EPAB: Early Pattern Aware Bayesian Model for Social Content Popularity Prediction

Abstract

The boom of information technology enables social platforms (like Twitter) to disseminate social content (like news) in an unprecedented rate, which makes early-stage prediction for social content popularity of great practical significance. However, most existing studies assume a long-term observation before prediction and suffer from limited precision for early-stage prediction due to insufficient observation. In this paper, we take a fresh perspective, and propose a novel early pattern aware Bayesian model. The early pattern representation, which stands for early time series normalized on future popularity, can address what we call early-stage indistinctiveness challenge. Then we use an expressive evolving function to fit the time series and estimate three interpretable coefficients characterizing temporal effect of observed series on future evolution. Furthermore, Bayesian network is leveraged to model the probabilistic relations among features, early indicators and early patterns. Experiments on three real-world social platforms (Twitter, Weibo and WeChat) show that under different evaluation metrics, our model outperforms other methods in early-stage prediction and possesses low sensitivity to observation time.