On Group Popularity Prediction in Event-Based Social Networks

Abstract

Although previous work has shown that member and structural features are important to the future popularity of groups in EBSN, it is not yet clear how different member roles and the interplay between them contribute to group popularity. In this paper, we study a real-world dataset from Meetup --- a popular EBSN platform --- and propose a deep neural network based method to predict the popularity of new Meetup groups. Our method uses group-level features specific to event-based social networks, such as time and location of events in a group, as well as the structural features internal to a group, such as the inferred member roles in a group and social substructures among members. Empirically, our approach reduces the RMSE of the popularity prediction (measured in RSVPs) of a group's future events by up to 12%, against the state-of-the-art baselines.