Abstract
With the increasing number of real-world events that are originated and discussed over social networks, event detection is becoming a compelling research issue. However, the traditional approaches to event detection on large text streams are not designed to deal with a large number of short and noisy messages. This paper proposes an approach for the early detection of emerging hotspot events in social networks with location sensitivity. We consider the message-mentioned locations for identifying the locations of events. In our approach, we identify strong correlations between user locations and event locations in detecting the emerging events. We evaluate our approach based on a real-world Twitter dataset. Our experiments show that the proposed approach can effectively detect emerging events with respect to user locations that have different granularities.
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Aggarwal, C.C., Subbian, K.: Event detection in social streams. In: SDM, pp. 624–635 (2012)
Alvanaki, F., Michel, S., Ramamritham, K., Weikum, G.: See what’s enblogue: real-time emergent topic identification in social media. In: EDBT, pp. 336–347 (2012)
Banerjee, S., Pedersen, T.: An adapted lesk algorithm for word sense disambiguation using wordnet. In: CICLing, pp. 136–145 (2002)
Banerjee, S., Ramanathan, K., Gupta, A.: Clustering short texts using wikipedia. In: SIGIR, pp. 787–788 (2007)
Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on twitter. In: ICWSM, pp. 438–441 (2011)
Cataldi, M., Caro, L.D., Schifanella, C.: Emerging topic detection on twitter based on temporal and social terms evaluation. In: MDMKDD, pp. 4:1–4:10 (2010)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification and Scene Analysis, 2 edn. John Wiley & Sons Inc (2001)
Fang, H.: A re-examination of query expansion using lexical resources. In: ACL: HLT, pp. 139–147 (2008)
Gimpel, K., Schneider, N., O’Connor, B., Das, D., Mills, D., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J., Smith, N.A.: Part-of-speech tagging for twitter: Annotation, features, and experiments. In: ACL, pp. 42–47 (2011)
Hotho, A., Staab, S., Stumme, G.: Ontologies improve text document clustering. In: ICDM, pp. 541–544 (2003)
Hu, J., Fang, L., Cao, Y., Zeng, H.J., Li, H., Yang, Q., Chen, Z.: Enhancing text clustering by leveraging wikipedia semantics. In: SIGIR, pp. 179–186 (2008)
Huang, A.L., Milne, D.N., Frank, E., Witten, I.H.: Clustering documents using a wikipedia-based concept representation. In: PAKDD, pp. 628–636 (2009)
Jurafsky, D., Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, vol. 163. Prentice Hall (2000)
Li, C., Sun, A., Datta, A.: Twevent: segment-based event detection from tweets. In: CIKM, pp. 155–164 (2012)
Liu, S., Liu, F., Yu, C.T., Meng, W.: An effective approach to document retrieval via utilizing wordnet and recognizing phrases. In: SIGIR, pp. 266–272 (2004)
Marcus, A., Bernstein, M.S., Badar, O., Karger, D.R., Madden, S., Miller, R.C.: Twitinfo: aggregating and visualizing microblogs for event exploration. In: CHI, pp. 227–236 (2011)
Mathioudakis, M., Koudas, N.: Twittermonitor: trend detection over the twitter stream. In: SIGMOD Conference, pp. 1155–1158 (2010)
Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995). doi:10.1145/219717.219748
Ni, X., Quan, X., Lu, Z., Wenyin, L., Hua, B.: Short text clustering by finding core terms. Knowl. Inf. Syst. 27(3), 345–365 (2011)
Ozdikis, O., Senkul, P., Oguztuzun, H.: Semantic expansion of hashtags for enhanced event detection in twitter. In: VLDB-WOSS, pp. 1:1–1:6 (2012)
Peng, J., Yang, D., Tang, S.W., Gao, J., yi Zhang, P., Fu, Y.: A concept similarity based text classification algorithm. In: FSKD (1), pp. 535–539 (2007)
Ruthven, I., Lalmas, M.: A survey on the use of relevance feedback for information access systems. Knowl. Eng. Rev. 18(2), 95–145 (2003)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)
Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., Sperling, J.: Twitterstand: news in tweets. In: GIS, pp. 42–51 (2009)
Sayyadi, H., Hurst, M., Maykov, A.: Event detection and tracking in social streams. In: ICWSM, pp. 311–314 (2009)
Unankard, S., Li, X., Sharaf, M.A.: Location-based emerging event detection in social networks. In: APWeb, pp. 280–291 (2013)
Wang, J., Zhou, Y., Li, L., Hu, B., Hu, X.: Improving short text clustering performance with keyword expansion. In: ISNN (4), pp. 291–298 (2009)
Watanabe, K., Ochi, M., Okabe, M., Onai, R.: Jasmine: a real-time local-event detection system based on geolocation information propagated to microblogs. In: CIKM, pp. 2541–2544 (2011)
Weng, J., Lee, B.S.: Event detection in twitter. In: ICWSM, pp. 401–408 (2011)
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Unankard, S., Li, X. & Sharaf, M.A. Emerging event detection in social networks with location sensitivity. World Wide Web 18, 1393–1417 (2015). https://doi.org/10.1007/s11280-014-0291-3
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DOI: https://doi.org/10.1007/s11280-014-0291-3