Know your neighbors: web spam detection using the web topology

By Carlos Castillo, Debora Donato, Aristides Gionis, Vanessa Murdock, Fabrizio Silvestri. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2007

404 citations recorded by the source corpus.

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 combines link-based and content-based features, and uses the topology of the Web graph by exploiting the link dependencies among the Web pages. 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, tested on a large and public dataset, using algorithms that can be applied in practice to large-scale Web data.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/05a4c4a36fd24cf0be9d34e025985769e8efc683

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Citations in the corpus

Don't follow me: Spam detection in Twitter (2010, background): “In (Castillo et al., 2007), both link-based features and content-based features are proposed.”
Understanding and combating link farming in the twitter social network (2012, background): “Castillo et al. [8] also combined link-based and content-based features to build a spam classiﬁer using the idea that hosts that are well-linked together are likely to have the same class label (spam or non-spam).”
Efficient semi-streaming algorithms for local triangle counting in massive graphs (2008, result): “We also compared the number of triangles and clustering coefficient with a known set of link-based and content-based features for the hosts in this collection [12].”
Graph based anomaly detection and description: a survey (2014, background): “2009), email and Web spam (Castillo et al. 2007), opinion deception and reviews spam (Ott et al.”
What Yelp Fake Review Filter Might Be Doing? (2013, background): “Web spam (Castillo et al., 2007; Spirin and Han, 2012) and email spam (Chirita et al.”
Detecting Spammers on Twitter (2010, methodology): “[10] proposed a framework to detect Web spamming which uses a classification approach and explore social network metrics extracted from the Web graph.”