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Information / Email / Spam filtering / Collaboration / Social information processing / Anti-spam techniques / Spam / Social bookmarking / Tag / Internet / Spamming / Computing


Using Semantic Features to Detect Spamming in Social Bookmarking Systems Amgad Madkour1 , Tarek Hefni2 , Ahmed Hefny1 , Khaled S. Refaat1 Human Language Technologies Group IBM Cairo Technology Development Center1 P.O.Box
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Document Date: 2008-08-25 03:47:28

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Edmonton / Cambridge / Beijing / Barcelona / frequent term / London / Mountain View / New York / Giza / /

Company

IBM / Social Bookmarking Systems / Neural Networks / MIT Press / Analyzing Social Bookmarking Systems / Ahmed Hefny1 / /

Country

United States / Canada / United Kingdom / China / Spain / Egypt / /

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Facility

American University / /

IndustryTerm

web-based searching / similar systems / user online bookmarks / association rule mining / domain mail server / on-line learning / collaborative social software systems / /

Organization

American University in Cairo / AUC RBF / HFTC / MIT / IMC / Federal Trade Commission / be evaluated using AUC / /

Person

Gomes / Yoav Freund / G. Potamias / V / Carl Edward Rasmussen / Robert E. Schapire / Chris Williams / /

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Alberta / /

PublishedMedium

Machine Learning / /

Technology

Neural Network / machine learning / two algorithms / /

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http /

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