A probabilistic approach to spatiotemporal theme pattern mining on weblogs

ABSTRACT

Mining subtopics from weblogs and analyzing their spatiotemporal patterns have applications in multiple domains. In this paper, we define the novel problem of mining spatiotemporal theme patterns from weblogs and propose a novel probabilistic approach to model the subtopic themes and spatiotemporal theme patterns simultaneously. The proposed model discovers spatiotemporal theme patterns by (1) extracting common themes from weblogs; (2) generating theme life cycles for each given location; and (3) generating theme snapshots for each given time period. Evolution of patterns can be discovered by comparative analysis of theme life cycles and theme snapshots. Experiments on three different data sets show that the proposed approach can discover interesting spatiotemporal theme patterns effectively. The proposed probabilistic model is general and can be used for spatiotemporal text mining on any domain with time and location information.

References

1. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, 2003.]]
2. S. Boykin and A. Merlino. Machine learning of event segmentation for news on demand. Commun. ACM, 43(2):35--41, 2000.]]
3. W. B. Croft and J. Lafferty, editors. Language Modeling and Information Retrieval. Kluwer Academic Publishers, 2003.]]
4. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal Statist. Soc. B, 39:1--38, 1977.]]
5. U. Fayyad, D. Haussler, and P. Stolorz. Mining scientific data. Commun. ACM, 39(11):51--57, 1996.]]
6. E. Gabrilovich, S. Dumais, and E. Horvitz. Newsjunkie: providing personalized newsfeeds via analysis of information novelty. In Proceedings of the 13th international conference on World Wide Web, pages 482--490, 2004.]]
7. K. E. Gill. Blogging, rss and the information landscape: A look at online news. In WWW 2005 Workshop on the Weblogging Ecosystem, 2005.]]
8. N. Glance, M. Hurst, and T. Tornkiyo. Blogpulse: Automated trend discovery for weblogs. In WWW 2004 Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics, 2004.]]
9. T. L. Gri'ths and M. Steyvers. Fiding scientific topics. Proceedings of the National Academy of Sciences, 101(suppl.1):5228--5235, 2004.]]
10. D. Gruhl, R. Guha, R. Kumar, J. Novak, and A. Tomkins. The predictive power of online chatter. In Proceeding of KDD '05, pages 78--87, 2005.]]
11. D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. Information diffusion through blogspace. In Proceedings of the 13th international conference on World Wide Web, pages 491--501, 2004.]]
12. T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of SIGIR '99, pages 50--57, 1999.]]
13. J. Kleinberg. Bursty and hierarchical structure in streams. In Proceedings of KDD '02, pages 91--101, 2002.]]
14. A. Kontostathis, L. Galitsky, W. M. Pottenger, S. Roy, and D. J. Phelps. A survey of emerging trend detection in textual data mining. Survey of Text Mining, pages 185--224, 2003.]]
15. R. Krovetz. Viewing morphology as an inference process. In Proceedings of SIGIR '93, pages 191--202, 1993.]]
16. R. Kumar, J. Novak, P. Raghavan, and A. Tomkins. On the bursty evolution of blogspace. In Proceedings of the 12th international conference on World Wide Web, pages 568--576, 2003.]]
17. R. Kumar, J. Novak, P. Raghavan, and A. Tomkins. Structure and evolution of blogspace. Commun. ACM, 47(12):35--39, 2004.]]
18. Z. Li, B. Wang, M. Li, and W.-Y. Ma. A probabilistic model for retrospective news event detection. In Proceedings of SIGIR '05, pages 106--113, 2005.]]
19. J. Ma and S. Perkins. Online novelty detection on temporal sequences. In Proceedings of KDD '03, pages 613--618, 2003.]]
20. N. Mamoulis, H. Cao, G. Kollios, M. Hadjieleftheriou, Y. Tao, and D. W. Cheung. Mining, indexing, and querying historical spatiotemporal data. In Proceedings of KDD '04, pages 236--245, 2004.]]
21. Q. Mei and C. Zhai. Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In Proceeding of KDD '05, pages 198--207, 2005.]]
22. S. Morinaga and K. Yamanishi. Tracking dynamics of topic trends using finite mixture model. In Proceedings of KDD '04, pages 811--816, 2004.]]
23. D. B. Neill, A. W. Moore, M. Sabhnani, and K. Daniel. Detection of emerging space-time clusters. In Proceeding of KDD '05, pages 218--227, 2005.]]
24. J. Perkio, W. Buntine, and S. Perttu. Exploring independent trends in a topic-based search engine. In Proceedings of WI'04, pages 664--668, 2004.]]
25. K. Rajaraman and A.-H. Tan. Topic detection, tracking, and trend analysis using self-organizing neural networks. In PAKDD, pages 102--107, 2001.]]
26. B. Tseng, J. Tatemura, and Y. Wu. Tomographic clustering to visualize blog communities as mountain views. In WWW 2005 Workshop on the Weblogging Ecosystem, 2005.]]
27. C. Zhai, A. Velivelli, and B. Yu. A cross-collection mixture model for comparative text mining. In Proceedings of KDD '04, pages 743--748, 2004.]]

Index Terms

• Information systems

  • Information retrieval