A Probabilistic Approach to Spatiotemporal Theme Pattern Mining on Weblogs

• Qiaozhu Mei, Department of Computer Science University of Illinois at Urbana-Champaign, USA
• Chao Liu, Department of Computer Science University of Illinois at Urbana-Champaign, USA
• Hang Su, Department of EECS Vanderbilt University, USA
• ChengXiang Zhai, Department of Computer Science University of Illinois at Urbana-Champaign, USA

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Track: Data Mining

Slot: 11:00-12:30, Friday 26th May

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.