A probabilistic approach to spatiotemporal theme pattern mining on weblogs

Qiaozhu Mei, Chao Liu, Hang Su, Chengxiang Zhai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 15th International Conference on World Wide Web
Pages533-542
Number of pages10
DOIs
StatePublished - 2006
Event15th International Conference on World Wide Web - Edinburgh, Scotland, United Kingdom
Duration: May 23 2006May 26 2006

Publication series

NameProceedings of the 15th International Conference on World Wide Web

Other

Other15th International Conference on World Wide Web
Country/TerritoryUnited Kingdom
CityEdinburgh, Scotland
Period5/23/065/26/06

Keywords

  • Mixture model
  • Spatiotemporal text mining
  • Theme pattern
  • Weblog

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Fingerprint

Dive into the research topics of 'A probabilistic approach to spatiotemporal theme pattern mining on weblogs'. Together they form a unique fingerprint.

Cite this