World Wide Web

, Volume 18, Issue 5, pp 1393–1417 | Cite as

Emerging event detection in social networks with location sensitivity

Article

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.

Keywords

Emerging event detection Location-based social networks Short text clustering Synonym expansion Conceptual similarity 

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References

  1. 1.
    Aggarwal, C.C., Subbian, K.: Event detection in social streams. In: SDM, pp. 624–635 (2012)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    Banerjee, S., Pedersen, T.: An adapted lesk algorithm for word sense disambiguation using wordnet. In: CICLing, pp. 136–145 (2002)Google Scholar
  4. 4.
    Banerjee, S., Ramanathan, K., Gupta, A.: Clustering short texts using wikipedia. In: SIGIR, pp. 787–788 (2007)Google Scholar
  5. 5.
    Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on twitter. In: ICWSM, pp. 438–441 (2011)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification and Scene Analysis, 2 edn. John Wiley & Sons Inc (2001)Google Scholar
  8. 8.
    Fang, H.: A re-examination of query expansion using lexical resources. In: ACL: HLT, pp. 139–147 (2008)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    Hotho, A., Staab, S., Stumme, G.: Ontologies improve text document clustering. In: ICDM, pp. 541–544 (2003)Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    Li, C., Sun, A., Datta, A.: Twevent: segment-based event detection from tweets. In: CIKM, pp. 155–164 (2012)Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    Mathioudakis, M., Koudas, N.: Twittermonitor: trend detection over the twitter stream. In: SIGMOD Conference, pp. 1155–1158 (2010)Google Scholar
  18. 18.
    Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995). doi:10.1145/219717.219748 CrossRefMATHGoogle Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    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)Google Scholar
  21. 21.
    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)Google Scholar
  22. 22.
    Ruthven, I., Lalmas, M.: A survey on the use of relevance feedback for information access systems. Knowl. Eng. Rev. 18(2), 95–145 (2003)CrossRefGoogle Scholar
  23. 23.
    Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)CrossRefGoogle Scholar
  24. 24.
    Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., Sperling, J.: Twitterstand: news in tweets. In: GIS, pp. 42–51 (2009)Google Scholar
  25. 25.
    Sayyadi, H., Hurst, M., Maykov, A.: Event detection and tracking in social streams. In: ICWSM, pp. 311–314 (2009)Google Scholar
  26. 26.
    Unankard, S., Li, X., Sharaf, M.A.: Location-based emerging event detection in social networks. In: APWeb, pp. 280–291 (2013)Google Scholar
  27. 27.
    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)Google Scholar
  28. 28.
    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)Google Scholar
  29. 29.
    Weng, J., Lee, B.S.: Event detection in twitter. In: ICWSM, pp. 401–408 (2011)Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia

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