Canonical Trends: Detecting Trend Setters in Web Data
Abstract
Much information available on the web is copied, reused or rephrased. The phenomenon that multiple web sources pick up certain information is often called trend. A central problem in the context of web data mining is to detect those web sources that are first to publish information which will give rise to a trend. We present a simple and efficient method for finding trends dominating a pool of web sources and identifying those web sources that publish the information relevant to a trend before others. We validate our approach on real data collected from influential technology news feeds.
Cite
Text
Bießmann et al. "Canonical Trends: Detecting Trend Setters in Web Data." International Conference on Machine Learning, 2012.Markdown
[Bießmann et al. "Canonical Trends: Detecting Trend Setters in Web Data." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/biemann2012icml-canonical/)BibTeX
@inproceedings{biemann2012icml-canonical,
title = {{Canonical Trends: Detecting Trend Setters in Web Data}},
author = {Bießmann, Felix and Papaioannou, Jens-Michalis and Braun, Mikio L. and Harth, Andreas},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/biemann2012icml-canonical/}
}