A Taxonomy of Hyperlink Hiding Techniques

Hidden links are designed solely for search engines rather than visitors. To get high search engine rankings, link hiding techniques are usually used for the profitability of black industries, such as illicit game servers, false medical services, illegal gambling, and less attractive high-profit industry, etc. This paper investigates hyperlink hiding techniques on the Web, and gives a detailed taxonomy. We believe the taxonomy can help develop appropriate countermeasures. Study on 5,583,451 Chinese sites' home pages indicate that link hidden techniques are very prevalent on the Web. We also tried to explore the attitude of Google towards link hiding spam by analyzing the PageRank values of relative links. The results show that more should be done to punish the hidden link spam.Comment: 12 pages, 2 figure

Fuzzy equivalence relation based clustering and its use to restructuring websites' hyperlinks and web pages

Quality design of websites implies that among other factors, hypelinks’ structure should allow the users to reach the information they seek with the minimum number of clicks. This paper utilises the fuzzy equivalence relation based clustering in adapting website hyperlinks’ structure so that the redesigned website allows users to meet as effectively as possible their informational and navigational requirements. The fuzzy tolerance relation is calculated based on the usage rate of hyperlinks in a website. The equivalence relation identifies clusters of hyperlinks. The clusters are then used to realocate hyperlinks in webpages and to rearrange webpages into the website structure hierarchy

Examining the Impact of a Reasoning Aid to Help People Evaluate the Evidentiary Weight of Consensus

This item is only available electronically.Social media is a vortex of information and people may see distorted views of consensus, where the independence of information and sources is unclear. A tool that summarises consensus information might help people to navigate these important cues. This study examined whether a reasoning aid (in the form of a diagram) visually illustrating both the number of independent people supporting/disagreeing with a claim and the diversity of arguments would persuade people to change their original beliefs. Participants (n=605) were recruited through Amazon’s Mechanical Turk to evaluate 24 claims on a mock Twitter interface. Participants were randomly assigned to conditions with either tweets only, diagram only or tweets with a diagram. Participants rated their initial agreement level (0-100) with each claim and then saw the diagram and/or set of tweets, then were able to update their agreement level if their original opinion had now changed. The findings of this study show that without assistance, people mostly rely on cues of argument quantity, such as the number of tweets for a given stance. However, when presented with a diagram, people were able to utilise cues of argument quality, such as when there were different sources providing the information and when multiple arguments were used.Thesis (B.PsychSc(Hons)) -- University of Adelaide, School of Psychology, 202

An Analysis Of Machine Learning Methods For Spam Host Detection

The web is becoming an increasingly important source of entertainment, communication, research, news and trade. In this way, the web sites compete to attract the attention of users and many of them achieve visibility through malicious strategies that try to circumvent the search engines. Such sites are known as web spam and they are generally responsible for personal injury and economic losses. Given this scenario, this paper presents a comprehensive performance evaluation of several established machine learning techniques used to automatically detect and filter hosts that disseminate web spam. Our experiments were diligently designed to ensure statistically sounds results and they indicate that bagging of decision trees, multilayer perceptron neural networks, random forest and adaptive boosting of decision trees are promising in the task of web spam classification and, hence, they can be used as a good baseline for further comparison. © 2012 IEEE.2227232Ledford, J.L., (2009) Search Engine Optimization Bible, , 2nd ed. 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