Skip to main content
SpringerLink
Log in

Social Media in Disaster Relief

Usage Patterns, Data Mining Tools, and Current Research Directions

  • Chapter
Book cover Data Mining and Knowledge Discovery for Big Data

Part of the book series: Studies in Big Data ((SBD,volume 1))

Abstract

As social media has become more integrated into peoples’ daily lives, its users have begun turning to it in times of distress. People use Twitter, Facebook, YouTube, and other social media platforms to broadcast their needs, propagate rumors and news, and stay abreast of evolving crisis situations. Disaster relief organizations have begun to craft their efforts around pulling data about where aid is needed from social media and broadcasting their own needs and perceptions of the situation. They have begun deploying new software platforms to better analyze incoming data from social media, as well as to deploy new technologies to specifically harvest messages from disaster situations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Pfeffer, J., Carley, K.M.: Social Networks, Social Media, Social Change. In: Nicholson, D.M., Schmorrow, D.D. (eds.) Adv. Des. Cross-Cult. Act. Part Ii, pp. 273–282. CRC Press (2012)

    Google Scholar 

  2. Pfeffer, J., Zorbach, T., Carley, K.M.: Understanding online firestorms: Negative word of mouth dynamics in social media networks. J. Mark. Commun. (2013)

    Google Scholar 

  3. Moloney, A.: Haiti must act to address housing crisis - Oxfam. Thompson Reuters Found. (2013)

    Google Scholar 

  4. Drabek, T.E.: Human System Responses to Disaster: An Inventory of Sociological Findings. Springer, New York (1986)

    Book  Google Scholar 

  5. Dynes, R.R.: Organized Behavior in Disaster. Heath (1970)

    Google Scholar 

  6. Shklovski, I., Palen, L., Sutton, J.: Finding community through information and communication technology in disaster response. In: Proc. 2008 ACM Conf. Comput. Support. Coop. Work, pp. 127–136. ACM, San Diego (2008)

    Google Scholar 

  7. Shklovski, I., Burke, M., Kiesler, S., Kraut, R.: Technology Adoption and Use in the Aftermath of Hurricane Katrina in New Orleans. Am. Behav. Sci. 53, 1228–1246 (2010), doi:10.1177/0002764209356252

    Article  Google Scholar 

  8. Arcenaux, N., Weiss, A.S.: Seems stupid until you try it: press coverage of Twitter, 2006-9. New Media Soc. 12, 1262–1279 (2010), doi:10.1177/1461444809360773

    Article  Google Scholar 

  9. Sullivan, D.: Tracking Hurricane Sandy News Through Twitter. Mark. Land. (2012)

    Google Scholar 

  10. Carr, D.: How Hurricane Sandy Slapped the Sarcasm Out of Twitter, New York Media Decod. (2012)

    Google Scholar 

  11. Laird, S.: Sandy Sparks 20 Million Tweets. Mashable (2012)

    Google Scholar 

  12. Munro, R., Manning, C.D.: Short message communications: users, topics, and in-language processing. In: Proc. 2nd ACM Symp. Comput. Dev., pp. 1–10. ACM, Atlanta (2012)

    Google Scholar 

  13. Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proc. 19th Int. Conf. World Wide Web, pp. 591–600. ACM, Raleigh (2010)

    Google Scholar 

  14. Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proc. 9th Webkdd 1st Sna-Kdd 2007 Work. Web Min. Soc. Netw. Anal., pp. 56–65. ACM, San Jose (2007)

    Google Scholar 

  15. Naaman, M., Boase, J., Lai, C.-H.: Is it really about me?: message content in social awareness streams. In: Proc. 2010 ACM Conf. Comput. Support. Coop. Work, Cscw, pp. 189–192. ACM, Savannah (2010)

    Google Scholar 

  16. Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: Proc. Fourth ACM Int. Conf. Web Search Data Min., pp. 65–74. ACM, Hong Kong (2011)

    Chapter  Google Scholar 

  17. Starbird, K., Palen, L., Hughes, A.L., Vieweg, S.E.: Chatter on the red: what hazards threat reveals about the social life of microblogged information. In: Proc. 2010 ACM Conf. Comput. Support. Coop. Work, pp. 241–250. ACM, Savannah (2010)

    Google Scholar 

  18. Sinnappan, S., Farrell, C., Stewart, E.: Priceless Tweets! A Study on Twitter Messages Posted During Crisis: Black Saturday. In: Proc. 2010 Australas. Conf. Inf. Syst. Acis (2010)

    Google Scholar 

  19. Qu, Y., Huang, C., Zhang, P., Zhang, J.: Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake. In: Proc. Acm 2011 Conf. Comput. Support. Coop. Work, Cscsw, pp. 25–34. ACM, Hangzhou (2011)

    Google Scholar 

  20. Vieweg, S.E.: Situational Awareness in Mass Emergency: A Behavioral and Linguistic Analysis of Microblogged Communications. University of Colorado at Boulder (2012)

    Google Scholar 

  21. Sutton, J.: Twittering Tennessee: Distributed Networks and Collaboration Following a Technological Disaster. In: Proc. 7th Int. Conf. Inf. Syst. Crisis Response Manag. (2010)

    Google Scholar 

  22. Starbird, K., Palen, L. (How) will the revolution be retweeted?: information diffusion and the 2011 Egyptian uprising. In: Proc. ACM 2012 Conf. Comput. Support. Coop. Work, Cscw, pp. 7–16. ACM, Seattle (2012)

    Google Scholar 

  23. Centola, D., Macy, M.: Complex Contagions and the Weakness of Long Ties. Am. J. Sociol. 113, 702–734 (2007)

    Article  Google Scholar 

  24. NPR Staff, “Distant Witness”: Social Media’s “Journalism Revolution.” Talk Naton (2013)

    Google Scholar 

  25. @TwitterMedia NPR’s Andy Carvin Uses Twitter to Debunk A Hoax. #OnlyOnTwitter

    Google Scholar 

  26. Kaczynski, A.: How One Well-Connected Pseudonymous Twitter Spread Fake News About Hurricane Sandy. Buzzfeed Polit (2012)

    Google Scholar 

  27. Stuef, J.: The Man Behind @ComfortablySmug, Hurricane Sandy’s Worst Twitter Villain. Buzzfeed Fwd. (2012)

    Google Scholar 

  28. Mendoza, M., Poblete, B., Castillo, C.: Twitter under crisis: can we trust what we RT? In: Proc. First Work. Soc. Media Anal. Soma, pp. 71–79. ACM, Washington D.C. (2010)

    Google Scholar 

  29. Madrigal, A.C.: It Wasn’t Sunil Tripathi: The Anatomy of a Misinformation Disaster. The Atlantic (2013)

    Google Scholar 

  30. Weinstein, A.: Everybody Named the Wrong Boston Suspects Last Night and Promptly Forgot. Gawker (2013)

    Google Scholar 

  31. Martin, E.: Reflections on the Recent Boston Crisis. Reddit Blog (2013)

    Google Scholar 

  32. Keller, J.: How Boston Police Won the Twitter Wars During the Marathon Bomber Hunt. Bloom. Bussinessweek (2013)

    Google Scholar 

  33. Mission 4636, Collaborating organizations and History. Mission 4636 (2010)

    Google Scholar 

  34. Harvard Humanitarian Initiative, Disaster Relief 2.0: The future of Information Sharing in Humanitarian Emergencies. Harvard Humanitarian Initiative, UN Office for the Coordination of Humanitarian Affairs, United Nations Foundation (2011)

    Google Scholar 

  35. Liu, S.B., Palen, L., Sutton, J., et al.: In search of the bigger picture: The emergent role of online photo sharing in times of disaster. In: Proc. 5th Int. Conf. Inf. Syst. Crisis Response Manag. (2008)

    Google Scholar 

  36. Cohen, S.E.: Sandy Marked a Shift for Social Media Use in Disasters. Emerg. Manag. (2013)

    Google Scholar 

  37. St. Denis, L.A., Hughes, A.L., Palen, L.: Trial by Fire: The Deployment of Trusted Digital Volunteers in the 2011 Shadow Lake Fire. In: Proc. 9th Int. Conf. Inf. Syst. Crisis Response Manag. (2012)

    Google Scholar 

  38. Reuter, S.: What is a Virtual Operations Support Team? Idisaster 20 (2012)

    Google Scholar 

  39. Stephens, K.: Understanding VOSTs (Virtual Operations Support Teams) Hint: It’s All About Trust. West. Mass Smem (2012)

    Google Scholar 

  40. VOSG.us, About. Virtual Oper. Support Group (2011)

    Google Scholar 

  41. Panagiotopoulos, P., Ziaee Bigdeli, A., Sams, S.: "5 Days in August" – How London Local Authorities Used Twitter During the 2011 Riots. In: Scholl, H.J., Janssen, M., Wimmer, M.A., Moe, C.E., Flak, L.S. (eds.) EGOV 2012. LNCS, vol. 7443, pp. 102–113. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  42. Sarcevic, A., Palen, L., White, J., et al.: “Beacons of hope” in decentralized coordination: learning from on-the-ground medical twitterers during the 2010 Haiti earthquake. In: Proc. ACM 2012 Conf. Comput. Support. Coop. Work, Cscw, pp. 47–56. ACM, Seattle (2012)

    Google Scholar 

  43. Munro, R.: Crowdsourcing and the crisis-affected community: Lessons learned and looking forward from Mission 4636. Inf. Retr. 16, 210–266 (2013), doi:10.1007/s10791-012-9203-2

    Article  Google Scholar 

  44. Okolloh, O.: Ushahidi, or “testimony”: Web 2.0 tools for crowdsourcing crisis information. Particip. Learn. Action 59, 65–70 (2009)

    Google Scholar 

  45. Neubig, G., Yuichiroh, M., Masato, H., Koji, M.: Safety Information Mining — What can NLP do in a disaster—. In: Proc. 5th Int. Jt. Conf. Nat. Lang. Process. Asian Federation of Natural Language Processing, Chiang Mai, Thailand, pp. 965–973 (2011)

    Google Scholar 

  46. Kumar, S., Barbier, G., Abbasi, M.A., Liu, H.: TweetTracker: An Analysis Tool for Humanitarian and Disaster Relief. In: Proc. 2011 Int. Aaai Conf. Weblogs Soc. Media, pp. 661–662. AAAI, Barcelona (2011)

    Google Scholar 

  47. Carley, K.M., Reminga, J., Storrick, J., Columbus, D.: ORA User’s Guide, Carnegie Mellon University, School of Computer Science, Institute for Software Research, Pittsburgh, Pennsylvania (2013)

    Google Scholar 

  48. Carley, K.M., Columbus, D.: Basic Lessons in ORA and AutoMap 2011. Carnegie Mellon University, Pittsburgh (2011)

    Google Scholar 

  49. Carley, K.M., Pfeffer, J.: Dynamic Network Analysis (DNA) and ORA. Adv. Des. Cross-Cult. Act. Part (2012)

    Google Scholar 

  50. Costa, B., Boiney, J.: Social Radar. MITRE, McLean, Virginia, USA (2012)

    Google Scholar 

  51. Mathieu, J., Fulk, M., Lorber, M., et al.: Social Radar Workflows, Dashboards, and Environments. MITRE, Bedford (2012)

    Google Scholar 

  52. Schmerl, B., Garlan, D., Dwivedi, V., et al.: SORASCS: a case study in SOA-based platform design for socio-cultural analysis. In: Proc. 33rd Int. Conf. Softw. Eng., pp. 643–652. ACM, Waikiki (2011)

    Google Scholar 

  53. Garlan, D., Schmerl, B., Dwivedi, V., et al.: Specifying Workflows in SORASCS to Automate and Share Common HSCB Processes. In: Proc. Hscb Focus. Integrating Soc. Sci. Theory Anal. Methods Oper. Use (2011), doi:10.1.1.190.7086

    Google Scholar 

  54. Bostock, M., Ogievetsky, V., Heer, J.: D3 Data-Driven Documents. IEEE Trans. Vis. Comput. Graph. 17, 2301–2309 (2011), doi:10.1109/TVCG.2011.185

    Article  Google Scholar 

  55. Bostock, M., Carter, S.: Wind Speeds Along Hurricane Sandy’s Path - Interactive Feature, New York (2012)

    Google Scholar 

  56. Bostock, M., Ericson, M., Leonhardt, D., Marsh, B.: Across U.S. Companies, Tax Rates Vary Greatly, New York (2013)

    Google Scholar 

  57. Bostock, M., Bradsher, K.: China Still Dominates, but Some Manufacturers Look Else-where, New York (2013)

    Google Scholar 

  58. Carvin, A.: Andy Carvin’s Social Stories. Andy Carvins Soc. Stories

    Google Scholar 

  59. Lin, J., Snow, R., Morgan, W.: Smoothing techniques for adaptive online language models: topic tracking in tweet streams. In: Proc. 17th Acm Sigkdd Int. Conf. Knowl. Discov. Data Min., pp. 422–429. ACM, San Diego (2011)

    Chapter  Google Scholar 

  60. Martin, M.K., Pfeffer, J., Carley, K.M.: Network text analysis of conceptual overlap in interviews, newspaper articles and keywords. Soc. Netw. Anal. Min. (forthcoming)

    Google Scholar 

  61. Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Now Publishers (2008)

    Google Scholar 

  62. Carley, K.M., Pfeffer, J., Morstatter, F., et al.: Near Real Time Assessment of Social Media Using Geo-Temporal Network Analytics. In: Proc. 2013 Ieeeacm Int. Conf. Adv. Soc. Networks Anal. Min. (2013)

    Google Scholar 

  63. Schroeder, S.: Google Launches Crisis Map for Hurricane Sandy. Mashable (2012)

    Google Scholar 

  64. Abbasi, M.-A., Kumar, S., Filho, J.A.A., Liu, H.: Lessons Learned in Using Social Media for Disaster Relief - ASU Crisis Response Game (2012)

    Google Scholar 

  65. Carley, K.M., Columbus, D., Landwehr, P.: AutoMap User’s Guide, Carnegie-Mellon University, School of Computer Science, Institute for Software Research, Pittsburgh, Pennsylvania (2013)

    Google Scholar 

  66. Gimpel, K., Schneider, N., O’Connor, B., et al.: Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments. In: Proc. 49th Annu. Meet. Assoc. Comput. Linguist. Hum. Lang. Technol. (2011)

    Google Scholar 

  67. Owoputi, O., O’Connor, B., Dyer, C., et al.: Part-of-Speech Tagging for Twitter: Word Clusters and Other Advances. Carnegie Mellon University, Machine Learning Department, Pittsburgh, Pennsylvania, USA (2012)

    Google Scholar 

  68. Woolf, S.H.: The Meaning of Translational Research and Why It Matters. J. Am. Med. Assoc. 299, 211–213 (2008), doi:10.1001/jama.2007.26

    Article  Google Scholar 

  69. Kipper Schuler, K.: VerbNet: A broad-coverage, comprehensive verb lexicon. University of Pennsylvania (2005)

    Google Scholar 

  70. Fellbaum, C.: WordNet: an electronic lexical database. The MIT Press (1998)

    Google Scholar 

  71. Verma, S., Vieweg, S.E., Corvey, W.J., et al.: Natural Language Processing to the Rescue? Extracting “Situational Awareness” Tweets During Mass Emergency. In: Proc. 2011 Int. Aaai Conf. Weblogs Soc. Media (2011)

    Google Scholar 

  72. Starbird, K., Muzny, G., Palen, L.: Learning from the Crowd: Collaborative Filtering Techniques for Identifying On-the-Ground Twitterers during Mass Disruptions. In: Proc. 9th Int. Conf. Inf. Syst. Crisis Response Manag. Iscram (2012)

    Google Scholar 

  73. Settles, B.: Closing the loop: fast, interactive semi-supervised annotation with queries on features and instances. In: Proc. Conf. Empir. Methods Nat. Lang., pp. 1467–1478. Association for Computational Linguistics, Edinburgh (2011)

    Google Scholar 

  74. Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. Proc. 19th Int. Conf. World Wide Web, pp. 851–860. ACM, Raleigh (2010)

    Google Scholar 

  75. Carneiro, H.A., Mylonakis, E.: Google Trends: A Web-Based Tool for Real-Time Surveillance of Disease Outbreaks. Clin. Infect Dis. 49, 1557–1564 (2009), doi:10.1086/630200

    Article  Google Scholar 

  76. Fontugne, R., Cho, K., Won, Y., Fukuda, K.: Disasters seen through Flickr cameras. In: Proc. Spec. Work. Internet Disasters, pp. 1–10. ACM, Tokyo (2011)

    Chapter  Google Scholar 

  77. Rivadeneira, A.W., Gruen, D.M., Muller, M.J., Millen, D.R.: Getting our head in the clouds: toward evaluation studies of tagclouds. In: Proc. Sigchi Conf. Hum. Factors Comput. Syst., pp. 995–998. ACM, San Jose (2007)

    Chapter  Google Scholar 

  78. Bernstein, M.S., Suh, B., Hong, L., et al.: Eddi: interactive topic-based browsing of social status streams. In: Proc. 23rd Annu. ACM Symp. User Interface Softw. Technol., pp. 303–312. ACM, New York (2010)

    Google Scholar 

  79. Kas, M., Suh, B.: Visual Summarization for Topical Clusters in Twitter Streams (forthcoming, 2013)

    Google Scholar 

  80. Shahaf, D., Guestrin, C., Horvitz, E.: Trains of thought: generating information maps. In: Proc. 21st Int. Conf. World Wide Web, pp. 899–908. ACM, Lyon (2012)

    Chapter  Google Scholar 

  81. Kamvar, S.D., Harris, J.: We feel fine and searching the emotional web. In: Proc. Fourth ACM Int. Conf. Web Search Data Min., pp. 117–126. ACM, Hong Kong (2011)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Peter M. Landwehr & Kathleen M. Carley

Authors
  1. Peter M. Landwehr
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kathleen M. Carley
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter M. Landwehr .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of California, Los Angeles, USA

    Wesley W. Chu

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Landwehr, P.M., Carley, K.M. (2014). Social Media in Disaster Relief. In: Chu, W. (eds) Data Mining and Knowledge Discovery for Big Data. Studies in Big Data, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40837-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40837-3_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40836-6

  • Online ISBN: 978-3-642-40837-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation