Contributo in atti di convegno, 2007, ENG, 10.1145/1277741.1277814

Know your neighbors: Web spam detection using the web topology

Castillo C.; Donato D.; Gionis A.; Murdock V.; Silvestri F.

Yahoo Research, Barcelona, Spain; Yahoo Research, Barcelona, Spain; Yahoo Research, Barcelona, Spain; Yahoo Research, Barcelona, Spain; CNR-ISTI, Pisa, Italy

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.

30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 423–430, Amsterdam, Netherland, 23-27 July 2007

Keywords

H.4.m Information Systems Applications. Miscellaneous, Web Spam Detection

CNR authors

Silvestri Fabrizio

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

ID: 91643

Year: 2007

Type: Contributo in atti di convegno

Creation: 2009-06-16 00:00:00.000

Last update: 2018-02-26 11:31:36.000

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:91643

DOI: 10.1145/1277741.1277814

Scopus: 2-s2.0-36448992581