Abstract
Most of the new social media sites such as Twitter and Flickr are using RSS Feeds for sharing a wide variety of current and future real-world events. Indeed, RSS Feeds is considered as a powerful realtime means for real-world events sharing within the social Web. Thus, by identifying these events and their associated user-contributed social media resources, we can greatly improve event browsing and searching. However, a thriving challenge of events mining processes is owed to an efficient as well as a timely identification of events. In this paper, we are mainly dealing with event mining from heterogenous social media RSS Feeds. Therefore, we introduce a new approach, called RssE-Miner, in order to get out these events. The main thrust of the introduced approach stands in presenting a better trade-off between event mining accuracy and swiftness. Specifically, we adopted the probabilistic Naive Bayesian model within the exploitation of the rich context associated with social media Rss Feeds contents, including user-provided annotations (e.g., title, tags) and the automatically generated information (e.g., time) for efficiently mining future events. Carried out experiments over two real-world datasets emphasize the relevance of our proposal.
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References
Ahern, S., Nair, R., Kennedy, L., Naaman, M., Rattenbury, T.: How flickr helps us make sense of the world: context and content in community-contributed media collections. In: Proceedings of the 15th International Conference on Multimedia, MULTIMEDIA 2007, pp. 631–640. ACM (2007)
Allan, J.: Introduction to topic detection and tracking. In: Topic Detection and Tracking: Event-Based Information Organization, pp. 1–16. Kluwer Academic Publishers (2002)
Amer-Yahia, S., Lakshmanan, L.V.S., Benedikt, M., Stoyanovich, J.: Efficient network aware search in collaborative tagging sites. In: Proc. VLDB Endow., vol. 1(1), pp. 710–721 (2008)
Becker, H., Naaman, M., Gravano, L.: Learning similarity metrics for event identification in social media. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM 2010, pp. 291–300. ACM (2010)
Bekkerman, R., Mccallum, A., Huang, G.: Automatic categorization of email into folders:benchmark experiments on enron and sri corpora. Technical Report, Computer Science department, IR-418. pp. 4–6
Dann Wei Li, R., Abdin, K., Moore, A.: Approaching real-time network traffic classification. Technical Report, RR-06-12, Department of Computer Science, Queen Mary, University of London (October 2006)
Donato, D., Gionis, A., Agichtein, E., Castillo, C., Mishne, G.: Finding high-quality content in social media. In: Proceedings of the First ACM International Conference on Web Search and Data Mining, WSDM 2008, pp. 183–194. ACM (2008)
Drumond, L., Buza, K., Reuter, T., Cimiano, P., Schmidt-Thieme, L.: Scalable event-based clustering of social media via record linkage techniques. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, ICWSM 2011, pp. 313–320. AAAI Press (2011)
Forman, G., Cohen, I.: Learning from Little: Comparison of Classifiers Given Little Training. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 161–172. Springer, Heidelberg (2004)
Hall, M.A.: Correlation-based Feature Selection for Machine Learning. Doctoral thesis, The University of Waikato (April 1999)
Hogenboom, A., Hogenboom, F., Frasincar, F., Kaymak, U., van der Meer, O., Schouten, K.: Detecting Economic Events Using a Semantics-Based Pipeline. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) DEXA 2011, Part I. LNCS, vol. 6860, pp. 440–447. Springer, Heidelberg (2011)
Hurst, M., Sayyadi, H., Maykov, A.: Event detection and tracking in social streams. In: ICWSM. The AAAI Press (2009)
Java, A., Finin, T., Nirenburg, S.: Semnews: A semantic news framework. In: Proceedings of the 21st National Conference on Artificial Intelligence, AAAI 2006, pp. 1939–1940. AAAI Press (2006)
Kumaran, G., Allan, J.: Text classification and named entities for new event detection. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2004, pp. 25–29 (2004)
Lewis, D.D.: Naive (Bayes) at Forty. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 4–15. Springer, Heidelberg (1998)
Papka, R., Allan, J., Lavrenko, V.: On-line new event detection and tracking. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1998, pp. 37–45. ACM (1998)
Ramage, D., Heymann, P., Garcia-Molina, H.: Social tag prediction. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 531–538. ACM (2008)
Song, Y., Kolcz, A., Lee Giles, C.: Better naive bayes classification for high-precision spam detection. Softw. Pract. Exper. 39(11), 1003–1024 (2009)
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Dhahri, N., Trabelsi, C., Ben Yahia, S. (2012). RssE-Miner: A New Approach for Efficient Events Mining from Social Media RSS Feeds. In: Cuzzocrea, A., Dayal, U. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2012. Lecture Notes in Computer Science, vol 7448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32584-7_21
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DOI: https://doi.org/10.1007/978-3-642-32584-7_21
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