The Spontaneous Behavior in Extreme Events: A Clustering-Based Quantitative Analysis

1. Yin, J., Lampert, A., Cameron, M., Robinson, B., Power, R.: Using social media to enhance emergency situation awareness. IEEE Intelligent Systems 27(6), 52–59 (2012)

   Article  Google Scholar 

2. Adam, N.R., Shafiq, B., Staffin, R.: Spatial computing and social media in the context of disaster management. IEEE Intelligent Systems 27(6), 90–96 (2012)

   Article  Google Scholar 

3. Jones, J.H., Salathe, M.: Early assessment of anxiety and behavioral response to novel swine-origin influenza A(H1N1). PLoS ONE 4(12), e8032 (2009)

   Google Scholar 

4. Oh, O., Kwon, K.H., Rao, H.R.: An exploration of social media in extreme events: rumor theory and twitter during the Haiti earthquake 2010. In: Proceedings of 31st International Conference on Information Systems (ICIS 2010), paper 231 (2010)

   Google Scholar 

5. Onnela, J.P., Reed-Tsochas, F.: Spontaneous emergence of social influence in online systems. Proceedings of the National Academy of Sciences 107(43), 18375–18380 (2010)

   Article  Google Scholar 

6. Lehmann, J., Goncalves, B., Ramasco, J.J., Cattuto, C.: Dynamical classes of collective attention in Twitter. In: Proceedings of the 21st World Wide Web Conference (WWW 2012), pp. 251–260 (2012)

   Google Scholar 

7. Signorini, A., Segre, A.M., Polgreen, P.M.: The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza a H1N1 pandemic. PLoS ONE 6(5), e19467 (2011)

   Google Scholar 

8. Sasahara, K., Hirata, Y., Toyoda, M., Kitsuregawa, M., Aihara, K.: Quantifying collective attention from Tweet stream. PLoS ONE 8(4), e61823 (2013)

   Google Scholar 

9. Crane, R., Sornette, D.: Robust dynamic classes revealed by measuring the response function of a social system. Proceedings of the National Academy of Sciences of the United States of America 105(41), 15649–15653 (2008)

   Article  Google Scholar 

10. Sakaki, T., Okazaki, M., Matsuo, Y.: Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Transactions on Knowledge and Data Engineering 25(4), 919–931 (2013)

    Article  Google Scholar 

11. Sarma, A.D., Jain, A., Yu, C.: Dynamic relationship and event discovery. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM 2011), pp. 207–216 (2011)

    Google Scholar 

12. Mendonca, D., Wallace, W.A.: A cognitive model of improvisation in emergency management. IEEE Transactions on Systems, Man, and Cybernetics- Part A: System and Humans 37(4), 547–561 (2007)

    Article  Google Scholar 

13. Lindell, M.K., Prater, C.S., Perry, R.W.: Fundamentals of Emergency Management. Federal Emergency Management Agency Emergency Management Institute, Emmitsburg (2006)

    Google Scholar 

14. Gao, C., Liu, J.: Clustering-based media analysis for understanding human emotional reactions in an extreme event. In: Chen, L., Felfernig, A., Liu, J., Raś, Z.W. (eds.) ISMIS 2012. LNCS, vol. 7661, pp. 125–135. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

15. Figueriredo, F., Benevenuto, F., Almeida, J.: The tube over time: characterizing popularity growth of youtube videos. In: Proceedings of the 4th International Conference on Web Search and Data Mining (WSDM 2011), pp. 745–754 (2011)

    Google Scholar 

16. Bassett, D.S., Alderson, D.L., Carlson, J.M.: Collective decision dynamics in the presence of external drivers. Physical Review E 85, 036105 (2012)

    Google Scholar 

17. Myers, S., Chenguang, Z., Leskovec, J.: Information diffusion and external influence in networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012), pp. 33–41 (2012)

    Google Scholar 

18. de Menezes, M.A., Barabasi, A.-L.: Separating internal and external dynamics of complex systems. Physical Review Letters 93(6), 068701 (2004)

    Google Scholar 

19. Thelwall, M., Wilkinson, D., Uppal, S.: Data mining emotion in social network communication: Gender differences in MySpace. Journal of the American Society for Information Science and Technology 61(1), 190–199 (2010)

    Article  Google Scholar 

20. O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM 2010), pp. 122–129 (2010)

    Google Scholar 

21. Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science 2(1), 1–8 (2011)

    Article  Google Scholar 

22. Christakis, N.A., Fowler, J.H.: The spread of obesity in a large social network over 32 Years. The New England Journal of Medicine 357(4), 370–379 (2007)

    Article  Google Scholar 

23. Durham, D., Casman, E.: Incorporating Individual health-protective decisions into disease transmission models: a mathematical framework. Journal of The Royal Society Interface 9, 562–570 (2012)

    Article  Google Scholar 

24. Moran, J., Cordaro, J.: Understanding the hit-rate dynamics of a large website with an agent-based model. In: Processing of 8th International Conference on Autonomous Agents and Multiagent System (AAMAS 2009), pp. 105–109 (2009)

    Google Scholar 

25. Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event eetection by social sensors. In: Proceedings of the 19th International World Wide Web Conference (WWW 2010), pp. 851–860 (2010)

    Google Scholar 

26. Sano, Y., Yamada, K., Watanabe, H., Takayasu, H., Takayasu, M.: Empirical analysis of collective human behavior for extraordinary events in the blogosphere. Physical Review E 87(1), 012805 (2013)

    Google Scholar 

27. Matsubara, Y., Sakurai, Y., Prakash, B.A., Li, L., Faloutsos, C.: Rise and fall patterns of information diffusion: model and implications. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012), pp. 6–14 (2012)

    Google Scholar 

28. Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Physical Review E 69(6), 066133 (2004)

    Google Scholar 

Download references