Predicting Hot Events in the Early Period through Bayesian Model for Social Networks

Zuowu Zheng, Xiaofeng Gao, Xiao Ma, Guihai Chen

Abstract: Predicting emerging hot events in an early stage is essential for various applications, including information dissemination mining, ads recommendation and etc. Existing techniques either require a long-term observation over the event or features that are expensive to extract. However, given limited data at the early stage of an emerging event, the temporal features of hot events and non-hot events are not distinctive enough yet. In this work, we introduce BEEP, a Bayesian perspective Early stage Event Prediction model, that tackles this dilemma. We formulate the hot event prediction problem by two Semi-Naive Bayes Classifiers, where we consider both the temporal features and structural features and perform distribution test for the selected features. Theoretical analysis and extensive empirical evaluations on two real datasets demonstrate the effectiveness of our methods.