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Paper detail

Canonical Trends: Detecting Trend Setters in Web Data

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.

preprint2012arXivOpen access
Felix BiessmannJens-Michalis PapaioannouMikio BraunAndreas Harth
Social and Information NetworksMachine Learning
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Felix BiessmannJens-Michalis PapaioannouMikio BraunAndreas Harth

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