A spatio-temporal framework for related topic search in micro-blogging

Authors:
Shuangyong Song;Qiudan Li;Nan Zheng
Affiliations:
Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
AMT'10 Proceedings of the 6th international conference on Active media technology
Year:
2010

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Abstract

With the rapid development of Web 2.0, micro-blogging such as twitter is increasingly becoming an important source of up-to-date topics about what is happening in the world. By analyzing topic trends sequences and identifying relations among topics we have opportunities to gain insights into topic associations and thereby provide better services for micro-bloggers. This paper proposes a novel framework that mines the associations among topic trends in twitter by considering both temporal and location information. The framework consists of the extraction of topics' spatio-temporal information and the calculation of the similarity among topics. The experimental results show that our method can find the related topics effectively and accurately.