Skip to main content Accessibility help
×
Hostname: page-component-76fb5796d-dfsvx Total loading time: 0 Render date: 2024-04-26T23:09:13.081Z Has data issue: false hasContentIssue false

6 - Disaster Monitoring

Published online by Cambridge University Press:  05 May 2015

Bella Robinson
Affiliation:
Commonwealth Scientific and Industrial Research Organisation
Robert Power
Affiliation:
Commonwealth Scientific and Industrial Research Organisation
Mark Cameron
Affiliation:
Commonwealth Scientific and Industrial Research Organisation
Yelena Mejova
Affiliation:
Qatar Computing Research Institute, Doha
Ingmar Weber
Affiliation:
Qatar Computing Research Institute, Doha
Michael W. Macy
Affiliation:
Cornell University, New York
Get access

Summary

Twitter is a new data channel for emergency managers to source public information for situational awareness and as a means of engaging with the community during disaster response and recovery activities. Twitter has been used successfully to identify emergency events, obtain crowd sourced information as the event unfolds, provide up-to-date information to the affected community from authoritative agencies, and conduct resource planning.

Introduction

Motivation

Natural disasters have increased in severity and frequency in recent years. According to Guha-Sapir et al. (2011), in 2010, 385 natural disasters killed over 297,000 people worldwide, impacted 217 million human lives, and cost the global economy an estimated US$123.9 billion. There are numerous examples from around the world: the 2004 Indian Ocean earthquake and tsunami; the more recent 2011 Tōhoku earthquake and tsunami, which damaged the Fukushima nuclear power station; hurricanes Katrina and Sandy in 2005 and 2012 respectively; the 2010 China floods, which caused widespread devastation; and Victoria's 2009 “Black Saturday” bushfires in Australia, killing 173 people and having an estimated A$2.9 billion in total losses (Stephenson, Handmer, & Haywood, 2012).

With urban development occurring on coastlines and spreading into rural areas, houses and supporting infrastructure are expanding into high-risk regions. The growing world population is moving into areas progressively more prone to natural disasters and unpredictable weather events. These events have been increasing in frequency and severity in recent years (Hawkins et al., 2012).

It has been recognized that information published by the general public on social media is relevant to emergency managers and that social media is a useful means of providing information to communities that may be impacted by emergency events (Lindsay, 2011; Anderson, 2012). To prepare and respond to such emergency situations effectively, it is critical that emergency managers have relevant and reliable information. For example, bushfire management is typically a regional government responsibility, and each jurisdiction has its own agency that takes the lead in coordinating community preparedness and responding to bushfires when they occur.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2015

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abel, Fabian, Hauff, Claudia, Houben, Geert-Jan, Stronkman, Richard, and Tao, Ke. (2012). Twitcident: fighting fire with information from social web streams. InWWW (Companion Volume), ed. Mille, Alain, Gandon, Fabien L., Misselis, Jacques, Rabinovich, Michael, and Staab, Steffen (pp. 305–8). ACM.CrossRef
AFAC. (2013). The Australiasian Inter-Service Incident Management System, 4th ed. Australiasian Fire and Emergency Service Authorities Council.
Anderson, Martin. (2012). Integrating social media into traditional management command and control structures: the square peg into the round hole. In Australian and New Zealand Disaster and Emergency Management Conference, ed. Peter Sugg (pp. 18–34). AST Management Pty Ltd.Google Scholar
Beneito-Montagut, Roser, Anson, Susan, Shaw, Duncan, and Brewster, Christopher. (2013). Resilience: two case studies on governmental social media use for emergency communication. In Proceedings of the Information Systems for Crisis Response and Management Conference (ISCRAM 2013 12–15 May, 2013) (pp. 828–33). ISCRAM.
Cameron, Mark A., Power, Robert, Robinson, Bella, and Yin, Jie. (2012). Emergency situation awareness from Twitter for crisis management. In Proceedings of the 21st International Conference Companion on World Wide Web. WWW ’12 Companion (pp. 695–8). ACM.CrossRef
Charlton, Kym. (2012). Disaster Management and Social Media – A Case Study. Tech. rept. Media and Public Affairs Branch, Queensland Police Service. Accessed April 26, 2013.
Chatfield, Akemi Takeoka, Scholl, Hans Jochen, and Brajawidagda, Uuf. (2014). #Sandy tweets: citizens’ co-production of time-critical information during an unfolding catastrophe. In 2014 47th Hawaii International Conference on System Sciences, 1947–57.Google Scholar
Chowdhury, Soudip Roy, Imran, Muhammad, Asghar, Muhammad Rizwan, Amer-Yahia, Sihem, and Castillo, Carlos. (2013). Tweet4act: using incident-specific profiles for classifying crisis-related messages. In The 10th International Conference on Information Systems for Crisis Response and Management (ISCRAM) (pp. 834–9) ISCRAM.
Earle, Paul S., Bowden, Daniel C., and Guy, Michelle. (2012). Twitter earthquake detection: earthquake monitoring in a social world. Annals of GeoPhysics, 54(6), 708–15.Google Scholar
Endsley, Mica R. (1995). Toward a theory of situation awareness in dynamic systems: situation awareness. Human Factors, 37(1), 32–64.Google Scholar
Guha-Sapir, Debby, Vos, Femke, and Below, Regina, with Ponserre, Sylvain. (2011). Annual Disaster Statistical Review 2010:The Numbers and Trends. Centre for Research on the Epidemiology of Disasters. http://www.cred.be/sites/default/files/ADSR_2010.pdf.Google Scholar
Gupta, Aditi, Lamba, Hemank, Kumaraguru, Ponnurangam, and Joshi, Anupam. (2013). Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy. In Proceedings of the 22nd International Conference on World Wide Web Companion. WWW ’13 Companion (pp. 729–36). International World Wide Web Conferences Steering Committee.CrossRef
Hawkins, Charlie, Prakash, Mahesh, Sullivan, Andrew, Box, Paul, Cameron, Mark, Power, Robert, Gould, Jim, Dunstall, Simon, and Dormer, Alan. (2012). All Hazards: Digital Technology & Services for Disaster Management. Tech. rept. CSIRO, ePublush # EP128826.
Heinzelman, Jessica, and Waters, Carol. (2010). Crowdsourcing Crisis Information in Disaster-Affected Haiti. Tech. rept. United States Institute of Peace.
Imran, Muhammad, Castillo, Carlos, Lucas, Ji, Meier, Patrick, and Rogstadius, Jakob. (2014). Coordinating human and machine intelligence to classify microblog communications in crises. In The 11th International Conference on Information Systems for Crisis Response and Management (ISCRAM) (pp. 712–21). ISCRAM.
Imran, Muhammad, Elbassuoni, Shady Mamoon, Castillo, Carlos, Diaz, Fernando, and Meier, Patrick. (2013). Extracting information nuggets from disaster-related messages in social media. In The 10th International Conference on Information Systems for Crisis Response and Management (ISCRAM) (pp. 791–800). ISCRAM.
Joachims, Thorsten. (1998). Text categorization with support vector machines: learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning. ECML ’98 (pp. 137–42). Springer-Verlag.CrossRef
Lewis, David D. (1998). Naive (Bayes) at forty: the independence assumption in information retrieval. In Machine Learning: ECML-98 (pp. 4–15). Springer.
Lewis, David D., and Ringuette, Marc. (1994). A comparison of two learning algorithms for text categorization. In Third Annual Symposium on Document Analysis and Information Retrieval, vol. 33 (pp. 81–93). Information Science Research Institute, University of Nevada.
Lindsay, Bruce R. (2011). Social Media and Disasters: Current Uses, Future Options, and Policy Considerations. Tech. rept. Analyst in American National Government. http://www.fas.org/sgp/crs/homesec/R41987.pdf (Accessed March 12, 2013).
Mendoza, Marcelo, Poblete, Barbara, and Castillo, Carlos. (2010). Twitter under crisis: can we trust what we RT? In Proceedings of the First Workshop on Social Media Analytics. SOMA ’10 (pp. 71–9). ACM.CrossRef
Nigam, Kamal, Lafferty, John, and McCallum, Andrew. (1999). Using maximum entropy for text classification. In IJCAI-99 Workshop on Machine Learning for Information Filtering, vol. 1 (pp. 61–7). IJCAI.
Robinson, Bella, Power, Robert, and Cameron, Mark. (2013a). An evidence based earthquake detector using Twitter. In Proceedings of the Workshop on Language Processing and Crisis Information 2013 (pp. 1–9). Asian Federation of Natural Language Processing.
Robinson, Bella, Power, Robert, and Cameron, Mark. (2013b). A sensitive Twitter earthquake detector. In Proceedings of the 22nd international conference on World Wide Web Companion. WWW ’13 Companion (pp. 999–1002). International World Wide Web Conferences Steering Committee.
Rogstadius, Jakob, Vukovic, Maja, Teixeira, Claudio A., Kostakos, Vassilis, Karapanos, Evangelos, and Laredo, Jim A. (2013). CrisisTracker: crowdsourced social media curation for disaster awareness. IBM Journal of Research and Development, 57(5), 4:1–4:13.CrossRefGoogle Scholar
Sakaki, Takeshi, Okazaki, Makoto, and Matsuo, Yutaka. (2010). Earthquake shakes Twitter users: real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web. WWW ’10 (pp. 851–60). ACM.CrossRef
Sakaki, Takeshi, Okazaki, Makoto, and Matsuo, Yutaka. (2013). Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Transactions on Knowledge and Data Engineering, 25(4), 919–31.CrossRefGoogle Scholar
Schulz, Axel, and Ristoski, Petar. (2013). The car that hit the burning house: understanding small scale incident related information in microblogs. In Seventh International AAAI Conference on Weblogs and Social Media (pp. 11–14). AAAI.
Schulz, Axel, Ristoski, Petar, and Paulheim, Heiko. (2013). I see a car crash: real-time detection of small scale incidents in microblogs. In The Semantic Web: ESWC 2013 Satellite Events. Lecture Notes in Computer Science, no. 7955, ed. Cimiano, Philipp, Fernàndez, Miriam, Lopez, Vanessa, Schlobach, Stefan, and Völker, Johanna (pp. 22–33). SpringerBerlin Heidelberg.Google Scholar
Stephenson, Catherine, Handmer, John, and Haywood, Aimee. (2012). Estimating the net cost of the 2009 Black Saturday Fires to the affected regions. Tech. rept. RMIT, Bushfire CRC, Victorian DSE.
Stollberg, Beate, and de Groeve, Tom. (2012). The use of social media within the global disaster alert and coordination system (GDACS). In Proceedings of the 21st International Conference Companion on World Wide Web. WWW ’12 Companion (pp. 703–6). ACM.CrossRef
Teague, Bernard, McLeod, Ronald, and Pascoe, Susan. (2010). 2009 Victorian Bushfires Royal Commission: Final Report. Parliament of Victoria.
Thomson, Robert, Ito, Naoya, Suda, Hinako, Lin, Fangyu, Liu, Yafei, Hayasaka, Ryo, Isochi, Ryuzo, and Wang, Zian. (2012). Trusting tweets: the Fukushima disaster and information source credibility on Twitter. In The 9th International Conference on Information Systems for Crisis Response and Management (ISCRAM). http://www.iscramlive.org/ISCRAM2012/proceedings/112.pdf.
Verma, Sudha, Vieweg, Sarah, Corvey, William, Palen, Leysia, Martin, James H., Palmer, Martha, Schram, Aaron, and Anderson, Kenneth Mark. (2011). Natural language processing to the rescue? Extracting “situational awareness” tweets during mass emergency. In ICWSM, ed.Adamic, Lada A., Baeza-Yates, Ricardo A., and Counts, Scott (pp. 385–92). AAAI.

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×