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.

中文翻译：

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时变动态主题模型

在本文中，作者基于先前的文献来开发自适应且时变的启用元数据的动态主题模型（mDTM），并使用在线Gibbs采样器将其应用于大型Weibo数据集进行参数估计。他们的方法同时捕获了微博固有的最大动态特征，从而使其与现有文献中的其他在线文档挖掘方法区分开来。总之，作者的研究结果显示，与以前的研究相比，mDTM在挖掘信息的质量方面具有更好的性能，并展示了mDTM是在快速变化的全球信息空间中有效挖掘微博的有前途的工具。