Abstract
Social networks provide a wealth of online sources about real-world events. Due to the large volume of data in social streams, the event detection suffers from high computational complexity. In this work, we present a location-based event detection approach using Locality-Sensitive Hashing to accelerate the similarity comparison. We use this approach to detect real-world events from Sina Weibo by clustering microblogs with high similarities. We propose a message-mentioned location extraction method based on the textual content based on Part-of-Speech tagging and a Support Vector Machine classifier and a novel similarity measurement considering content, location, and time between messages to improve the precision of event detection. We compare our approach with the state-of-the-art baselines on event detection, and demonstrate the effectiveness of our approach.
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References
TDT 2004: Annotation manual. http://www.ldc.upenn.edu/Projects/TDT2004
Unankard, S., Li, X., Sharaf, M.A.: Location-based emerging event detection in social networks. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds.) APWeb 2013. LNCS, vol. 7808, pp. 280–291. Springer, Heidelberg (2013)
Zhou, X., Chen, L.: Event detection over Twitter social media streams. VLDB J. 23(3), 381–400 (2014)
Petrović, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to Twitter. In: NACL, pp. 181–189. ACL (2010)
Petrović, S., Osborne, M., Lavrenko, V.: Using paraphrases for improving first story detection in news and Twitter. In: NACL, pp. 338–346. ACL (2012)
Becker, H., Naaman, M., Gravano, L.: Learning similarity metrics for event identification in social media. In: WSDM, pp.291–300. ACM(2010)
Lee, C.: Mining spatio-temporal information on microblogging streams using a density-based online clustering method. Expert Syst. Appl. 39, 9623–9641 (2012). Elseiver
Cataldi, M., Caro, L.D., Schifanella, C.: Emerging topic detection on twitter based on temporal and social terms evaluation. In: MDMKDD, no. 4. ACM(2010)
Ozdikis, O., Senkul, P., Oguztuzun, H.: Semantic expansion of hashtags for enhanced event detection in Twitter. In: VLDB (2012)
Charikar, M.S.: Similarity estimation techniques from rounding algorithms. In: STOC, pp. 380–388. ACM(2002)
Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., Sperling, J.: TwitterStand: news in Tweets. In: GIS, pp.42–51. ACM (2009)
Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2011)
Sayyadi, H., Hurst, M., Maykov, A.: Event detection and tracking in social streams. In: ICWSM, pp. 311–314 (2009)
Becker, H., Naaman, M.R, Gravano, L.: Beyond trending topics: real-world event identification on Twitter. In: AAAI, pp. 438–441 (2011)
Tan, Z., Zhang, P., Tan, J.: A multi-layer event detection algorithm for detecting global and local hot events in social networks. Procedia Comput. Sci. 29, 2080–2089 (2014)
Wang, Y., Agichtein, E., Benzi, M.: TM-LDA: efficient online modeling of latent topic transitions in social media. In: KDD, pp. 123–131. ACM (2012)
Baldwin, T., Cook, P., Han, B., Harwood, A., Karunasekera, S., Moshtaghi, M.: A support platform for event detection using social intelligence. In: EACL, pp. 69–72. ACL (2012)
Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating Twitter users. In: CIKM, pp. 759–768. ACM (2010)
Li, W., Serdyukov, P., de Vries, A.P., Eickhoff, C., Larson, M.: the where in the Tweet. In: CIKM, pp. 2473–2476. ACM (2011)
Acknowledgements
The work is supported by the National Key Technology R&D Program of China under Grant No. 2012BAH75F03 and 2013BAH61F01.
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Feng, X., Zhang, S., Liang, W., Liu, J. (2015). Efficient Location-Based Event Detection in Social Text Streams. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_21
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DOI: https://doi.org/10.1007/978-3-319-23862-3_21
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