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 language | English |
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Article number | 6 |
Pages (from-to) | 104-119 |
Number of pages | 16 |
Journal | Journal of Global Information Management |
Volume | 26 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Jan 2018 |