Contributo in atti di convegno, 2007, ENG, 10.1145/1277741.1277814
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
H.4.m Information Systems Applications. Miscellaneous, Web Spam Detection
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
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
URL: http://dl.acm.org/citation.cfm?id=1277814&CFID=106740534&CFTOKEN=21970113
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:91643
DOI: 10.1145/1277741.1277814
Scopus: 2-s2.0-36448992581