Qiaozhu Mei, Chao Liu, Hang Su, and ChengXiang Zhai
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 di®erent data sets show that the proposed approach can discover interesting spatiotemporal theme patterns e®ectively. The proposed probabilistic model is general and can be used for spatiotemporal text mining on any domain with time and location information.
In Proceedings of the 15th international conference on World Wide Web (WWW'06)
Publisher Association for Computing Machinery, Inc.
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