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The quantity and quality of seed sets are key factors for the success of propagation-based anti-web spam techniques. This kind of approach is simple yet effective, but the manual evaluation of seed sets is time-consuming. Therefore, a manual evaluation process is vital. In this article, we propose Trust Propagation Rank (TPRank), which automatically propagates trust to demote web spam based on a small number of reputable and spam seeds. Moreover, the proposed algorithm is extended to trust propagation (TP) spam mass in detection of web spam. Experiments were performed on two public available data sets—WEBSPAM-UK2006 and WEBSPAM-UK2007—and the results showed that both TPRank and TP spam mass outperform the state-of-the-art TrustRank in demotion up to 10.623% and spam mass algorithm in detection up to 43.216%.
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Keywords: TrustRank; adversarial information retrieval; spam mass; trust propagation; web spam filtering algorithms

Document Type: Research Article

Affiliations: 1: Department of Electrical and Computer Engineering, Curtin University, Sarawak Campus, Miri, Malaysia 2: National Institute of Technology, Kurukshetra, India

Publication date: May 19, 2014

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