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The Next Generation of Disaster Management and Relief Planning: Immersive Analytics Based Approach

  • Radhia Toujani
  • Yasmin Chaabani
  • Zeineb Dhouioui
  • Hanen Bouali
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 840)

Abstract

Managing the risks of natural disasters can be enhanced by exploring social data. The need to swiftly extract meaningful information from large amounts of data generated by social network is on the rise especially to deal with natural disasters. New methods are needed to deeply support immersive social data analytics. Moreover, big data analysis seems to be able to improve accurate decisions to disaster management systems. The aim of this research is to determine critical cases and to focus on immersive sentiment analysis for big social data using Hadoop platform and machine learning technique. In one hand, we use MapReduce for the introduced data processing step. In the other hand, we apply support vector machine algorithm for the sentiment classification. We evaluate the performance of the performed classification method using the standard classification performance metrics accuracy, precision, recall, and F-measure and Microsoft Power BI as a visualization tool.

Keywords

Social network Social network analysis Sentiment analysis Sentiment classification Immersive classification Immersive analytics Big data MapReduce Machine learning Disaster management 

References

  1. 1.
    Velev, D., Zlateva, P.: Use of social media in natural disaster management. In: International Proceedings of Economic Development and Research, vol. 39, pp. 41–45 (2012)Google Scholar
  2. 2.
    Bilenko, M., Mooney, R.J.: Adaptive duplicate detection using learnable string similarity measures. In: Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 39–48. ACM (2003)Google Scholar
  3. 3.
    Chen, Z., Kalashnikov, D.V., Mehrotra, S.: Exploiting context analysis for combining multiple entity resolution systems. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, pp. 207–218. ACM (2009)Google Scholar
  4. 4.
    Gao, H., Barbier, G., Goolsby, R.: Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intell. Syst. 26(3), 10–14 (2011)CrossRefGoogle Scholar
  5. 5.
    Kongthon, A., et al.: The role of Twitter during a natural disaster: case study of 2011 Thai flood. In: 2012 Proceedings of Technology Management for Emerging Technologies (PICMET), PICMET 2012, pp. 2227–2232. IEEE (2012)Google Scholar
  6. 6.
    Yin, J., et al.: Using social media to enhance emergency situation awareness. IEEE Intell. Syst. 27(6), 52–59 (2012)CrossRefGoogle Scholar
  7. 7.
    Dufty, N., et al.: Using social media to build community disaster resilience. Aust. J. Emerg. Manag. 27(1), 40 (2012)Google Scholar
  8. 8.
    Dugdale, J., Van de Walle, B., Koeppinghoff, C.: Social media and SMS in the Haiti earthquake. In: Proceedings of the 21st International Conference on World Wide Web, pp. 713–714. ACM (2012)Google Scholar
  9. 9.
    Heer, J., Agrawala, M.: Design considerations for collaborative visual analytics. Inf. Vis. 7(1), 49–62 (2008)CrossRefGoogle Scholar
  10. 10.
    Reda, K., et al.: Visualizing large, heterogeneous data in hybrid-reality environments. IEEE Comput. Graph. Appl. 33(4), 38–48 (2013)CrossRefGoogle Scholar
  11. 11.
    Dong, A., et al.: Time is of the essence: improving recency ranking using Twitter data. In: Proceedings of the 19th International Conference on World Wide Web, pp. 331–340. ACM (2010)Google Scholar
  12. 12.
    Beneito-Montagut, R., et al.: Governmental social media use for emergency communication. In: ISCRAM (2013)Google Scholar
  13. 13.
    Bharosa, N., Lee, J., Janssen, M.: Challenges and obstacles in sharing and coordinating information during multi-agency disaster response: propositions from field exercises. Inf. Syst. Front. 12(1), 49–65 (2010)CrossRefGoogle Scholar
  14. 14.
    Pavlopoulos, S., Kyriacou, E., Berler, A., Dembeyiotis, S., Koutsouris, D.: A novel emergency telemedicine system based on wireless communication technology-ambulance. IEEE Trans. Inf Technol. Biomed. 2(4), 261–267 (1998)CrossRefGoogle Scholar
  15. 15.
    Ahmed, Y.A., Ahmad, M.N., Zakaria, N.H.: Towards exploring factors that influence social media-based knowledge sharing intentions in disaster management. J. Theor. Appl. Inf. Technol. 88(3) (2016)Google Scholar
  16. 16.
    Yates, D., Paquette, S.: Emergency knowledge management and social media technologies: a case study of the 2010 Haitian earthquake. Int. J. Inf. Manag. 31(1), 6–13 (2011)CrossRefGoogle Scholar
  17. 17.
    Cook, K.A., Thomas, J.J.: Illuminating the Path: The Research and Development Agenda for Visual Analytics (2005)Google Scholar
  18. 18.
    Chandler, T., et al.: Immersive analytics. In: Big Data Visual Analytics (BDVA), pp. 1–8. IEEE (2015)Google Scholar
  19. 19.
    Batrinca, B., Treleaven, P.C.: Social media analytics: a survey of techniques, tools and platforms. AI Soc. 30(1), 89–116 (2015)CrossRefGoogle Scholar
  20. 20.
    Das, S., Chen, M.: Yahoo! for Amazon: extracting market sentiment from stock message boards. In: Proceedings of the Asia Pacific Finance Association Annual Conference (APFA), Bangkok, Thailand, vol. 35, p. 43 (2001)Google Scholar
  21. 21.
    Tong, R.M.: An operational system for detecting and tracking opinions in on-line discussion. In: Working Notes of the ACM SIGIR 2001 Workshop on Operational Text Classification, vol. 1, p. 6 (2001)Google Scholar
  22. 22.
    Kowcika, A., et al.: Sentiment analysis for social media. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(7), 1–6 (2013)Google Scholar
  23. 23.
    Bouali, H., Akaichi, J.: Comparative study of different classification techniques: heart disease use case. In: 2014 13th International Conference on Machine Learning and Applications (ICMLA), pp. 482–486. IEEE (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Radhia Toujani
    • 1
  • Yasmin Chaabani
    • 1
  • Zeineb Dhouioui
    • 1
  • Hanen Bouali
    • 1
  1. 1.BESTMOD Department, Higher Institute of ManagementUniversity of TunisTunisTunisia

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