Canonical Trends: Detecting Trend Setters in Web Data

Autor:	Biessmann, Felix, Papaioannou, Jens-Michalis, Braun, Mikio, Harth, Andreas
Rok vydání:	2012
Předmět:	Computer Science - Learning Computer Science - Social and Information Networks Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1206.6388 Zobrazit plný text záznamu View this record from Arxiv