Web spam can significantly deteriorate the quality of search engine results. Thus there is a large incentive for commercial search engines to detect spam pages efficiently and accurately. In this paper we present a spam detection system that uses the topology of the Web graph by exploiting the link dependencies among the Web pages, and the content of the pages themselves. We find that linked hosts tend to belong to the same class: either both are spam or both are non-spam. We demonstrate three methods of incorporating the Web graph topology into the predictions obtained by our base classifier: (i) clustering the host graph, and assigning the label of all hosts in the cluster by majority vote, (ii) propagating the predicted labels to neighboring hosts, and (iii) using the predicted labels of neighboring hosts as new features and retraining the classifier. The result is an accurate system for detecting Web spam that can be applied in practice to large-scale Web data.

Spammers use questionable search engine optimization (SEO) techniques to promote their spam links into top search results. In this paper, we focus on one prevalent type of spam – redirection spam – where one can identify spam pages by the third-party domains that these pages redirect traffic to. We propose a fivelayer, double-funnel model for describing end-to-end redirection spam, present a methodology for analyzing the layers, and identify prominent domains on each layer using two sets of commercial keywords – one targeting spammers and the other targeting advertisers. The methodology and findings are useful for search engines to strengthen their ranking algorithms against spam, for legitimate website owners to locate and remove spam doorway pages, and for legitimate advertisers to identify unscrupulous syndicators who serve ads on spam pages.

www.ilab.sztaki.hu/websearch We investigate the usability of similarity search in fighting Web spam based on the assumption that an unknown spam page is more similar to certain known spam pages than to honest pages. In order to be successful, search engine spam never appears in isolation: we observe link farms and alliances for the sole purpose of search engine ranking manipulation. The artificial nature and strong inside connectedness however gave rise to successful algorithms to identify search engine spam. One example is trust and distrust propagation, an idea originating in recommender systems and P2P networks, that yields spam classificators by spreading information along hyperlinks from white and blacklists. While most previous results use PageRank variants for propagation, we form classifiers by investigating similarity top lists of an unknown page along various measures such as co-citation, companion, nearest neighbors in low dimensional projections and SimRank. We test our method over two data sets previously used to measure spam filtering algorithms. 1.

Abstract. We compare a wide range of semi-supervised learning techniques both for Web spam filtering and for telephone user churn classification. Semisupervised learning has the assumption that the label of a node in a graph is similar to those of its neighbors. In this paper we measure this phenomenon both for Web spam and telco churn. We conclude that spam is often linked to spam while honest pages are linked to honest ones; similarly churn occurs in bursts in groups of a social network. 1

The presence of Web spam in query results is one of the critical challenges facing search engines today. While search engines try to combat the impact of spam pages on their results, the incentive for spammers to use increasingly sophisticated techniques has never been higher, since the commercial success of a Web page is strongly correlated to the number of views that page receives. This paper describes a term-based technique for spam detection based on a simple new summary data structure called Term Distance Histograms that tries to capture the topical structure of a page. We apply this technique as a post-filtering step to a major search engine. Our experiments show that we are able to detect many of the artificially generated spam pages that remained in the results of the engine. Specifically, our method is able to detect many web pages generated by utilizing techniques such as dumping, weaving, or phrase stitching [11], which are spamming techniques designed to achieve high rankings while still exhibiting many of the individual word frequency (and even bi-gram) properties of natural human text.

The Web today includes many pages intended to deceive search engines, in which content or links are created to attain an unwarranted result ranking. Since the links among web pages are used to calculate authority, ranking systems should take into consideration which pages contain content to be trusted and which do not. In this paper, we assume the existence of a mechanism, such as, but not limited to Gyöngyi et al.’s TrustRank, to estimate the trustworthiness of a given page. However, unlike existing work that uses trust to identify or demote spam pages, we propose how to incorporate a given trust estimate into the process of calculating authority for a cautious surfer. We apply a total of forty-five queries over two large, real-world datasets to demonstrate that incorporating trust into an authority calculation using our cautious surfer can improve PageRank’s precision at 10 by 11-26 % and average top-10 result quality by 53-81%. 1

Abstract. Today, more and more social networking websites support collaborative tagging, which allows users to annotate resources (e.g., video clips, blog posts, and bookmarks) on the web. Due to its increasing popularity, however, spammers started to target this new type of service and generate misleading tags either to increase the visibility of some resources or simply to confuse users. Consequently, the performance of applications built upon tag data, such as recovery and discovery of web resources, can be limited. In this paper, we propose an algorithm to identify spammers from the collaborating systems by employing a spam score propagating technique. The three dimensional relationship among users, tags and web resources is firstly represented by a graph structure. A set of seed nodes, where each node represents a user, are then selected and assigned values to indicate whether the corresponding users are spammers or not. The initial values are propagated through the graph to infer the status of the remaining users. Our experimental results demonstrate the effectiveness of this approach in identify tag spammers. 1

Abstract In client-server interaction scenarios over a network the problem of unsolicited network transactions is often encountered. In this paper, we propose a reputation model based on the behavioural history of long-lived network client identities as a solution to this problem. The reputations of clients are shared between trusted servers anonymously through global reputation analysers. Shared global reputations and local reputations help servers to infer local opinions of clients and control service levels in attempts to reduce unsolicited network transactions. 1

While Web archive quality is endangered by Web spam, a side effect of the high commercial value of top-ranked search-engine results, so far Web spam filtering technologies are rarely used by Web archivists. In this paper we make the first attempt to disseminate existing methodology and envision a solution for Web archives to share knowledge and unite efforts in Web spam hunting. We survey the state of the art in Web spam filtering illustrated by the recent Web spam challenge data sets and techniques and describe the filtering solution for archives envisioned in the LiWA—Living Web Archives project.

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