Spam, damn spam, and statistics: using statistical analysis to locate spam web pages

By Dennis Fetterly, Mark S. Manasse, Marc Najork. International Workshop on the Web and Databases, 2004

360 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/69a076f962b868b0e276ddb0399041ba54f736d9

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Citations in the corpus

Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter (2011, methodology): “Others are spam email analysis based on data compression algorithms (Bratko et al. 2006), machine learning (Goodman , Heckerman, and Rounthwaite 2005; Sahami et al. 1998) or statistics (Fetterly, Manasse, and Najork 2004; Ntoulas et al. 2006; Yoshida et al. 2004).”
Detecting Fake Content with Relative Entropy Scoring (2008, background): “Based on such attributes, it is fairly straightforward to build robust, genre independent, classification systems that can sort salads from natural texts with a pretty high accuracy [5, 13, 11].”
Introducing the Webb Spam Corpus: Using Email Spam to Identify Web Spam Automatically (2006, background): “Web spam is regarded as one of the most important challenges facing search engines and Web users [12] [14], and recent studies suggest that it accounts for a significant portion of all Web content, including 8% of Web pages [11] and 18% of Web sites [13].”
Discovering Large Dense Subgraphs in Massive Graphs (2005, background): “While measurements are diﬃcult, it is generally believed that a nontrivial fraction of the web is spam of one form or another; the Search Engine Optimization community typically reports a number from 25% to 40%, while Fetterly et al. [14] have reported 5%.”
Understanding User Behavior Through Log Data and Analysis (2014, background): “For example, if a search engine boosts results that are consistently clicked on for a query towards the top of the ranked list, then spammers may game the system by repeatedly clicking a result solely for the purpose of boosting its rank [Fetterly et al., 2004].”
Detecting spam web pages through content analysis (2006, methodology): “In addition, the MSN Search crawler already uses numerous spam detection heuristics, including many described in [8].”
Detecting Spammers on Twitter (2010, background): “Spam has been observed in various applications, including e-mail [9], Web search engines [14], blogs [25], videos [7, 8], opinions [18], and social networks [?].”
Efficient semi-streaming algorithms for local triangle counting in massive graphs (2008, background): “Spam and non-spam pages exhibit different statistical properties, and this difference can be exploited for Web Spam Detection [18].”
WebKnoGraph: GNN-Powered Internal Linking (2026, unknown): “Live evaluation is also noisy: deployed link changes interact with content updates, external links, crawl behavior, seasonality, ranking-system changes, and signals such as relevance, anchor context, freshness, topical consistency, and spam safeguards [1, 10, 14, 32].”