Topical TrustRank: Using Topicality to Combat Web Spam

Baoning Wu, Vinay Goel, and Brian D. Davison

Full Paper (10 pages)
Official ACM published version:
Author's version: PDF (250KB)

Web spam is behavior that attempts to deceive search engine ranking algorithms. TrustRank is a recent algorithm that can combat web spam. However, TrustRank is vulnerable in the sense that the seed set used by TrustRank may not be sufficiently representative to cover well the different topics on the Web. Also, for a given seed set, TrustRank has a bias towards larger communities. We propose the use of topical information to partition the seed set and calculate trust scores for each topic separately to address the above issues. A combination of these trust scores for a page is used to determine its ranking. Experimental results on two large datasets show that our Topical TrustRank has a better performance than TrustRank in demoting spam sites or pages. Compared to TrustRank, our best technique can decrease spam from the top ranked sites by as much as 43.1%.

In Proceedings of the 15th International World Wide Web Conference, pages 63-72, Edinburgh, Scotland, May 2006.

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Last modified: 7 July 2011
Brian D. Davison