Spam-Resilient Web Rankings via Influence Throttling
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Web search is one of the most critical applications for managing the massive amount of distributed Web content. Due to the overwhelming reliance on Web search, there is a rise in efforts to manipulate (or spam) Web search engines. In this paper, we develop a spam-resilient ranking model that promotes a source-based view of the Web. One of the most salient features of our spam-resilient ranking algorithm is the concept of influence throttling. We show how to utilize influence throttling to counter Web spam that aims at manipulating link-based ranking systems, especially PageRank-like systems. Through formal analysis and experimental evaluation, we show the effectiveness and robustness of our spam-resilient ranking model in comparison with existing Web algorithms such as PageRank. © 2007 IEEE.
author list (cited authors)
Caverlee, J., Webb, S., & Liu, L.