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arXiv · 1206.6388

Canonical Trends: Detecting Trend Setters in Web Data

Felix Biessmann·Jens-Michalis Papaioannou·Mikio Braun·Andreas Harth
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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.

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Felix Biessmann, Jens-Michalis Papaioannou, Mikio Braun, Andreas Harth. 2012-06-27. Canonical Trends: Detecting Trend Setters in Web Data. https://arxiv.org/abs/1206.6388

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