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What to Expect When the Unexpected Happens: Social Media Communications Across Crises

Published:28 February 2015Publication History

ABSTRACT

The use of social media to communicate timely information during crisis situations has become a common practice in recent years. In particular, the one-to-many nature of Twitter has created an opportunity for stakeholders to disseminate crisis-relevant messages, and to access vast amounts of information they may not otherwise have. Our goal is to understand what affected populations, response agencies and other stakeholders can expect-and not expect-from these data in various types of disaster situations. Anecdotal evidence suggests that different types of crises elicit different reactions from Twitter users, but we have yet to see whether this is in fact the case. In this paper, we investigate several crises-including natural hazards and human-induced disasters-in a systematic manner and with a consistent methodology. This leads to insights about the prevalence of different information types and sources across a variety of crisis situations.

References

  1. World humanitarian data and trends. Tech. rep., UN OCHA, Dec. 2013.Google ScholarGoogle Scholar
  2. Acar, A., and Muraki, Y. Twitter for crisis communication: lessons learned from Japan's tsunami disaster. Int. J. of Web Based Communities (2011). Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Adams, D. S. Policies, programs, and problems of the local Red Cross disaster relief in the 1960s. Tech. rep., University of Delaware, Disaster Research Center, 1970.Google ScholarGoogle Scholar
  4. Aramaki, E., Maskawa, S., and Morita, M. Twitter catches the flu: Detecting influenza epidemics using Twitter. In Proc. of EMNLP (2011). Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Benevenuto, F., Magno, G., Rodrigues, T., and Almeida, V. Detecting spammers on Twitter. In In Proc. of CEAS (2010).Google ScholarGoogle Scholar
  6. Bruns, A., Burgess, J. E., Crawford, K., and Shaw, F. #qldfloods and @qpsmedia: Crisis communication on Twitter in the 2011 South East Queensland floods. Tech. rep., ARC Centre, Queensland University of Technology, 2012.Google ScholarGoogle Scholar
  7. Caragea, C., McNeese, N., Jaiswal, A., Traylor, G., Kim, H., Mitra, P., Wu, D., Tapia, A., Giles, L., Jansen, B. J., et al. Classifying text messages for the Haiti earthquake. In Proc. of ISCRAM (2011).Google ScholarGoogle Scholar
  8. Carr, L. J. Disaster and the sequence-pattern concept of social change. American Journal of Sociology (1932).Google ScholarGoogle Scholar
  9. Carvin, A. Distant Witness. CUNY Journalism Press, 2013.Google ScholarGoogle Scholar
  10. Castillo, C., Mendoza, M., and Poblete, B. Information credibility on Twitter. In Proc. of WWW (2011). Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Cohen, W. W. Fast effective rule induction. In Proc. of ICML (1995), 115--123.Google ScholarGoogle ScholarCross RefCross Ref
  12. De Choudhury, M., Diakopoulos, N., and Naaman, M. Unfolding the event landscape on Twitter: Classification and exploration of user categories. In Proc. of CSCW (2012). Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Denef, S., Bayerl, P. S., and Kaptein, N. A. Social media and the police: tweeting practices of British police forces during the August 2011 riots. In Proc. of CHI (2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Diakopoulos, N., De Choudhury, M., and Naaman, M. Finding and assessing social media information sources in the context of journalism. In Proc. of CHI (2012). Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Esser, F., and Hanitzsch, T., Eds. The Handbook of Comparative Communication Research. ICA Handbooks. Routledge, Apr. 2012.Google ScholarGoogle Scholar
  16. Fischer, H. W. Response to disaster: fact versus fiction & its perpetuation-the sociology of disaster. University Press of America, 1998.Google ScholarGoogle Scholar
  17. Fraustino, J. D., Liu, B., and Jin, Y. Social media use during disasters: A review of the knowledge base and gaps. Tech. rep., Science and Technology Directorate, U.S. Dept. of Homeland Security, 2012.Google ScholarGoogle Scholar
  18. Gerlitz, C., and Rieder, B. Mining one percent of Twitter: collections, baselines, sampling. M/C Journal (2013).Google ScholarGoogle Scholar
  19. González-Bailón, S., Wang, N., Rivero, A., Borge-Holthoefer, J., and Moreno, Y. Assessing the bias in communication networks sampled from Twitter. Social Networks 38 (July 2014), 16--27.Google ScholarGoogle ScholarCross RefCross Ref
  20. Hong, L., Convertino, G., and Chi, E. H. Language matters in Twitter: A large scale study. In Proc. of ICWSM (2011).Google ScholarGoogle Scholar
  21. Hughes, A. L. Participatory design for the social media needs of emergency public information officers. In Proc. of ISCRAM (2014).Google ScholarGoogle Scholar
  22. Hughes, A. L., St. Denis, L. A., Palen, L., and Anderson, K. Online public communications by police and fire services during the 2012 hurricane Sandy. In Proc. of CHI (2014). Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Imran, M., Castillo, C., Lucas, J., Patrick, M., and Rogstadius, J. Coordinating human and machine intelligence to classify microblog communications in crises. In Proc. of ISCRAM (2014).Google ScholarGoogle Scholar
  24. Imran, M., Elbassuoni, S. M., Castillo, C., Diaz, F., and Meier, P. Extracting information nuggets from disaster-related messages in social media. In Proc. of ISCRAM (2013).Google ScholarGoogle Scholar
  25. Joseph, K., Landwehr, P. M., and Carley, K. M. Two 1% s don't make a whole: Comparing simultaneous samples from Twitter's streaming API. In Social Computing, Behavioral-Cultural Modeling and Prediction. 2014.Google ScholarGoogle ScholarCross RefCross Ref
  26. Kanhabua, N., and Nejdl, W. Understanding the diversity of tweets in the time of outbreaks. In Proc. of WOW workshop (2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Kumar, S., Morstatter, F., Zafarani, R., and Liu, H. Whom should I follow?: Identifying relevant users during crises. In Proc. of Hypertext (2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Leavitt, A., and Clark, J. A. Upvoting hurricane Sandy: event-based news production processes on a social news site. In Proc. of CHI (2014). Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Metaxas, P., and Mustafaraj, E. The rise and the fall of a citizen reporter. In Proc. of WebSci (2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Monroy-Hernández, A., boyd, d., Kiciman, E., De Choudhury, M., and Counts, S. The new war correspondents: The rise of civic media curation in urban warfare. In Proc. of CSCW (2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Morstatter, F., Pfeffer, J., and Liu, H. When is it biased?: assessing the representativeness of Twitter's streaming API. In Proc. of Web Science Track at WWW (2014). Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Morstatter, F., Pfeffer, J., Liu, H., and Carley, K. M. Is the sample good enough? comparing data from Twitter's streaming API with Twitter's Firehose. In Proc. of ICWSM (2013).Google ScholarGoogle Scholar
  33. Munro, R., and Manning, C. D. Short message communications: users, topics, and in-language processing. In Proc. of DEV (2012). Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Olteanu, A., Castillo, C., Diaz, F., and Vieweg, S. CrisisLex: A lexicon for collecting and filtering microblogged communications in crises. In Proc. of ICWSM (2014).Google ScholarGoogle Scholar
  35. Palen, L., and Liu, S. B. Citizen communications in crisis: anticipating a future of ICT-supported public participation. In Proc. of CHI (2007). Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Perry, R. W., and Quarantelli, E. L. What is a disaster?: New answers to old questions. Xlibris Corporation, 2005.Google ScholarGoogle Scholar
  37. Poblete, B., Garcia, R., Mendoza, M., and Jaimes, A. Do all birds tweet the same?: characterizing Twitter around the world. In Proc. of CIKM (2011). Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Prelog, A. J. Social Change and Disaster Annotated Bibliography. PhD thesis, Dept. of Economics, Colorado State University, 2010.Google ScholarGoogle Scholar
  39. Qu, Y., Huang, C., Zhang, P., and Zhang, J. Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake. In Proc. of CSCW (2011). Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Shaw, F., Burgess, J., Crawford, K., and Bruns, A. Sharing news, making sense, saying thanks: Patterns of talk on Twitter during the Queensland floods. Australian Journal of Communication (2013).Google ScholarGoogle Scholar
  41. Snow, R., O'Connor, B., Jurafsky, D., and Ng, A. Y. Cheap and fast-but is it good?: Evaluating non-expert annotations for natural language tasks. In Proc. of EMNLP (2008). Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Sreenivasan, N. D., Lee, C. S., and Goh, D. H.-L. Tweet me home: exploring information use on Twitter in crisis situations. In Online Communities and Social Computing. 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Starbird, K., Muzny, G., and Palen, L. Learning from the crowd: Collaborative filtering techniques for identifying on-the-ground twitterers during mass disruptions. In Proc. of ISCRAM (2012).Google ScholarGoogle Scholar
  44. Starbird, K., and Palen, L. "Voluntweeters": Self-organizing by digital volunteers in times of crisis. In Proc. of CHI (2011). Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Starbird, K., Palen, L., Hughes, A. L., and Vieweg, S. Chatter on the red: what hazards threat reveals about the social life of microblogged information. In Proc. of CSCW (2010). Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Thomson, R., Ito, N., Suda, H., Lin, F., Liu, Y., Hayasaka, R., Isochi, R., and Wang, Z. Trusting tweets: The Fukushima disaster and information source credibility on Twitter. In Proc. of ISCRAM (2012).Google ScholarGoogle Scholar
  47. Tierney, K. J. Disaster preparedness and response: Research findings and guidance from the social science literature. Tech. rep., University of Delaware, Disaster Research Center, 1993.Google ScholarGoogle Scholar
  48. Truelove, M., Vasardani, M., and Winter, S. Towards credibility of micro-blogs: characterising witness accounts. GeoJournal (2014), 1--21.Google ScholarGoogle Scholar
  49. Vieweg, S. Situational Awareness in Mass Emergency: A Behavioral and Linguistic Analysis of Microblogged Communications. PhD thesis, University of Colorado at Boulder, 2012.Google ScholarGoogle Scholar
  50. Vieweg, S., Hughes, A. L., Starbird, K., and Palen, L. Microblogging during two natural hazards events: What Twitter may contribute to situational awareness. In Proc. of CHI (2010). Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Wukich, C., and Mergel, I. A. Closing the Citizen-Government communication gap: Content, audience, and network analysis of government tweets. Social Science Research Network Working Paper Series (Aug. 2014).Google ScholarGoogle Scholar

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          cover image ACM Conferences
          CSCW '15: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing
          February 2015
          1956 pages
          ISBN:9781450329224
          DOI:10.1145/2675133

          Copyright © 2015 ACM

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          Publication History

          • Published: 28 February 2015

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