Life Cycle Modeling of News Events Using Aging Theory
Abstract
In this paper, an adaptive news event detection method is proposed. We consider a news event as a life form and propose an aging theory to model its life span. A news event becomes popular with a burst of news reports, and it fades away with time. We incorporate the proposed aging theory into the traditional single-pass clustering algorithm to model life spans of news events. Experiment results show that the proposed method has fairly good performance for both long-running and short-term events compared to other approaches.
Cite
Text
Chen et al. "Life Cycle Modeling of News Events Using Aging Theory." European Conference on Machine Learning, 2003. doi:10.1007/978-3-540-39857-8_7Markdown
[Chen et al. "Life Cycle Modeling of News Events Using Aging Theory." European Conference on Machine Learning, 2003.](https://mlanthology.org/ecmlpkdd/2003/chen2003ecml-life/) doi:10.1007/978-3-540-39857-8_7BibTeX
@inproceedings{chen2003ecml-life,
title = {{Life Cycle Modeling of News Events Using Aging Theory}},
author = {Chen, Chien Chin and Chen, Yao-Tsung and Sun, Yeali S. and Chen, Meng Chang},
booktitle = {European Conference on Machine Learning},
year = {2003},
pages = {47-59},
doi = {10.1007/978-3-540-39857-8_7},
url = {https://mlanthology.org/ecmlpkdd/2003/chen2003ecml-life/}
}