Web spam detection via commercial intent analysis

Benczúr, András A.; Bíró, István; Csalogány, Károly; Sarlós, Tamás

doi:10.1145/1244408.1244424

Cited by 30 publications

(35 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The utility-based spamicity is clearly more effective than the characteristics-based spamicity in detection quality. The F-measure results in Figure 12 are comparable to the results of the spam labeling challenge from the 2007 AIRWeb [Benczúr et al 2007;Chien et al 2007;Cormack 2007;Abou-Assaleh and Das 2007]. The winner method [Benczúr et al 2007] is reported to achieve an F-measure score of 0.91 on average.…”

Section: Spam Detectionsupporting

confidence: 54%

“…The F-measure results in Figure 12 are comparable to the results of the spam labeling challenge from the 2007 AIRWeb [Benczúr et al 2007;Chien et al 2007;Cormack 2007;Abou-Assaleh and Das 2007]. The winner method [Benczúr et al 2007] is reported to achieve an F-measure score of 0.91 on average. This indicates that our methods are comparable in performance with the state-of-the-art spam detection methods such as Benczúr et al [2007] and Chien et al [2007].…”

Section: Spam Detectionsupporting

confidence: 54%

“…The winner method [Benczúr et al 2007] is reported to achieve an F-measure score of 0.91 on average. This indicates that our methods are comparable in performance with the state-of-the-art spam detection methods such as Benczúr et al [2007] and Chien et al [2007]. The methods from AIRWeb 2007 do not use the spamicity-like approach.…”

Section: Spam Detectionmentioning

confidence: 99%

“…They need much more background information than our spamicity-based methods. For example, the winner method [Benczúr et al 2007] used features including Microsoft OCI and Yahoo! Mindset, Google AdWords, and AdSense, as well as the graph similarity.…”

Section: Spam Detectionmentioning

confidence: 99%

See 3 more Smart Citations

Link spam target detection using page farms

Zhou

Pei

2009

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

Currently, most popular Web search engines adopt some link-based ranking methods such as PageRank. Driven by the huge potential benefit of improving rankings of Web pages, many tricks have been attempted to boost page rankings. The most common way, which is known as link spam, is to make up some artificially designed link structures. Detecting link spam effectively is a big challenge. In this article, we develop novel and effective detection methods for link spam target pages using page farms. The essential idea is intuitive: whether a page is the beneficiary of link spam is reflected by how it collects its PageRank score. Technically, how a target page collects its PageRank score is modeled by a page farm, which consists of pages contributing a major portion of the PageRank score of the target page. We propose two spamicity measures based on page farms. They can be used as an effective measure to check whether the pages are link spam target pages. An empirical study using a newly available real dataset strongly suggests that our method is effective. It outperforms the state-of-the-art methods like SpamRank and SpamMass in both precision and recall.

show abstract

Section: Spam Detectionsupporting

confidence: 54%

Section: Spam Detectionsupporting

confidence: 54%

Section: Spam Detectionmentioning

confidence: 99%

Section: Spam Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Link spam target detection using page farms

Zhou

Pei

2009

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

show abstract

“…Much of that work has focused on graph-based methods for detecting link farms, i.e., groups of sites that exploit link structure to push up the ranking of other sites beyond what it should be [9,2,10,19]. Less work has been published on page-and site-based methods for identifying spam content, which is often either copied from other sites or automatically generated [17,7,8,3], although this is clearly an important ingredient in successful spam detection. Much of that work has relied on summary statistics about a page or site, such as the lengths of pages or URLs, the number of pages in a site, or sites in a domain, although actual page content is clearly also important.…”

Section: Related Workmentioning

confidence: 99%

Cleaning search results using term distance features

Attenberg¹,

Suel²

2008

Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web

View full text Add to dashboard Cite

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.

show abstract

Binary naive possibilistic classifiers: Handling uncertain inputs

Benferhat

Tabia

2009

Int. J. Intell. Syst.

View full text Add to dashboard Cite

Possibilistic networks are graphical models particularly suitable for representing and reasoning with uncertain and incomplete information. According to the underlying interpretation of possibilistic scales, possibilistic networks are either quantitative (using product-based conditioning) or qualitative (using min-based conditioning). Among the multiple tasks, possibilitic models can be used for, classification is a very important one. In this paper, we address the problem of handling uncertain inputs in binary possibilistic-based classification. More precisely, we propose an efficient algorithm for revising possibility distributions encoded by a naive possibilistic network. This algorithm is suitable for binary classification with uncertain inputs since it allows classification in polynomial time using several efficient transformations of initial naive possibilistic networks. C 2009 Wiley Periodicals, Inc.

show abstract

Web spam detection via commercial intent analysis

Cited by 30 publications

References 11 publications

Link spam target detection using page farms

Link spam target detection using page farms

Cleaning search results using term distance features

Binary naive possibilistic classifiers: Handling uncertain inputs

Contact Info

Product

Resources

About