Lifecycle Modeling for Buzz Temporal Pattern Discovery

Yi Chang, Makoto Yamada, Antonio Ortega, Yan Liu
2016 ACM Transactions on Knowledge Discovery from Data  
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, where most existing time-series models
more » ... In this paper, we propose novel buzz modeling approaches which 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-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.
doi:10.1145/2994605 fatcat:7qbytdz2xrgxxb5ayndu4blurq