| Skip to main content | Skip to navigation |

Register Now!

Topical TrustRank: Using Topicality to Combat Web Spam

  • Baoning Wu, Department of Computer Science & Engineering, Lehigh University, USA
  • Vinay Goel, Department of Computer Science & Engineering, Lehigh University, USA
  • Brian D. Davison, Department of Computer Science & Engineering, Lehigh University, USA

Full text:

Presentation Slides:

Track: Search

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%.


Wu, B., Goel, V., and Davison, B. D. 2006. Topical TrustRank: using topicality to combat web spam. In Proceedings of the 15th International Conference on World Wide Web (Edinburgh, Scotland, May 23 - 26, 2006). WWW '06. ACM Press, New York, NY, 63-72.
DOI= http://doi.acm.org/10.1145/1135777.1135792

Other items being presented by these speakers

Organised by

ECS Logo

in association with

BCS Logo ACM Logo

Platinum Sponsors

Sponsor of The CIO Dinner

Become a sponsor or exhibitor
Valid XHTML 1.0! IFIP logo WWW Conference Committee logo Web Consortium logo Valid CSS!