A sparse topic model for bursty topic discovery in social networks
Joint Authors
Shi, Lei
Kou, Feifei
Du, Junping
Source
The International Arab Journal of Information Technology
Issue
Vol. 17, Issue 5 (30 Sep. 2020), pp.816-824, 9 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2020-09-30
Country of Publication
Jordan
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
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 web 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.
American Psychological Association (APA)
Shi, Lei& Du, Junping& Kou, Feifei. 2020. A sparse topic model for bursty topic discovery in social networks. The International Arab Journal of Information Technology،Vol. 17, no. 5, pp.816-824.
https://search.emarefa.net/detail/BIM-1439794
Modern Language Association (MLA)
Shi, Lei…[et al.]. A sparse topic model for bursty topic discovery in social networks. The International Arab Journal of Information Technology Vol. 17, no. 5 (Sep. 2020), pp.816-824.
https://search.emarefa.net/detail/BIM-1439794
American Medical Association (AMA)
Shi, Lei& Du, Junping& Kou, Feifei. A sparse topic model for bursty topic discovery in social networks. The International Arab Journal of Information Technology. 2020. Vol. 17, no. 5, pp.816-824.
https://search.emarefa.net/detail/BIM-1439794
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 822-824
Record ID
BIM-1439794