A Survey on Trust and Distrust Propagation for Web Pages

Search engines are the hub for information retrieval from the web. But due to the web spam, we may not get the desired information from the search engines. The phrase web spam is used for the web pages that are designed to spam the web search results by using some unacceptable tactics. Web spam pages use different techniques to achieve undeserved ranking in the web. Over the last decades researchers are trying to design different techniques to identify the web spam pages so that it does not deteriorate the quality of the search results. In this paper we present a survey on different web spam techniques with underlying principles and algorithms. We have surveyed all the major spam detection techniques and provided a brief discussion on the pros and cons of all the existing techniques. Finally, we summarized the various observations and underlying principles that are applied for spam detection techniques.Keywords:TrustRank, Anti-TrustRank, Good-Bad Rank, Spam Detection, Demotio

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

A Dynamic Trust Relations-Based Friend Recommendation Algorithm in Social Network Systems

A discovered algorithm based on the dynamic trust relations of users in a social network system (SNS) was proposed aiming at getting useful information more efficiently in an SNS. The proposed dynamic model combined the interests and trust relations of users to explore their good friends for recommendations. First, the network based on the interests and trust relations of users was set up. Second, the temporal factor was added to the model, then a dynamic model of the degree of the interest and trust relations of the users was calculated. Lastly, the similarities among the users were measured via this dynamic model, and the recommendation list of good friends was achieved. Results showed that the proposed algorithm effectively described the changes in the interest similarities and trust relations of users with time, and the recommended result was more accurate and effective than the traditional ones

Vertrauensbasierte Empfehlungen in mehrschichtigen Netzwerken

The huge interest in social networking applications - Friendster.com, for example, has more than 40 million users - led to a considerable research interest in using this data for generating recommendations. Especially recommendation techniques that analyze trust networks were found to provide very accurate and highly personalized results. The main contribution of this thesis is to extend the approach to trust-based recommendations, which up to now have been made for unlinked items such as products or movies, to linked resources, in particular documents. Therefore, a second type of network, namely a document reference network, is considered apart from the trust network. This is, for example, the citation network of scientific publications or the hyperlink graph of webpages. Recommendations for documents are typically made by reference-based visibility measures which consider a document to be the more important, the more often it is referenced by important documents. Document and trust networks, as well as further networks such as organization networks are integrated in a multi-layer network. This architecture makes it possible to combine classical measures for the visibility of a document with trust-based recommendations, giving trust-enhanced visibility measures. Moreover, an approximation approach is introduced which considers the uncertainty induced by duplicate documents. These measures are evaluated in simulation studies. The trust-based recommender system for scientific publications SPRec implements a two-layer architecture and provides personalized recommendations via a Web interface.Soziale Netzwerke mit ihren Millionen von Nutzern haben zu einem großen Interesse an der Fragestellung geführt, wie die Informationen aus solchen sozialen Netzwerken in Empfehlungssystemen genutzt werden können. Aktuelle Forschungsarbeiten haben gezeigt, dass vor allem Techniken, die soziale Vertrauensnetzwerke zur Grundlage nehmen, sehr gute Ergebnisse liefern. Die vorliegende Dissertation erweitert Ansätze zu vertrauensbasierten Empfehlungen, die bisher nur isolierte Objekte wie beispielsweise Produkte oder Filme berücksichtigt haben, zu Ansätzen für vernetzte Ressourcen, insbesondere Dokumente. Daher wird neben dem Vertrauensnetzwerk eine zweite Art von Netzwerk betrachtet, ein Dokumentennetzwerk. Beispiele für Dokumentennetzwerke sind Zitationsnetzwerke wissenschaftlicher Publikationen oder der Hyperlink-Graph zwischen Webseiten. Dokumentenempfehlungen werden typischerweise mit referenzbasierten Sichtbarkeitsmaßen berechnet, die ein Dokument als wichtig erachten, wenn es von vielen wichtigen Dokumenten referenziert wird. Vertrauensnetzwerke und Dokumentennetzwerke werden in einer zweischichtigen Architektur integriert. Weitere Netzwerke, wie zum Beispiel Organisationsnetzwerke bauen sie zu einer mehrschichtigen Architektur aus. In dieser Architektur können klassische Maße für Dokumentensichtbarkeit mit vertrauensbasierten Empfehlungen kombiniert werden, nämlich in den sogenannten vertrauensbasierten Sichtbarkeitsmaßen. Darüberhinaus führt die Dissertation einen Ansatz ein, um die vertrauensbasierte Sichtbarkeit dann approximieren zu können, wenn das Dokumentennetzwerk Duplikate von Dokumenten enthält. Die entwickelten Sichtbarkeitsmaße werden in einer Simulationsstudie analysiert. Das webbasierte Empfehlungssystem für wissenschaftliche Veröffentlichungen SPRec implementiert die vertrauensbasierten Sichtbarkeitsmaße und generiert personalisierte Empfehlungen

Addressing Automated Adversaries of Network Applications

The Internet supports a perpetually evolving patchwork of network services and applications. Popular applications include the World Wide Web, online commerce, online banking, email, instant messaging, multimedia streaming, and online video games. Practically all networked applications have a common objective: to directly or indirectly process requests generated by humans. Some users employ automation to establish an unfair advantage over non-automated users. The perceived and substantive damages that automated, adversarial users inflict on an application degrade its enjoyment and usability by legitimate users, and result in reputation and revenue loss for the application\u27s service provider. This dissertation examines three challenges critical to addressing the undesirable automation of networked applications. The first challenge explores individual methods that detect various automated behaviors. Detection methods range from observing unusual network-level request traffic to sensing anomalous client operation at the application-level. Since many detection methods are not individually conclusive, the second challenge investigates how to combine detection methods to accurately identify automated adversaries. The third challenge considers how to leverage the available knowledge to disincentivize adversary automation by nullifying their advantage over legitimate users. The thesis of this dissertation is that: there exist methods to detect automated behaviors with which an application\u27s service provider can identify and then systematically disincentivize automated adversaries. This dissertation evaluates this thesis using research performed on two network applications that have different access to the client software: Web-based services and multiplayer online games

Network Propaganda

"Is social media destroying democracy? Are Russian propaganda or ""Fake news"" entrepreneurs on Facebook undermining our sense of a shared reality? A conventional wisdom has emerged since the election of Donald Trump in 2016 that new technologies and their manipulation by foreign actors played a decisive role in his victory and are responsible for the sense of a ""post-truth"" moment in which disinformation and propaganda thrives. Network Propaganda challenges that received wisdom through the most comprehensive study yet published on media coverage of American presidential politics from the start of the election cycle in April 2015 to the one year anniversary of the Trump presidency. Analysing millions of news stories together with Twitter and Facebook shares, broadcast television and YouTube, the book provides a comprehensive overview of the architecture of contemporary American political communications. Through data analysis and detailed qualitative case studies of coverage of immigration, Clinton scandals, and the Trump Russia investigation, the book finds that the right-wing media ecosystem operates fundamentally differently than the rest of the media environment. The authors argue that longstanding institutional, political, and cultural patterns in American politics interacted with technological change since the 1970s to create a propaganda feedback loop in American conservative media. This dynamic has marginalized centre-right media and politicians, radicalized the right wing ecosystem, and rendered it susceptible to propaganda efforts, foreign and domestic. For readers outside the United States, the book offers a new perspective and methods for diagnosing the sources of, and potential solutions for, the perceived global crisis of democratic politics.

Network Propaganda

"Is social media destroying democracy? Are Russian propaganda or ""Fake news"" entrepreneurs on Facebook undermining our sense of a shared reality? A conventional wisdom has emerged since the election of Donald Trump in 2016 that new technologies and their manipulation by foreign actors played a decisive role in his victory and are responsible for the sense of a ""post-truth"" moment in which disinformation and propaganda thrives. Network Propaganda challenges that received wisdom through the most comprehensive study yet published on media coverage of American presidential politics from the start of the election cycle in April 2015 to the one year anniversary of the Trump presidency. Analysing millions of news stories together with Twitter and Facebook shares, broadcast television and YouTube, the book provides a comprehensive overview of the architecture of contemporary American political communications. Through data analysis and detailed qualitative case studies of coverage of immigration, Clinton scandals, and the Trump Russia investigation, the book finds that the right-wing media ecosystem operates fundamentally differently than the rest of the media environment. The authors argue that longstanding institutional, political, and cultural patterns in American politics interacted with technological change since the 1970s to create a propaganda feedback loop in American conservative media. This dynamic has marginalized centre-right media and politicians, radicalized the right wing ecosystem, and rendered it susceptible to propaganda efforts, foreign and domestic. For readers outside the United States, the book offers a new perspective and methods for diagnosing the sources of, and potential solutions for, the perceived global crisis of democratic politics.