Link-based spam algorithms in adversarial information retrieval

Web spam has become one of the most exciting challenges and threats to web search engines. The relationship between the search systems and those who try to manipulate them came up with the field of adversarial information retrieval. In this article, we set up several experiments to compare HostRank and TrustRank to show how effective it is for TrustRank to combat web spam, and we report a comparison on different link-based web spam detection algorithms

Methods for demoting and detecting Web spam

Web spamming has tremendously subverted the ranking mechanism of information retrieval in Web search engines. It manipulates data source maliciously either by contents or links with the intention of contributing negative impacts to Web search results. The altering order of the search results by spammers has increased the difficulty level of searching and time consumption for Web users to retrieve relevant information. In order to improve the quality of Web search engines results, the design of anti-Web spam techniques are developed in this thesis to detect and demote Web spam via trust and distrust and Web spam classification.A comprehensive literature on existing anti-Web spam techniques emphasizing on trust and distrust model and machine learning model is presented. Furthermore, several experiments are conducted to show the vulnerability of ranking algorithm towards Web spam. Two public available Web spam datasets are used for the experiments throughout the thesis - WEBSPAM-UK2006 and WEBSPAM-UK2007.Two link-based trust and distrust model algorithms are presented subsequently: Trust Propagation Rank and Trust Propagation Spam Mass. Both algorithms semi automatically detect and demote Web spam based on limited human experts’ evaluation of non-spam and spam pages. In the experiments, the results for Trust Propagation Rank and Trust Propagation Spam Mass have achieved up to 10.88% and 43.94% improvement over the benchmark algorithms.Thereafter, the weight properties which associated as the linkage between two Web hosts are introduced into the task of Web spam detection. In most studies, the weight properties are involved in ranking mechanism; in this research work, the weight properties are incorporated into distrust based algorithms to detect more spam. The experiments have shown that the weight properties enhanced existing distrust based Web spam detection algorithms for up to 30.26% and 31.30% on both aforementioned datasets.Even though the integration of weight properties has shown significant results in detecting Web spam, the discussion on distrust seed set propagation algorithm is presented to further enhance the Web spam detection experience. Distrust seed set propagation algorithm propagates the distrust score in a wider range to estimate the probability of other unevaluated Web pages for being spam. The experimental results have shown that the algorithm improved the distrust based Web spam detection algorithms up to 19.47% and 25.17% on both datasets.An alternative machine learning classifier - multilayered perceptron neural network is proposed in the thesis to further improve the detection rate of Web spam. In the experiments, the detection rate of Web spam using multilayered perceptron neural network has increased up to 14.02% and 3.53% over the conventional classifier – support vector machines. At the same time, a mechanism to determine the number of hidden neurons for multilayered perceptron neural network is presented in this thesis to simplify the designing process of network structure

Link analysis algorithms to handle hanging and spam pages

Hanging pages are one of the hidden problems in link structure ranking algorithms and they can be compromised by spammers to induce Link Spam. In this thesis, efficient algorithms have been developed to produce fair and relevant results from Web search engines, by handling hanging pages and detecting Link spam caused by them. The effect of hanging pages in Website Optimisation is also investigated and methodologies were proposed to develop optimised websites