# Trend Prediction of Event Popularity from Microblogs
## 2 Related Work  
Scientific research on the evolution trend of microblog events can effectively monitor the development of event popularity at all stages [4; 5; 6], which is of great significance to the supervision of network opinion. At present, existing work can be divided into the following two aspects: event propagation research and event trend prediction research. In order to reveal the propagation mechanism and the evolution law of microblog events, the pioneering work [7] carried out feature extraction on Weibo data and developed an outlier knowledge management framework for dealing with public events. Compared with traditional methods, the graph theory-based approach performed well in modeling the interaction between users [8]. Meanwhile, it is worth mentioning that text and sentiment analysis are often used to analyze netizens' attitudes when they disseminate information. For example, the LSTM model [9] was exploited to capture the features of social contexts and can integrate them into text features. Additionally, [PERSON] et al. [10] proposed to use convolutional neural network (CNN) combined with word2 vec technology to establish emotion classification model. Moreover, among all text analysis methods, LDA model was widely considered to discovery of microblog text topics [11; 12]. Regarding the studies on events evolution trend prediction, the previous work is mainly divided into dynamic model and machine learning model. In [PERSON]'s work [13], a modified epidemic model was proposed to predict the dynamics of topic reading, which represents one indicator of event popularity. In their further work [14], considering both public contact and participation on microblog, they proposed the Susceptible-Reading-Forwarding-Immune (SRFI) model to predict the overall microblog event trend in all stages. Meanwhile, [PERSON] et al. [15] developed a Stochastic Differential Equation (SDE) to describe the observed collective patterns of the online microblogging behavior and predict the Sina Weibo volume data. On the other side, for machine learning-based method, the BP neural network [16; 17] was applied to predict the trend of microblog events in early. Aimed at the sudden and non-linear characteristics of microblog events, timely grasp of the information increment of microblog events plays a key role in measuring the event evolution trend, which can be better solved by using LSTM network model [18]. [PERSON] et al. [19] introduced LSTM model to analyze the sequence information and complete the prediction for the number of blogs for a certain event in a period. Moreover, in [PERSON]'s work [20], the bidirectional LSTM network model is proven to be not only capable of learning long-term dependencies between the time steps of sequence data, but also can effectively use past and future information for prediction. Consequently, thanks to the ability to process time series data, the Bi-LSTM model has been successfully applied in many time series prediction tasks [21; 22; 23; 24; 25; 26; 27]. Inspired by this idea, this paper aims to exploit the learning method based on Bi-LSTM model to deal with event trend prediction with a sequence to label model. Generally, the popularity of microblog events is affected by many factors. However, to our knowledge, so far there are very few works towards microblog events prediction considering multidimensional influential factors to model popularity index. Meanwhile, the dynamic change of microblog event popularity is easy to be nonlinear and irregular in a period of time; nevertheless, most of the previous works validate their methods and experiment only on one dataset, which may limit the applicability of the model especially for nonlinear time series data. This paper presents effective solutions to deal with trend prediction of microblog events' popularity. More specifically, by selecting the influence factors and quantifying the weight of each factor with information entropy algorithm, the microblog event popularity is modeled.