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.
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Song, S., Li, Q., Zheng, N. (2010). A Spatio-temporal Framework for Related Topic Search in Micro-Blogging. In: An, A., Lingras, P., Petty, S., Huang, R. (eds) Active Media Technology. AMT 2010. Lecture Notes in Computer Science, vol 6335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15470-6_8
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DOI: https://doi.org/10.1007/978-3-642-15470-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15469-0
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