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| Vector Autoregression Model Based Microblog Hidden Topic Popularity Prediction |
| DUAN Dongsheng1, LI Pengxiao1, LI Yuhua2, LI Ruixuan2 |
1.National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029
2.School of Computer Science and Technology, Huazhong University of Science and Technology,Wuhan 430074 |
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Abstract The existing topic popularity prediction methods predict the topic popularity just based on the features of topic and the correlations between different topics are not taken into account. However, there are correlations among different topics in microblog contexts, especially for the topics of the same event. Aiming at this problem, dynamic topic model is firstly employed to detect the hidden topics and their popularity time series from microblogs in this paper. Then, the Jensen-Shannon divergence and Pearson′s correlation coefficient are computed to investigate the correlations among topic contents and among topic time-series, respectively. Thus, the motivation of introducing topics correlation is revealed. Finally, a vector auto-regressive (VAR) model based Microblog hidden topic popularity prediction algorithm is proposed by introducing correlations among different topic time-series in model training. Experiments are conducted on the real data. Experimental results show that the proposed algorithm performs better in prediction accuracy and model fitting than algorithms without consideration of correlations among different topics.
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Received: 19 May 2015
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| About author:: DUAN Dongsheng, born in 1987, Ph.D., engineer. His research interests include social network analysis and mining.
LI Pengxiao (Corresponding author), born in 1986, Ph.D., engineer. His research interests include communication technology.LI Yuhua, born in 1968, Ph.D., associate professor. Her research interests include social network mining, graph mining and semantic web.
LI Ruixuan, born in 1974, Ph.D., professor. His research interests include cloud computing, information retrieval, social network and system security. |
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2016, Vol. 29 
