Predicting Future Participants of Information Propagation Trees

Understanding how information propagates among social media users can allow researchers to provide interesting insights into online social networks and lead to applications such as precise advertising and misinformation management. In this work, we focus on information diffusion through post sharing. Given an information propagation tree, our goal is to predict a list of potential users of the tree. A framework based on graph convolutional network (GCN) is proposed to learn the latent representation of a propagation tree and match it with the latent representation of a user. A novel strategy for tree pruning is further investigated to improve the GCN. Experimental results show that our framework outperforms the existing methods for modeling information diffusion.CCS CONCEPTS• Information systems →Collaborative filtering; Social recommendation; Social networks; • Human-centered computing → Social content sharing; Social media; • Computing methodologies → Neural networks.