Canonical Trends: Detecting Trend Setters in Web Data
Machine Learning
2012-07-03 v1 Social and Information Networks
Machine Learning
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
@article{arxiv.1206.6388,
title = {Canonical Trends: Detecting Trend Setters in Web Data},
author = {Felix Biessmann and Jens-Michalis Papaioannou and Mikio Braun and Andreas Harth},
journal= {arXiv preprint arXiv:1206.6388},
year = {2012}
}
Comments
Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)