Abstract
The analysis of potentially large volumes of crowd-sourced and social media data is central to meeting the requirements of the ATHENA project. Here, we discuss the various stages of the pipeline process we have developed, including acquisition of the data, analysis, aggregation, filtering, and structuring. We highlight the challenges involved when working with unstructured, noisy data from sources such as Twitter, and describe the crisis taxonomies that have been developed to support the tasks and enable concept extraction. State-of-the-art techniques such as formal concept analysis and machine learning are used to create a range of capabilities including concept drill down, sentiment analysis, credibility assessment, and assignment of priority. We ground many of these techniques using results obtained from a set of tweets which emerged from the Colorado wildfires of 2012 in order to demonstrate the applicability of our work to real crisis scenarios.
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Notes
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Several labelled data collections, including the Colorado wildfires dataset used in this chapter, have been downloaded from http://crisislex.org/data-collections.html.
References
Dubey, R., Luo, Z., Xu, M., & Wamba, S. F. (2015). Developing an integration framework for crowd-sourcing and internet of things with applications for disaster response. In 2015 IEEE International Conference on Data Science and Data Intensive Systems (pp. 520–524).
Sievers, J. A. (2015). Embracing crowdsourcing: A strategy for state and local governments approaching ‘whole community’ emergency planning. State and Local Government Review, 47(1), 57–67.
Alexander, D. E. (2014). Social media in disaster risk reduction and crisis management. Science and Engineering Ethics, 20(3), 717–733.
Oh, O., Agrawal, M., & Rao, H. R. (2013). Community intelligence and social media services: A rumor theoretic analysis of tweets during social crises. MIS Quarterly, 37(2), 407–426.
Gao, H., Barbier, G., & Goolsby, R. (2011). Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intelligent Systems, 26(3), 10–14.
Narvaez, R. W. (2012). Crowdsourcing for disaster preparedness: Realities and opportunities. Master’s thesis, Graduate Institute of International and Development Studies, Geneva, Switzerland.
Olteanu, A., Vieweg, S., & Castillo, C. (2015). What to expect when the unexpected happens: Social media communications across crises. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (pp. 994–1009). CSCW 2015, Vancouver, BC, Canada, March 14–18, 2015.
Andrews, S., & Orphanides, C. (2012). Knowledge discovery through creating formal contexts. International Journal of Space-Based and Situated Computing, 2(2), 123–138.
Andrews, S., & Orphanides, C. (2013). Discovering knowledge in data using formal concept analysis. International Journal of Distributed Systems and Technologies, 4(2), 31–50.
Domdouzis, K., Andrews, S., Gibson, H., Akhgar, B., & Hirsch, L. (2014). Service-oriented design of a command and control intelligence dashboard for crisis management. In Proceedings of the 7th IEEE/ACM International Conference on Utility and Cloud Computing (pp. 702–707). UCC 2014, London, UK, December 8–11, 2014.
de Assis, L. F. G., Herfort, B., Steiger, E., Horita, F. E. A., & de Albuquerque, J. P. (2015). Geographical prioritization of social network messages in near real-time using sensor data streams: an application to floods. In XVI Brazilian Symposium on GeoInformatics (pp. 26–37), Campos do Jordao, Sao Paulo, Brazil, November 29–December 2, 2015.
de Albuquerque, J. P., Herfort, B., Brenning, A., & Zipf, A. (2015). A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management. International Journal of Geographical Information Science, 29(4), 667–689.
Cresci, S., Tesconi, M., Cimino, A., & Dell Orletta, F. (2015). A linguistically-driven approach to cross-event damage assessment of natural disasters from social media messages. In Proceedings of the 24th international conference companion on World Wide Web. ACM.
Verma, S., Vieweg, S., Corvey, W. J., Palen, L., Martin, J. H., Palmer, M., et al. (2011). Natural language processing to the rescue? extracting ‘situational awareness’ tweets during mass emergency. In Proceedings of the 5th International Conference on Weblogs and Social Media, Barcelona, Spain, July 17–21, 2011.
Yin, J., Lampert, A., Cameron, M. A., Robinson, B., & Power, R. (2012). Using social media to enhance emergency situation awareness. IEEE Intelligent Systems, 27(6), 52–59.
Castillo, C., Mendoza, M., & Poblete, B. (2011). Information credibility on Twitter. In Proceedings of the 20th International Conference on World Wide Web (pp. 675–684), Hyderabad, India, March 28–April 1, 2011.
Gupta, A., Kumaraguru, P., Castillo, C., & Meier, P. (2014). TweetCred: A real-time web-based system for assessing credibility of content on Twitter. CoRR abs/1405.5490.
O’Donovan, J., Kang, B., Meyer, G., Hollerer, T., & Adali, S. (2012). Credibility in context: An analysis of feature distributions in Twitter. In 2012 International Conference on Privacy, Security, Risk and Trust, PASSAT 2012, and 2012 International Conference on Social Computing (pp. 293–301). Social Com 2012, Amsterdam, Netherlands. September 3–5, 2012.
Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015). Processing social media messages in mass emergency: A survey. ACM Computing Survey, 47(4), 67.
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques (3rd ed.). San Francisco, CA: Morgan Kaufmann.
Ganter, B., & Wille, R. (1999). Formal concept analysis—Mathematical foundations. New York: Springer.
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Andrews, S., Day, T., Domdouzis, K., Hirsch, L., Lefticaru, R., Orphanides, C. (2017). Analyzing Crowd-Sourced Information and Social Media for Crisis Management. In: Akhgar, B., Staniforth, A., Waddington, D. (eds) Application of Social Media in Crisis Management. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-319-52419-1_6
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