Abstract
As microblogging services on the mobile devices are widely used, microblogs can be viewed as a kind of event sensor to perceive the dynamic behaviors in the city. In particular, detecting live events in microblogs, such as mass gathering, emergencies, etc., can help to understand what happened from the point of view of people who are present. For identifying the live events from a large number of short and noisy microblogs, the paper builds a generative probabilistic model named the ST-LDA model to cluster the microblogs whose semantics, time and space are similar into the same topic, and then determines the live events from the topics by an HMM-based method. The paper conducts the experiments on the real microblogs from weibo.com. Experimental results show that our method can detect live events more accurately and more completely than the LDA-based method and the TimeLDA-based method.
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References
Zhang, W., Qi, G., Pan, G.: City-scale social event detection and evaluation with taxi traces. Trans. Intell. Syst. Technol. Article No. 40 6(3). ACM (2015)
Du, R., Yu, Z., Mei, T.: Predicting activity attendance in event-based social networks: content, context and social influence. In: International Joint Conference on Pervasive and Ubiquitous Computing, pp. 425–434. ACM (2014)
Lee, R., Wakamiya, S., Sumiya, K.: Discovery of unusual regional social activities using geo-tagged microblogs. World Wide Web 14(4), 321–349 (2011)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: International Conference on World Wide Web, pp. 851–860. ACM (2010)
Sankaranarayanan, J., Samet, H., Teitler, B.E.: Twitterstand: news in Tweets. In: ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 42–51. ACM (2009)
Becker, H., Naaman, M., Gravano, L.: Learning similarity metrics for event identification in social media. In: International Conference on Web Search and Data Mining, pp. 291–300. ACM (2010)
Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on Twitter. In: International Conference on Web and Social Media, pp. 438–441 (2011)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Ramage, D., Dumais, S.T., Liebling, D.J.: Characterizing microblogs with topic models. In: International Conference on Web and Social Media, pp. 130–137 (2010)
Wang, X., McCallum, A.: Topics over time: a non-Markov continuous-time model of topical trends. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 424–433. ACM (2006)
Diao, Q., Jiang, J., Zhu, F.: Finding bursty topics from microblogs. In: Annual Meeting of the Association for Computational Linguistics: Long Papers-vol. 1, pp. 536–544. ACL (2012)
Zhou, D., Chen, L., He, Y.: An unsupervised framework of exploring events on Twitter: filtering, extraction and categorization. In: 29th AAAI Conference on Artificial Intelligence, pp. 2468–2474 (2015)
Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant No. 61472408 and No. 61379044.
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Zheng, Z., Jin, B., Cui, Y., Ji, Q. (2016). Detecting Live Events by Mining Textual and Spatial-Temporal Features from Microblogs. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9659. Springer, Cham. https://doi.org/10.1007/978-3-319-39958-4_28
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DOI: https://doi.org/10.1007/978-3-319-39958-4_28
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