Ups and Downs in Buzzes: Life Cycle Modeling for Temporal Pattern Discovery

@article{Chang2014UpsAD,
  title={Ups and Downs in Buzzes: Life Cycle Modeling for Temporal Pattern Discovery},
  author={Yi Chang and Makoto Yamada and Antonio Ortega and Yan Liu},
  journal={2014 IEEE International Conference on Data Mining},
  year={2014},
  pages={749-754}
}
In social media analysis, one critical task is detecting burst of topics or buzz, which is reflected by extremely frequent mentions of certain key words 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 usually exhibits sudden spikes and heavy tails, which fails most existing time-series… CONTINUE READING