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:

Track: Search

Slot: 11:00-12:30, Wednesday 24th May

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 dierent 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%.

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