Lifecycle Modeling for Buzz Temporal Pattern Discovery

Abstract

In social media analysis, one critical task is detecting a burst of topics or buzz , which is reflected by extremely frequent mentions of certain keywords in a short-time interval. Detecting buzz not only provides useful insights into the information propagation mechanism, but also plays an essential role in preventing malicious rumors. However, buzz modeling is a challenging task because a buzz time-series often exhibits sudden spikes and heavy tails, wherein most existing time-series models fail. In this article, we propose novel buzz modeling approaches that capture the rise and fade temporal patterns via Product Lifecycle (PLC) model, a classical concept in economics. More specifically, we propose to model multiple peaks in buzz time-series with PLC mixture or PLC group mixture and develop a probabilistic graphical model (K-Mixture of Product Lifecycle ( K-MPLC ) to automatically discover inherent lifecycle patterns within a collection of buzzes. Furthermore, we effectively utilize the model parameters of PLC mixture or PLC group mixture for burst prediction. Our experimental results show that our proposed methods significantly outperform existing leading approaches on buzz clustering and buzz-type prediction.