Abstract
Social media records the pulse of social discourse and drives human behaviors in temporal and spatial dimensions, as well as the structural characteristics. These online contexts give us an opportunity to understand social perceptions of people in the context of certain events, and can help us improve disaster relief. Taking Twitter as data source, this paper quantitatively measures exogenous and endogenous social influences on collective behaviors in different events based on standard fluctuation scaling method. Different from existing studies utilizing manual keywords to denote events, we apply a clustering-based event analysis to identify the core event and its related episodes in a hashtag network. The statistical results show that exogenous factors drive the amount of information about an event and the endogenous factors play a major role in the propagation of hashtags.
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References
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)
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)
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)
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)
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)
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)
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)
Sasahara, K., Hirata, Y., Toyoda, M., Kitsuregawa, M., Aihara, K.: Quantifying collective attention from Tweet stream. PLoS ONE 8(4), e61823 (2013)
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)
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)
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)
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)
Lindell, M.K., Prater, C.S., Perry, R.W.: Fundamentals of Emergency Management. Federal Emergency Management Agency Emergency Management Institute, Emmitsburg (2006)
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)
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)
Bassett, D.S., Alderson, D.L., Carlson, J.M.: Collective decision dynamics in the presence of external drivers. Physical Review E 85, 036105 (2012)
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)
de Menezes, M.A., Barabasi, A.-L.: Separating internal and external dynamics of complex systems. Physical Review Letters 93(6), 068701 (2004)
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)
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)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science 2(1), 1–8 (2011)
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)
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)
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)
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)
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)
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)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Physical Review E 69(6), 066133 (2004)
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Shi, N., Gao, C., Zhang, Z., Zhong, L., Huang, J. (2013). The Spontaneous Behavior in Extreme Events: A Clustering-Based Quantitative Analysis. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53914-5_29
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DOI: https://doi.org/10.1007/978-3-642-53914-5_29
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