# Enhancing Short-Term Sales Prediction with Microblogs: A Case Study of the Movie Box Office
## 2 Related Work  
### Microblog Influence Analysis  
The social network is the primary platform for communication between people in the Internet era, including e-mail, BBS, and microblogs. Influence is reflected in the interaction and communication between social network users on the social network platform by publishing news, updating statuses, instant comments, paying attention to others, and other behaviors. Users play an essential role in social network relationships. Various user behaviors will directly affect the dissemination of information. Therefore, user influence is the main object of influence research. Driven by the word-of-mouth effect in the market, researchers have investigated an essential issue in social network analysis, that is, \"which customers should be selected to start marketing activities and make the effect of marketing activities cover the largest community\". To study the influence maximization problem, we need to find an appropriate influence propagation model. The basic influence propagation process refers to the spreading process of the active nodes in social networks. The active nodes will affect other nodes following a certain propagation mechanism. The commonly used influence propagation models [7] are similar to graph traversing algorithms. The main difference between the models lies in the probability and the mode of influence propagation between nodes. For example, in the linear threshold model [7], the possibility of a node to be traversed depends on whether all active neighbor nodes' probabilities are more significant than a specified threshold. Based on a given influence propagation model, the influence maximization algorithm mainly focuses on heuristic and greedy algorithms. A heuristic algorithm generally selects an initial node for influence propagation based on the node degree and the network centrality. However, it does not consider the diffusion process of influence and cannot guarantee the optimal influence range. [PERSON] et al. [7] proposed a greedy algorithm based on mountain climbing, which starts from an empty initial active-node set and selects the most influential node to join the active-node set through influence evaluation at each step until the target number is reached. The disadvantage of this algorithm is that it has high time complexity in the case of large data scales. In the literature [8], researchers selected the potentially influential nodes in the inspiration stage and the most influential nodes in the greedy stage. The two-step improved algorithm effectively reduced the running time and improved the algorithm's efficiency. In the research of influence maximization algorithms, the evaluation of user influence is a vital link. The assessment of user influence in social networks is based on network topology and user interaction behavior, mainly the evaluation of user communication ability. The stronger the communication ability of users, the greater the breadth and depth of communication scope and influence. The number of fans usually reflects the influence of microblog users. If a user has more fans, the microblogs published by him or her will be read by more users, meaning that the user has a greater influence on the microblogging platform. In addition to the number of fans, researchers also introduced other features to measure the influence of users, e.g., the number of comments or forwards. [PERSON] et al. [9] listed various characteristics of users' behavior and studied the impact of these characteristics on the influence of microblogs as well as users. They demonstrated that the number of microblogs and the number of forwards were significant indicators affecting the influence of users. Some other researchers introduced the PageRank algorithm to measure the importance of web pages [10]. So far, most research on the influence of microblogs is aimed at the influence measurement of microblog users, and few were toward the microblog contents.