Tracking Web spam with HTML style similarities

Urvoy, Tanguy; Chauveau, Emmanuel; Filoche, Pascal; Lee, Thomas

doi:10.1145/1326561.1326564

Cited by 83 publications

(74 citation statements)

References 19 publications

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“…Due to the same independent hash values in the filled space, the error of the b-bit scheme corresponds to the error of the estimate of |S1∆S2|. Inaccuracy in just a few bit positions in the white space will yield a large relative error of the estimate of J. the scheme has been applied successfully in a variety of applications, including similarity search [4,5,6], association rule learning [8], compressing social networks [7], advertising diversification [11], tracking Web spam [21], web duplicate detection [15], large-scale learning [16], and more [1,3,14].…”

Section: Minwise Hashingmentioning

confidence: 99%

Efficient estimation for high similarities using odd sketches

Mitzenmacher

Pagh

Pham

2014

Proceedings of the 23rd International Conference on World Wide Web

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Estimating set similarity is a central problem in many computer applications. In this paper we introduce the Odd Sketch, a compact binary sketch for estimating the Jaccard similarity of two sets. The exclusive-or of two sketches equals the sketch of the symmetric difference of the two sets. This means that Odd Sketches provide a highly spaceefficient estimator for sets of high similarity, which is relevant in applications such as web duplicate detection, collaborative filtering, and association rule learning. The method extends to weighted Jaccard similarity, relevant e.g. for TF-IDF vector comparison.We present a theoretical analysis of the quality of estimation to guarantee the reliability of Odd Sketch-based estimators. Our experiments confirm this efficiency, and demonstrate the efficiency of Odd Sketches in comparison with b-bit minwise hashing schemes on association rule learning and web duplicate detection tasks.

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Section: Minwise Hashingmentioning

confidence: 99%

Efficient estimation for high similarities using odd sketches

Mitzenmacher

Pagh

Pham

2014

Proceedings of the 23rd International Conference on World Wide Web

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“…When referring to web content, structural features include the usage of HTML-encoded text, which includes the ability to format text with word bolding, italics, font coloring, font size, etc. [15]. Syntactic features refer to the sentencelevel of a document, including patterns used for formulating sentences.…”

Section: Stylometric and Text Analysismentioning

confidence: 99%

Evaluating text visualization for authorship analysis

et al. 2014

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Methods and tools to conduct authorship analysis of web contents is of growing interest to researchers and practitioners in various security-focused disciplines, including cybersecurity, counter-terrorism, and other fields in which authorship of text may at times be uncertain or obfuscated. Here we demonstrate an automated approach for authorship analysis of web contents. Analysis is conducted through the use of machine learning methodologies, an expansive stylometric feature set, and a series of visualizations intended to help facilitate authorship analysis at the author, message, and feature levels. To operationalize this, we utilize a testbed containing 506,554 forum messages in English and Arabic, source from 14,901 authors that participated in an online web forum. A prototype portal system providing authorship comparisons and visualizations was then designed and constructed in order to support feasibility analysis and real world value of the automated authorship analysis approach. A preliminary user evaluation was performed to assess the efficacy of visualizations, with evaluation results demonstrating task performance accuracy and efficiency was improved through use of the portal.

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“…Invisible text usually means text in the same color as the background or text in layers which are behind the normal text or which are invisible. Again, much research has been performed to identify content spam, among others [13][14][15].…”

Section: Related Workmentioning

confidence: 99%

On the robustness of google scholar against spam

Beel

Gipp

2010

Proceedings of the 21st ACM Conference on Hypertext and Hypermedia

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In this research-in-progress paper we present the current results of several experiments in which we analyzed whether spamming Google Scholar is possible. Our results show, it is possible: We 'improved' the ranking of articles by manipulating their citation counts and we made articles appear in searchers for keywords the articles did not originally contained by placing invisible text in modified versions of the article.

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Tracking Web spam with HTML style similarities

Cited by 83 publications

References 19 publications

Efficient estimation for high similarities using odd sketches

Efficient estimation for high similarities using odd sketches

Evaluating text visualization for authorship analysis

On the robustness of google scholar against spam

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