A Predictive Paradigm for Event Popularity in Event-Based Social Networks.

Recently, event-based social networks (EBSNs) have been used as flexible online platforms that create online groups and make offline events for people. The success of popular offline events depends much on a participant number factor, which contributes to the growth of online groups and social networks. In this paper, we study a research problem of event popularity, where the popularity of an event is relevant to the number of participants of the event. In this work, we propose a predictive paradigm which consists of the procedure of generating features and training regression methods to estimate the popularity of events. We first crawled datasets and then generated features from the datasets. Finally, three famous regression methods, i.e., support vector machine, random forest, and decision tree, were used to predict the popularity of events. Extensive experiments were conducted on three city datasets with two different contexts of using these three datasets. In the city context, each city dataset was converted into a data table. Three regression methods used the data table to build predictive models and estimate the popularity of events. In the other context, each group in one city dataset was transformed into one group data table, and regression models were built on the group data table. Overall, the proposed paradigm with random forest is the best in terms of MAE and RMSE metrics. Moreover, this study has shown that for the city context, the event content is the best contributing factor that pushes people to engage in events. Furthermore, with the group context, the event time factor is very crucial to assist users in planning to join events.