Learning Cost-Effective Social Embedding for Cascade Prediction

Abstract

Given a message, cascade prediction aims to predict the individuals who will potentially retweet it. Most existing methods either exploit demographical, structural, and temporal features for prediction, or explicitly rely on particular information diffusion models. Recently, researchers attempt to design fully data-driven methods for cascade prediction (i.e., without requiring human-defined features or information diffusion models), directly leveraging historical cascades to learn interpersonal proximity and then making prediction based on the learned proximity. One widely-used method to represent interpersonal proximity is social embedding, i.e., each individual is embedded into a low-dimensional latent metric space. One challenging problem is to design cost-effective method to learn social embedding from cascades. In this paper, we propose a position-aware asymmetric embedding method to effectively learn social embedding for cascade prediction. Different from existing methods where individuals are embedded into a single latent space, our method embeds each individual into two latent spaces: a latent influence space and a latent susceptibility space. Furthermore, our method employs the occurrence position of individuals in cascades to improve the learning efficiency of social embedding. We validate the proposed method on a dataset extracted from Sina Weibo. Experimental results demonstrate that the proposed model outperforms state-of-the-art social embedding methods at both learning efficiency and prediction accuracy.