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Paper Title:
Improving Web Spam Detection with Re-Extracted Features

Authors:

  • Guang-Gang Geng(Chinese Academy of Sciences)
  • Chun-Heng Wang(Chinese Academy of Sciences)
  • Qiu-Dan Li(Chinese Academy of Sciences)

Abstract:
Web spam detection has become one of the top challenges for the Internet search industry. Instead of using some heuristic rules, we propose a feature re-extraction strategy to optimize the detection result. Based on the predicted spamicity obtained by the preliminary detection, through the host level web graph, three types of features are extracted. Experiments on WEBSPAM-UK2006 benchmark show that with this strategy, the performance of web spam detection can be improved evidently.

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