuhr, K. (2013b). Location-based services. Pew Research Center’s Internet and American Life Project. Accessed September 12, 2013.
  72. Zimmer, M., & Proferes, N. J. (2014). A topology of Twitter research: Disciplines, methods, and ethics. Aslib Journal of Information Management, 66(3), 250–261.CrossRefGoogle Scholar
  73. Zook, M., Graham, M., Shelton, T., & Gorman, S. (2010). Volunteered geographic information and crowdsourcing disaster relief: A case study of the Haitian earthquake. World Medical & Health Policy, 2(2), 7–33.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Microsoft ResearchNYU Information Law InstituteNew YorkUSA
  2. 2.University of WashingtonSeattleUSA

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