# Trend Prediction of Event Popularity from Microblogs
###### Abstract  
Owing to rapid development of the Internet and the rise of the big data era, microblog has become the main means for people to spread and obtain information. If people can accurately predict the development trend of a microblog event, it will be of great significance for the government to carry out public relations activities on network event supervision and guide the development of microblog event reasonably for network crisis. This paper presents effective solutions to deal with trend prediction of microblog events' popularity. Firstly, by selecting the influence factors and quantifying the weight of each factor with an  
(2) How can the future trend of nonlinear time series be predicted? The popularity evolution of microblog events tends to be a nonlinear and irregular time series. Therefore, to solve the popularity trend prediction, it is necessary to extract the trend components of popularity time series. However, statistical learning methods or traditional neural network methods have a poor prediction effect on nonlinear data. Consequently, this paper needs to design an effective prediction method to denoise the time series data and extract the different components for trend prediction.  
Aiming at these issues, this paper presents an effective approach to predicting the trend of microblog events' popularity. Firstly, the popularity time series of microblog events is modeled by comprehensive weighting. Secondly, the information entropy algorithm is used to measure the effects of various factors on the popularity of microblog events. Meanwhile, aiming to explore and predict the evolution trend of microblog events' popularity, this paper transforms the changes between every two time nodes in the time series into state features. Then, the box-plot method is used to divide the popularity of the microblog event into various trend spaces. Finally, this paper utilizes the Bi-LSTM model to solve the trend prediction of microblog events' popularity by learning the long-term dependence between the time steps of popularity time series.  
In summary, the following contributions are made in this paper.  
(1) Aiming to deal with time series modeling for nonlinear data, this paper leverages an effective model based on series data analysis to extract trend components of popularity time series. Specifically, by selecting the influence factors and quantifying the weight of each factor with information entropy algorithm, the microblog event popularity is modeled. And then, the singular spectrum analysis is carried out to decompose and reconstruct the time series of the popularity of microblog event (Section 3).  
(2) This paper exploits the learning method to deal with trend prediction with a sequence to label model. Firstly, this paper models the Bi-LSTM network using past and future data from time series. Secondly, by learning the long-term dependence between the time steps of the popularity time series, the future trend prediction of microblog events is solved. Compared with the traditional LSTM model, our proposal based on Bi-LSTM network has better prediction performance (Section 3).  
(3) This paper conducts experiments on a real microblog dataset from the Sina Weibo platform. Compared with three general-purpose algorithms for popularity prediction, the experimental results show that our approach achieves the best performance compared to its competitors, which provides a new solution to the trend prediction for event evolution on microblog platforms (Section 4).