Advertisement

Automated Emergence of a Crisis Situation Model in Crisis Response Based on Tweets

  • Aurélie Montarnal
  • Shane Halse
  • Andrea Tapia
  • Sébastien Truptil
  • Frederick Benaben
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 506)

Abstract

During a crisis, being able to understand quickly the situation on-site is crucial for the responders to take relevant decisions together. Social media, in particular Twitter, have proved to be a means for rapidly getting information from the field. However, the deluge of data is heterogeneous in many ways (location, trust, content, vocabulary, etc.), and getting a model of the crisis situation still requires laborious human actions. In addition, depending on which kind of information is mined from them, tweets have to be handle one-by-one (e.g. find victims), or as a whole - amount of tweets - (e.g. occurence of an event). This paper proposes a framework for automatically extracting, interpreting and aggregating streams of tweets to characterize crisis situations. It is based on a specific metamodel that determines the different concepts required to model a crisis situation.

Keywords

Crisis situation modeling Decision support system Crisis management Cross-organizational collaboration 

References

  1. 1.
    Altay, N., Green, W.G.: OR/MS research in disaster operations management. Eur. J. Oper. Res. 175(1), 475–493 (2006)CrossRefzbMATHGoogle Scholar
  2. 2.
    Charles, A., Lauras, M., Barthe, A. M., Bénaben, F.: Gathering, Structuring and Modeling Business Process Knowledge of the Response to a Nuclear Crisis: Towards a Simulation Platform for Better Coordination. In: Camarinha-Matos, L.M., Pereira-Klen, A., Afsarmanesh, H. (eds.) PRO-VE 2011. IAICT, vol. 362, pp. 486–493. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23330-2_53
  3. 3.
    Benaben, F., Truptil, S., Lauras, M., Salatge, N.: Management of collaborative behavior through a service-oriented mediation system: the case of crisis management. In: 2015 IEEE International Conference on Services Computing (SCC), pp. 554–561. IEEE (2015)Google Scholar
  4. 4.
    Wolbers, J., Boersma, K.: The common operational picture as collective sensemaking. J. Contingencies Crisis Manag. 21(4), 186–199 (2013)CrossRefGoogle Scholar
  5. 5.
    Alexander, D.E.: Social media in disaster risk reduction and crisis management. Sci. Eng. Ethics 20(3), 717–733 (2014)CrossRefGoogle Scholar
  6. 6.
    Vieweg, S.E.: Situational awareness in mass emergency: A behavioral and linguistic analysis of microblogged communications (2012)Google Scholar
  7. 7.
    Yin, J., Karimi, S., Lampert, A., Cameron, M., Robinson, B., Power, R.: Using social media to enhance emergency situation awareness. In: Twenty-Fourth International Joint Conference on Artificial Intelligence. (2015)Google Scholar
  8. 8.
    Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors: J. Hum. Factors Ergon. Soc. 37(1), 32–64 (1995)CrossRefGoogle Scholar
  9. 9.
    Benaben, F., Montarnal, A., Truptil, S., Lauras, M., Fertier, A., Salatge, N., Rebiere, S.: A conceptual framework and a suite of tools to support crisis management. In: Proceedings of the 50th Hawaii International Conference on System Sciences (2017)Google Scholar
  10. 10.
    Benaben, F., Montarnal, A., Fertier, A., Truptil, S. Big-data and the question of horizontal and vertical intelligence: a discussion on disaster management. In: Afsarmanesh, H., Camarinha-Matos, L., Lucas Soares, A. (eds) PRO-VE 2016. IAICT, vol. 480, pp. 156–162. Springer, Cham (2016). doi: 10.1007/978-3-319-45390-3_14
  11. 11.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM (2010)Google Scholar
  12. 12.
    Cameron, M.A., Power, R., Robinson, B., Yin, J.: Emergency situation awareness from twitter for crisis management. In: Proceedings of the 21st International Conference on World Wide Web, pp. 695–698. ACM (2012)Google Scholar
  13. 13.
    Olteanu, A., Vieweg, S., Castillo, C.: 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. ACM (2015)Google Scholar
  14. 14.
    Olteanu, A., Castillo, C., Diaz, F., Vieweg, S.: CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises. In: ICWSM (2014)Google Scholar
  15. 15.
    Temnikova, I., Castillo, C., Vieweg, S.: EMTerms 1.0: a terminological resource for crisis tweets. In: ISCRAM 2015 Proceedings of the 12th International Conference on Information Systems for Crisis Response and Management (2015)Google Scholar
  16. 16.
    Johnson, R., Zhang, T.: Effective use of word order for text categorization with convolutional neural networks. arXiv preprint arXiv:1412.1058 (2014)
  17. 17.
    Caragea, C., Silvescu, A., Tapia, A.H.: Identifying informative messages in disaster events using convolutional neural networks. In: International Conference on Information Systems for Crisis Response and Management (2016)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Aurélie Montarnal
    • 1
  • Shane Halse
    • 2
  • Andrea Tapia
    • 2
  • Sébastien Truptil
    • 1
  • Frederick Benaben
    • 1
  1. 1.IMT Mines AlbiUniversity of ToulouseAlbiFrance
  2. 2.Penn State UniversityState CollegeUSA

Personalised recommendations