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Detecting Link Spam from Temporal Statistics of Websites

Guoyang Shen, Bin Gao, Tie-Yan Liu, Guang Feng, Shiji Song, and Hang Li

Abstract

How to effectively protect against spam on search ranking results is an important issue for contemporary web search engines. This paper addresses the problem of combating one major type of web spam: 'link spam.' Most of the previous work on anti link spam managed to make use of one snapshot of web data to detect spam, and thus it did not take advantage of the fact that link spam tends to result in drastic changes of links in a short time period. To overcome the shortcoming, this paper proposes using temporal information on links in detection of link spam, as well as other information. Specifically, it defines temporal features such as In-link Growth Rate (IGR) and In-link Death Rate (IDR) in a spam classification model (i.e., SVM). Experimental results on web domain graph data show that link spam can be successfully detected with the proposed method.

Details

Publication typeArticle
Published inProceedings of the Sixth International Conference on Data Mining
PublisherIEEE Computer Society
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