Abstract
Natural disasters have been a major cause of huge losses for both people’s life and property. There is no doubt that the importance of Emergency Warning System (EWS) has been considered more seriously than ever. Unfortunately, most EWSs do not provide acceptable service to identify people who might be affected by a certain disasters. In this project, we propose an approach to identify possibly affected users of a target disaster by using online social networks. The proposed method consists of three phases. First of all, we collect location information from social network websites, such as Twitter. Then, we propose a social network analysis algorithm to identify potential victims and communities. Finally, we conduct an experiment to test the accuracy and efficiency of the approach. Based on the result, we claim that the approach can facilitate identifying potential victims effectively based on data from social networking systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Te Ara - the Encyclopedia of New Zealand: Natural Hazards and Disasters (2011)
Australian Emergency Alert 2011, Emergency Alert. Be Warned. Be Informed: Frequently Asked Question (FAQs) (2011), http://www.emergencyalert.gov.au/frequently-asked-questions.html
Carlson, N.: Facebook Has More Than 600 Million Users, Goldman Tells Clients, Business Insider (2011)
Diestel, R.: Graph Theory, 3rd edn. Springer, Berlin (2005) ISBN 978-3-540-26183-4
Jünger, M., Mutzel, P.: Graph Drawing Software, pp. 77–103. Springer, Berlin (2003)
Maggie, S.: Twitter co-founder Jack Dorsey Rejoins Company. BBC News (2011)
New South Wales Government 2011, New South Wales Government - Online Notification System: ONS Frequently Asked Questions (2011), https://notifications.workcover.nsw.gov.au/FAQ.aspx
Nielsen: Nine Million Australians Use Social Networks, Wentworth Avenue Sydney, NSW (2010)
Pfeffer, J.: Networks / Pajek Package for Large Network Analysis (2004)
Sean, D.Y., Rice, E.: Online social networking Tips on using online social networks for development. Participatory Learning and Action 59(1), 112–114 (2009)
Stephenson, K.A., Zelen, M.: Rethinking centrality: Methods and examples. Social Networks 11, 1–37 (1989)
The Standard Emergency Warning Signal 2011, Standard Emergency Warning Signal (SEWS) (2011), http://www.ema.gov.au/www/emaweb/rwpattach.nsf/VAP%28FC77CAE5F7A38CF2EBC5832A6FD3AC0C%29~SEWS_DL+Brochure_HR.PDF/$file/SEWS_DL+Brochure_HR.PDF
Thompson, K.: Ken Thompson the Bumble Bee - Social Network Analysis: an introduction (2011)
White, C., Plotnick, C., Kushma, J., Hiltz, R., Turoff, M.: An Online Social Network for Emergency Management (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pho, H., Han, S.C., Kang, B.H. (2011). Emergency-Affected Population Identification and Notification by Using Online Social Networks. In: Kim, Th., et al. Software Engineering, Business Continuity, and Education. ASEA 2011. Communications in Computer and Information Science, vol 257. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27207-3_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-27207-3_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27206-6
Online ISBN: 978-3-642-27207-3
eBook Packages: Computer ScienceComputer Science (R0)