Abstract
Effective event modeling allows accurate event identification and monitoring to enable timely response to emergencies occurring in various applications. Although event identification has been extensively studied in the last decade, the triggering relationship among initial and subsequent events has not been well studied, which limits the understanding of event evolvements from both spatial and temporal dimensions. Furthermore, it is also useful to measure the impact of events to the public so that the important events can be first seen. In this paper, we propose to systematically study event modeling and ranking in a novel framework. A new method is introduced to effectively identify events by considering the spreading effect of event in the spatio-temporal space. To capture the triggering relationships among events, we adapt the self-exciting point process model by jointly considering event spatial, temporal and content similarities. As a step further, we define the event impact and rank them at different time stamps. Extensive experimental results on real-life datasets demonstrate promising performance of our proposal in identifying, monitoring and ranking events.
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References
Allan, J., Papka, R., Lavrenko, V.: On-line new event detection and tracking. In: SIGIR, pp. 37–45 (1998)
Becker, H., Naaman, M., Gravano, L.: Learning similarity metrics for event identification in social media. In: WSDM, pp. 291–300 (2010)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. JMLR 3, 993–1022 (2003)
Chen, L., Roy, A.: Event detection from flickr data through wavelet-based spatial analysis. In: CIKM, pp. 523–532 (2009)
Sarma, A.D., Jain, A., Yu, C.: Dynamic relationship and event discovery. In: WSDM, pp. 207–216 (2011)
Rodriguez, M.G., Leskovec, J., Krause, A.: Inferring networks of diffusion and influence. In: KDD, pp. 1019–1028 (2010)
Ha-Thuc, V., Mejova, Y., Harris, C., Srinivasan, P.: A relevance-based topic model for news event tracking. In: SIGIR, pp. 764–765 (2009)
Hofmann, T.: Probabilistic latent semantic indexing. In: SIGIR, pp. 50–57 (1999)
Hong, L., Yin, D., Guo, J., Davison, B.D.: Tracking trends: incorporating term volume into temporal topic models. In: KDD, pp. 484–492 (2011)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. TOIS 20(4), 422–446 (2002)
Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: KDD, pp. 497–506 (2009)
Lin, C.X., Zhao, B., Mei, Q., Han, J.: Pet: a statistical model for popular events tracking in social communities. In: KDD, pp. 929–938 (2010)
Mohler, G.O., Short, M.B., Brantingham, P.J., Schoenberg, F.P., Tita, G.E.: Self-exciting point process modeling of crime. JASA 106(493), 100–108 (2011)
Papadopoulos, S., Zigkolis, C., Kompatsiaris, Y., Vakali, A.: Cluster-Based Landmark and Event Detection for Tagged Photo Collections. IEEE MultiMedia 18, 52–63 (2011)
Rattenbury, T., Good, N., Naaman, M.: Towards automatic extraction of event and place semantics from flickr tags. In: SIGIR, pp. 103–110 (2007)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: WWW, pp. 851–860 (2010)
Sizov, S.: Geofolk: latent spatial semantics in web 2.0 social media. In: WSDM, pp. 281–290 (2010)
Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.J.: Scan: a structural clustering algorithm for networks. In: KDD, pp. 824–833. ACM (2007)
Yang, Y., Yang, Y., Shen, H.T.: Effective transfer tagging from image to video. TOMCCAP 9(2), 14 (2013)
Yin, Z., Cao, L., Han, J., Zhai, C., Huang, T.S.: Geographical topic discovery and comparison. In: WWW, pp. 247–256 (2011)
Zhuang, J., Ogata, Y., Vere-Jones, D.: Stochastic declustering of space-time earthquake occurrences. JASA 97(458), 369–380 (2002)
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Li, X., Cai, H., Huang, Z., Yang, Y., Zhou, X. (2013). Spatio-temporal Event Modeling and Ranking. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds) Web Information Systems Engineering – WISE 2013. WISE 2013. Lecture Notes in Computer Science, vol 8181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41154-0_27
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DOI: https://doi.org/10.1007/978-3-642-41154-0_27
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