OpinionRank: Trustworthy Website Detection Using Three Valued Subjective Logic
Xiaofei Niu, Guangchi Liu, Qing Bo Yang · IEEE Transactions on Big Data · 2020
For a web search engine, it is critical to design a mechanism to promote trustworthy websites and eliminate spam ones in the searching results. In this paper, we propose the OpinionRank algorithm to compute the trustworthiness of a website and identify trustworthy ones with high trust values. OpinionRank is essentially a breadth-first-search based algorithm that starts from an existing set of trustworthy websites, also called seeds. Because seeds play a vital role in OpinionRank, we put forward a novel seed selection scheme, named HarMean PageRank algorithm. HarMean combines the results of two seed selection algorithms, i.e. High PageRank and Inverse PageRank, to rank websites based on their trustworthiness. After trustworthy seeds are chosen, OpinionRank iteratively computes the trustworthiness of every website, leveraging trust propagation and trust combination. Using the public dataset WEBSPAM-UK2006, we validate OpinionRank and HarMean PageRank, analyze the impact of seed selection, and evaluate the convergence speed of OpinionRank. Experimental results indicate that OpinionRank can detect more trustworthy websites with fewer seeds, when compared to three state-of-the-art solutions, TrustRank, GoodRank, and Enhanced OpinionWalk algorithms.