Topic Popularity Prediction of Microblog Based on Wavelet Transformation and ARIMA
CHEN Yu-Zhong, FANG Ming-Yue, GUO Wen-Zhong, GUO Kun
1.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108 2.Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing,Fuzhou University, Fuzhou 350108
Abstract The research of topic popularity prediction problem can be an important significance for maximizing the propagation effects of network advertisements as well as guiding and controlling the network consensus. Firstly, according to user relationship and topic factor, user influence is computed and topic influence is defined. Then, based on aging theory, topic energy value is calculated by considering both topic influence and the number of microblogs related to the topic, and the heat of topic is quantified. Finally, an method named topic popularity prediction of microblog based on wavelet transformation and autoregressive integrated moving average model is proposed to predict topic popularity of microblog and forecast when a topic will hit the peak. Experimental results show that the proposed method can effectively predict the topic popularity and the peak of a topic with lower residual error and omission rate.
CHEN Yu-Zhong,FANG Ming-Yue,GUO Wen-Zhong等. Topic Popularity Prediction of Microblog Based on Wavelet Transformation and ARIMA[J]. , 2015, 28(7): 586-594.
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2015, Vol. 28 
