Abstract Bursty topic discovery aims to automatically identify bursty events and continuously keep track of known events. The existing methods focus on the topic model. However, the sparsity of short text brings the challenge to the traditional topic models because the words are too few to learn from the original corpus. To tackle this problem, we propose a Sparse Topic Model (STM) for bursty topic discovery. First, we distinguish the modeling between the bursty topic and the common topic to detect the change of the words in time and discover the bursty words. Second, we introduce “Spike and Slab” prior to decouple the sparsity and smoothness of a distribution. The bursty words are leveraged to achieve automatic discovery of the bursty topics. Finally, to evaluate the effectiveness of our proposed algorithm, we collect Sina weibo dataset to conduct various experiments. Both qualitative and quantitative evaluations demonstrate that the proposed STM algorithm outperforms favorably against several state-of-the-art methods.

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[39] Zuo Y., Wu J., Zhang H., Lin H., Wang F., Xu K., and Xiong H., “Topic Modeling of Short Texts: A Pseudo-Document View,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, pp. 2105-2114, 2016. Lei Shi was born in 1986. He received the M.S. degree in Control Engineering from Inner Mongolia University of Science and Technology. He is now a Ph.D. candidate in Computer Science and Technology of Beijing University of Posts and Telecommunications. His research interests include social network search, data mining and cross-media search Junping Du was born in 1963. She is now a professor and Ph.D. tutor at the School of Computer Science and Technology, Beijing University of Posts and Telecommunications. Her research interests include artificial intelligence, image processing and pattern recognition. Feifei Kou was born in 1989. She received her M.S. degree in Computer technology from Beijing Technology and Business University. She is now a Ph.D. candidate in Computer Science and Technology of Beijing University of Posts and Telecommunications. Her research interests include social network search, semantic analysis and semantic learning.