Time-varying dynamic topic model: A better tool for mining microblogs at a global level

Jun Han, Yu Huang, Kuldeep Kumar, Sukanto Bhattacharya

Research output: Contribution to journalArticleResearchpeer-review

Abstract

In this paper the authors build on prior literature to develop an adaptive and time-varying metadata-enabled dynamic topic model (mDTM) and apply it to a large Weibo dataset using an online Gibbs sampler for parameter estimation. Their approach simultaneously captures the maximum number of inherent dynamic features of microblogs thereby setting it apart from other online document mining methods in the extant literature. In summary, the authors' results show a better performance of mDTM in terms of the quality of the mined information compared to prior research and showcases mDTM as a promising tool for the effective mining of microblogs in a rapidly changing global information space.

Original languageEnglish
Article number6
Pages (from-to)104-119
Number of pages16
JournalJournal of Global Information Management
Volume26
Issue number1
DOIs
Publication statusPublished - 1 Jan 2018

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Metadata
Dynamic models
Parameter estimation
Time-varying
Topic model

Cite this

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Time-varying dynamic topic model : A better tool for mining microblogs at a global level. / Han, Jun; Huang, Yu; Kumar, Kuldeep; Bhattacharya, Sukanto.

In: Journal of Global Information Management, Vol. 26, No. 1, 6, 01.01.2018, p. 104-119.

Research output: Contribution to journalArticleResearchpeer-review

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