A unified model for stable and temporal topic detection from social media data

Yin, Hongzhi, Cui, Bin, Lu, Hua, Huang, Yuxin and Yao, Junjie (2013). A unified model for stable and temporal topic detection from social media data. In: Christian S. Jensen, Chris Jermaine, Jiaheng Lu, Egemen Tanin and Xiaofang Zhou, International Conference on Data Engineering, Brisbane, Australia, (661-672). 8-11 April 2013. doi:10.1109/ICDE.2013.6544864

Author Yin, Hongzhi
Cui, Bin
Lu, Hua
Huang, Yuxin
Yao, Junjie
Title of paper A unified model for stable and temporal topic detection from social media data
Conference name International Conference on Data Engineering
Conference location Brisbane, Australia
Conference dates 8-11 April 2013
Journal name Proceedings - International Conference on Data Engineering   Check publisher's open access policy
Series International Conference on Data Engineering
Place of Publication Washington, DC, United States
Publisher I E E E Computer Society
Publication Year 2013
Year available 2013
Sub-type Fully published paper
DOI 10.1109/ICDE.2013.6544864
ISBN 9781467349086
ISSN 1084-4627
Editor Christian S. Jensen
Chris Jermaine
Jiaheng Lu
Egemen Tanin
Xiaofang Zhou
Start page 661
End page 672
Total pages 12
Language eng
Abstract/Summary Web 2.0 users generate and spread huge amounts of messages in online social media. Such user-generated contents are mixture of temporal topics (e.g., breaking events) and stable topics (e.g., user interests). Due to their different natures, it is important and useful to distinguish temporal topics from stable topics in social media. However, such a discrimination is very challenging because the user-generated texts in social media are very short in length and thus lack useful linguistic features for precise analysis using traditional approaches. In this paper, we propose a novel solution to detect both stable and temporal topics simultaneously from social media data. Specifically, a unified user-temporal mixture model is proposed to distinguish temporal topics from stable topics. To improve this model’s performance, we design a regularization framework that exploits prior spatial information in a social network, as well as a burst-weighted smoothing scheme that exploits temporal prior information in the time dimension. We conduct extensive experiments to evaluate our proposal on two real data sets obtained from Del.icio.us and Twitter. The experimental results verify that our mixture model is able to distinguish temporal topics from stable topics in a single detection process. Our mixture model enhanced with the spatial regularization and the burst-weighted smoothing scheme significantly outperforms competitor approaches, in terms of topic detection accuracy and discrimination in stable and temporal topics.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

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Created: Sun, 01 Mar 2015, 11:59:01 EST by Hongzhi Yin on behalf of School of Information Technol and Elec Engineering