Popularity Prediction Method Based on the Multi-Factor Coupling of the Online Social Network Event Base

With the rapid development of the new generation of information network technology in recent years, people become aware of all kinds of social events rapidly and extensively through the social network platform, which speeds up and expands the diffusion of events in the social network. Because of this situation, to manage the events in the social network and improve the governance level of network event information effectively, information diffusion in the social network needs to be analyzed. Popularity prediction is the focus of online social network event information diffusion analysis. Popularity prediction can provide profound insights into the occurrence, development, peak, and end of network events. Although popularity prediction has been widely investigated, the lack of instant and available indicator data for factors associated with event-related information and popularity and the difficulty in differentiating indicator data hinder the effective prediction of event popularity. For this reason, this study designs a popularity prediction method based on the multi-factor coupling of the online social network event base. First, a multi-factor indicator acquisition method based on the social network event database, which uses the event database to uniformly store social network data and extract each factor indicator from multi-source heterogeneous data, is proposed. Second, a multi-factor coupling method for popularity prediction is proposed, with the low-dimensional representation of factor indicators that can be combined is obtained by grouping and embedding to realize the comprehensive utilization of multi-factor indicators during prediction. Finally, the tweets contained in 3 000 subject tags in the Twitter 7 data set are utilized as the experimental subjects to calculate the average accuracy. The experimental results show that, compared with the existing deep neural network, support vector regression, and SH popularity prediction models, the prediction method proposed in this study has superior prediction accuracy. © 2020, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.