# Enhancing Short-Term Sales Prediction with Microblogs: A Case Study of the Movie Box Office
## 2 Related Work
### Social Network-Based Information Prediction  
The existing research on information prediction based on a social network involves many aspects. Researchers have carried out prediction research from different angles. Some researchers focus on the prediction of the attributes of social networks. For example, [PERSON] et al. [12] predict the life cycle of microblogs based on the information characteristics of publishers and the forwarding characteristics in the first hour of publishing. Other researchers use network data to predict the sales volume of products. For example, [PERSON] et al. [13] used the Google trend index to predict the sales of cars in the short-term future. As a result, they obtained an earlier and more accurate sales forecast than the official data.  
In recent years, network users' emotions for information prediction have attracted people's attention and have gradually become an essential direction of sentiment analysis and research. Sentiment is a subjective factor in different social activities. In some areas, sentiment analysis can be predicted independently. However, it can only be an additional factor in some applications. Based on the actual work needs, the sentiment analysis methods used by different researchers are also different. At present, the prediction research based on the social network has made beneficial attempts and explorations in many fields such as political elections, film box office, and stock price [14]. Researchers are committed to studying the relationship between blog, search volume, microblog and book sales, film box office, stock closing price, and other real-world fields. [PERSON] et al. [15] proposed using sentiment analysis to obtain the state changes of book reviews and predict book sales. [PERSON] et al. [16] combined the quantity information, such as microblog forwarding and microblog content information, and used machine learning methods such as neural networks to predict the film box office, which obtained more accurate results than the traditional linear prediction. [PERSON] et al. [17] proposed to find the potential emotional factors in blogs and established an autoregressive emotional perception model to predict commodity sales.  
However, some other researchers claimed that compared with traditional methods, the information on social platforms did not apply to some scenarios. For example, [PERSON] et al. [18] tried to use Twitter to predict the election in Singapore and found that Twitter's prediction has poor performance.  
Microblogging platforms have been one of the major social networks in the Web 2.0 era. The increasing amount of microblog information provides a new idea for information prediction from microblogs. Some recent studies have revealed that the sentiment of microblog messages can highly impact information diffusion and its spread on microblogging platforms [19; 20; 21]. Additionally, there are many studies aiming to predict different information from Twitter or other microblogging media, such as stock return prediction [21], election prediction [22], crime prediction [23], and Bitcoin price prediction [24]. Some researchers were concerned about the sales prediction problem on microblogging platforms [25]. However, they relied on other information sources rather than microblogs, which was different from this study. Instead, we only needed microblog data. In addition, we focused on short-term product sales prediction rather than long-term prediction.