Web Spam Taxonomy

By Zoltán Gyöngyi, Hector Garcia-Molina. 2005

691 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. B. Liu
2. Bing Liu
3. Jimmy J. Lin
4. Nitin Jindal
5. Lei Zhang
6. Arjun Mukherjee
7. Alexandros Ntoulas
8. Zixuan Ke

Citations in the corpus

• Detecting Fake Content with Relative Entropy Scoring (2008, background): “…such as hashing; • many spam Web sites are designed to automatically generate thousands of interconnected web pages on a selected topic, in order to reach the top of search engines response lists [7]; • many fake friends generators are available to boost one’s popularity in social networks [9].”
• To Clean or Not to Clean (2018, background): “A great deal of work has been done on IR in the presence of adversarial information providers and on detecting spam pages [12, 13, 21, 39, 53, 67].”
• Detecting spam web pages through content analysis (2006, background): “Gyöngyi and GarciaMolina [13] provide a more formal taxonomy of web spam.”
• Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer (2010, background): “) who are trying to manipulate search results—to promote a cause, product, or service, or in some cases, to trap and intentionally deceive users (see, for example, [6, 43]).”
• Introducing the Webb Spam Corpus: Using Email Spam to Identify Web Spam Automatically (2006, background): “Web spam is defined as Web pages that are created to manipulate search engines and deceive Web users [12] [13].”
• Review spam detection (2007, methodology): “Thus, most existing methods for detecting web spam and email spam [3, 7, 9, 11] are unsuitable for review spam.”
• Finding unusual review patterns using unexpected rules (2010, background): “The problem is analogous to spam in Web search [1, 9 11].”
• Google-bombing - Manipulating the PageRank Algorithm (2013, methodology): “The phrase “spamdexing” combines the terms “spam” and “indexing”, indicating an application of spamming techniques to the indexing process [4].”
• Microsoft Academic Graph: When experts are not enough (2020, background): “…are analogous to anchor texts in the web search scenario, in which they play a key role in determining the strength of affiliation between the citing and cited documents and contributing to the identification of spurious references to defend against link spam (Gyöngyi & Garcia-Molina, 2005).”
• Opinion spam and analysis (2008, background): “In the context of Web search, due to the economic and/or publicity value of the rank position of a page returned by a search engine, Web page spam is widespread [3, 5, 10, 24, 25, 26].”