Predicting the Popularity of Online Content by Modeling the Social Influence and Homophily Features

Abstract

Predicting the popularity of online content on social network can bring considerable economic benefits to companies and marketers, and it has wide application in viral marketing, content recommendation, early warning of social unrest, etc. The diffusion process of online contents is often a complex combination of both social influence and homophily; however, existing works either only consider the social influence or homophily of early infected users and fail to model the joint effect of social influence and homophily when predicting future popularity. In this study, we aim to develop a framework to unify the social influence and homophily in popularity prediction. We use an unsupervised graph neural network framework to model nondirectional social homophily and integrate the attention mechanism with the graph neural network framework to learn the directional and heterogeneous social relationship for generating social influence representation. On the other hand, existing research studies often overlook the social group characteristics of early infected users, and we try to divide users into different social groups based on user interest and learn the social group representation from clusters. We integrate the social influence, homophily, and social group representation of early infected users to make popularity predictions. Experiments on real datasets show that the proposed method significantly improves the prediction accuracy compared with the latest methods, which confirms the importance of joint model social influence and homophily and shows that social group characteristic is an important predictor in the popularity prediction task.