Group Popularity Prediction in EBSN based on an Improved Self-Excited Hawkes Process

Abstract

Event-based Social Network (EBSN) is a special online social network that not only provides users with a convenient virtual platform to communicate, but also helps them to participate in offline face-to-face social activities on a group basis. This paper studies the group popularity prediction problem in EBSN which is important for online advertising and event recommendation. Firstly, we construct the EBSN temporal network model with a timeline involved and propose a novel definition of group popularity in EBSN. Then on the basis of the above, we extract corresponding key features from four aspects: inherent group characteristics (i.e., founder, created time), historical popularity, the sentiment of users towards events as an internal factor, and the initiative of newly added users as an external factor. Combining these four features, we propose a group popularity prediction algorithm in EBSN based on an improved self-excited Hawkes Process: EBSN-Hawkes Process Algorithm (EHP). Experiments conducted by real EBSN datasets demonstrate that the proposed EHP algorithm has better prediction results than other comparative algorithms. We also conclude that the external dynamic feature plays a more important role than the internal sentiment feature by the ablation study.