Web spam detection via commercial intent analysis

  • Authors:

  • András Benczúr;István Bíró;Károly Csalogány;Tamás Sarlós

  • Affiliations:

  • Computer and Automation Research Institute of the Hungarian Academy of Sciences;Computer and Automation Research Institute of the Hungarian Academy of Sciences;Computer and Automation Research Institute of the Hungarian Academy of Sciences;Computer and Automation Research Institute of the Hungarian Academy of Sciences

  • Venue:
  • AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
  • Year:
  • 2007

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Abstract

We propose a number of features for Web spam filtering based on the occurrence of keywords that are either of high advertisement value or highly spammed. Our features include popular words from search engine query logs as well as high cost or volume words according to Google AdWords. We also demonstrate the spam filtering power of the Online Commercial Intention (OCI) value assigned to an URL in a Microsoft adCenter Labs Demonstration and the Yahoo! Mindset classification of Web pages as either commercial or non-commercial as well as metrics based on the occurrence of Google ads on the page. We run our tests on the WEBSPAM-UK2006 dataset recently compiled by Castillo et al. as a standard means of measuring the performance of Web spam detection algorithms. Our features improve the classification accuracy of the publicly available WEBSPAM-UK2006 features by 3%.