Abstract
In spite of social media’s lack of structural integrity, accuracy, and reduced noise with respect to other forms of communication, it plays an increasingly vital role in the observation of societal actions before, during, and after significant events. In October 2012, Hurricane Sandy making landfall on the northeastern coasts of the United States demonstrated this role. This work provides a preliminary view into how social media could be used to monitor and gauge community resilience to such natural disasters. We observe, evaluate, and visualize how Twitter data evolves over time before, during, and after a natural disaster such as Hurricane Sandy and what opportunities there may be to leverage social media for situational awareness and emergency response.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alexander, D.: The Study of Natural Disasters, 1977-97: Some Reflections on a Changing Field of Knowledge. Disasters 21(4), 284–304 (1997)
Birregah, B., Top, T., Perez, C., Chatelet, E., Matta, N., Lemercier, M., Snoussi, H.: Multi-layer Crisis Mapping: A Social Media-Based Approach. In: 2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 379–384 (2012)
Bruneau, M., et al.: A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities. Earthquake Spectra 19(4), 733–752 (2003)
Chi, T., et al.: Research on information system for natural disaster monitoring and assessment. In: Proc. of the 2003 IEEE International Geoscience and Remote Sensing Symposium (2003)
Chu, E.T., Chen, Y.-L., Lin, J.-Y., Liu, J.W.-S.: Crowdsourcing support system for disaster surveillance and response. In: 2012 15th International Symposium on Wireless Personal Multimedia Communications (WPMC), pp. 21–25 (2012)
Fung, G.P.C., et al.: Stock prediction: Integrating text mining approach using real-time news. In: Proc. of the 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, pp. 395–403 (2003)
Fung, G.P.C., et al.: The predicting power of textual information on financial markets. IEEE Intelligent Informatics Bulletin 5(1) (June 2005)
Gruhl, D., et al.: The predictive power of online chatter. In: Proceeding of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, Chicago, Illinois, pp. 78–87 (2005)
Hussain, M., et al.: Emerging geo-information technologies (GIT) for natural disaster management in Pakistan: an overview. In: Proc. of the 2nd International Conference on Recent Advances in Space Technologies (2005)
Jie, et al.: Using Social Media to Enhance Emergency Situation Awareness. Intelligent Systems 27(6), 52–59 (2012)
Kreimer, A., Arnold, M., Carlin, A.: Building Safer Cities: The Future of Disaster Risk. World Bank Publications (2003)
Little, R.G.: Toward more robust infrastructure: observations on improving the resilience and reliability of critical systems. In: Proc. of the 36th Annual Hawaii International Conference on System Sciences (2003)
Llinas, J.: Information fusion for natural and man-made disasters. In: Proc. of the 5th International Conference on Information Fusion, pp. 570–576 (2002)
Patton, R.M., et al.: Discovery, analysis, and characteristics of event impacts. In: 2008 11th International Conference on Information Fusion, pp. 1–8 (2008)
Yu, J.X., Ng, M.K., Huang, J.Z.: Patterns discovery based on time-series decomposition. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol. 2035, p. 336. Springer, Heidelberg (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Patton, R.M., Steed, C.A., Stahl, C.G., Treadwell, J.N. (2013). Observing Community Resiliency in Social Media. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Lecture Notes in Computer Science, vol 7975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39640-3_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-39640-3_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39639-7
Online ISBN: 978-3-642-39640-3
eBook Packages: Computer ScienceComputer Science (R0)