A Sparse Topic Model for Bursty Topic Discovery
in Social Networks
Lei Shi, Junping
Du, and Feifei Kou
Beijing Key Laboratory
of Intelligent Telecommunication Software and Multimedia, Beijing University of
Posts and Telecommunications, Beijing
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.
Keywords: Bursty topic discovery, topic
model, “Spike and Slab” prior.
Received August 15, 2017; accepted January
28, 2019
https://doi.org/10.34028/iajit/17/5/15
