BEEP: A Bayesian Perspective Early Stage Event Prediction Model for Online Social Networks

Abstract

In recent years, predicting future hot events in online social networks is becoming increasingly meaningful in marketing, advertisement, and recommendation systems to support companies' strategy making. Currently, most prediction models require long-term observations over the event or depend a lot on other features which are expensive to extract. However, at the early stage of an event, the temporal features of hot events and non-hot events are not distinctive yet. Besides, given the small amount of available data, high noise and complex network structure, those state-of-art models are unable to give an accurate prediction at the very early stage of an event. Hence, we propose two Bayesian perspective models to handle this dilemma. We first mathematically define the hot event prediction problem and introduce the general early stage event prediction framework, then model the five selected features into several continuous distributions, and present two Semi-Naive Bayes Classifier based prediction models, BEEP and SimBEEP, which is the simplified version of BEEP. Extensive experiments on real dataset have demonstrated that our model significantly outperforms the baseline methods.