Abstract
Detecting events in microblog is important but still challenging. As tweet stream is a mixture of user interests and external events, its expensive to distinguish them. Existing methods are ineffective since they ignore user interests or only model interests and events on a fixed dataset without scalability. In this paper, we introduce an online learning model User Modeling Based Interest and Event Topic Model (UMIETM). UMIETM (1) exploits user modeling’s information to discover events, which usually capture attentions from users with different interests, and (2) treats the arriving data as stream and run the detection in online learning style. Furthermore, UMIETM can handle dynamic increased vocabulary in tweet stream. The UMIETM is verified on the real dataset which spans one year and contains 16 million tweets, and it outperforms state-of-the-art models in quantitative.
This research is supported by the Natural Science Foundation of China (Grant No. 61300003, 61572043), and the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20130001120001).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lau, J.H., Collier, N., Baldwin, T.: On-line trend analysis with topic models: #twitter trends detection topic model online. In COLING, 2012
Diao, Q., Jiang, J., Zhu, F., Lim, E.-P.: Finding bursty topics from microblogs. In: ACL (2012)
He, Q., Chang, K., Lim, E.-P.: Analyzing feature trajectories for event detection. In: ACM SIGIR conference on Research and Development in information retrieval, pp. 207–214. ACM (2007)
Weng, J., Lee, B.-S.: Event detection in twitter. In: ICWSM (2011)
Petrović, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to twitter. In: HLT-NAACL (2010)
McCreadie, R., Macdonald, C., Ounis, I., Osborne, M., Petrovic, S.: Scalable distributed event detection for twitter (2013)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. JMLR (2003)
Diao, Q., Jiang, J.: A unified model for topics, events and users on twitter. In: EMNLP (2013)
Yan, X., Guo, J., Lan, Y., Jun, X., Cheng, X.: A Probabilistic Model for Bursty Topic Discovery in Microblogs. AAAI, pp. 353–359 (2015)
Zhao, Y., Wang, G., Yu, P.S., Liu, S., Zhang, S.: Simon: inferring social roles and statuses in social networks. In: SIGKDD. ACM (2013)
Yoshida, T.: Toward finding hidden communities based on user profile. J. Intell. Inf. Syst. 40(2), 189–209 (2013)
Culotta, A., Kumar, N.R., Cutler, J.: Predicting the demographics of twitter users from website traffic data. In: AAAI, pp. 72–78 (2015)
Faralli, S., Stilo, G., Velardi, P.: Large scale homophily analysis in twitter using a twixonomy. In: IJCAI, pp. 2334–2340. AAAI Press (2015)
Blei, D.M., Jordan, M.I.: Modeling annotated data. In: SIGIR (2003)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. PNAS (2004)
Zhao, W.X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., Li, X.: Comparing twitter and traditional media using topic models. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 338–349. Springer, Heidelberg (2011)
Quan, X., Kit, C., Ge, Y., Pan, S.J.: Short and sparse text topic modeling via self-aggregation. IJCAI, pp. 2270–2276 (2015)
Wallach, H.M.: Structured topic models for language. Doctoral dissertation, Univ. of Cambridge (2008)
Wallach, H.M., Murray, I., Salakhutdinov, R., Mimno, D.: Evaluation methods for topic models. In: ICML (2009)
Wallach, H.M., Mimno, D.M., McCallum, A.: Rethinking lda: Why priors matter. In: NIPS, vol. 22, pp. 1973–1981 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Huang, W., Chen, W., Zhang, L., Wang, T. (2016). An Efficient Online Event Detection Method for Microblogs via User Modeling. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-45814-4_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45813-7
Online ISBN: 978-3-319-45814-4
eBook Packages: Computer ScienceComputer Science (R0)