PolaritySpam: Propagating Content-based Information Through a Web-Graph to Detect Web Spam

Spam web pages have become a problem for Information Retrieval systems due to the negative effects that this phenomenon can cause in their results. In this work we tackle the problem of detecting these pages with a propagation algorithm that, taking as input a web graph, chooses a set of spam and no...

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Detalles Bibliográficos
Autores: Ortega Rodríguez, Francisco Javier, Troyano Jiménez, José Antonio, Cruz Mata, Fermín, García Vallejo, Carlos Antonio
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2012
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/130681
Acceso en línea:https://hdl.handle.net/11441/130681
Access Level:acceso abierto
Palabra clave:Information retrieval
Web spam detection
Graph algorithms
PageRank
Web search
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spelling PolaritySpam: Propagating Content-based Information Through a Web-Graph to Detect Web SpamOrtega Rodríguez, Francisco JavierTroyano Jiménez, José AntonioCruz Mata, FermínGarcía Vallejo, Carlos AntonioInformation retrievalWeb spam detectionGraph algorithmsPageRankWeb searchSpam web pages have become a problem for Information Retrieval systems due to the negative effects that this phenomenon can cause in their results. In this work we tackle the problem of detecting these pages with a propagation algorithm that, taking as input a web graph, chooses a set of spam and not-spam web pages in order to spread their spam likelihood over the rest of the network. Thus we take advantage of the links between pages to obtain a ranking of pages according to their relevance and their spam likelihood. Our intuition consists in giving a high reputation to those pages related to relevant ones, and giving a high spam likelihood to the pages linked to spam web pages. We introduce the novelty of including the content of the web pages in the computation of an a priori estimation of the spam likelihood of the pages, and propagate this information. Our graph-based algorithm computes two scores for each node in the graph. Intuitively, these values represent how bad or good (spam-like or not) is a web page, according to its textual content and its relations in the graph. The experimental results show that our method outperforms other techniques for spam detectionMinisterio de Educación y Ciencia HUM2007-66607-C04-04ICIC InternationalLenguajes y Sistemas InformáticosTIC134: Sistemas InformáticosMinisterio de Educación y Ciencia (MEC). España2012info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/130681reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésInternational Journal of Innovative Computing, Information and Control, 8 (4), 2915-2928.HUM2007-66607-C04-04http://www.ijicic.org/contents.htminfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1306812026-06-17T12:51:07Z
dc.title.none.fl_str_mv PolaritySpam: Propagating Content-based Information Through a Web-Graph to Detect Web Spam
title PolaritySpam: Propagating Content-based Information Through a Web-Graph to Detect Web Spam
spellingShingle PolaritySpam: Propagating Content-based Information Through a Web-Graph to Detect Web Spam
Ortega Rodríguez, Francisco Javier
Information retrieval
Web spam detection
Graph algorithms
PageRank
Web search
title_short PolaritySpam: Propagating Content-based Information Through a Web-Graph to Detect Web Spam
title_full PolaritySpam: Propagating Content-based Information Through a Web-Graph to Detect Web Spam
title_fullStr PolaritySpam: Propagating Content-based Information Through a Web-Graph to Detect Web Spam
title_full_unstemmed PolaritySpam: Propagating Content-based Information Through a Web-Graph to Detect Web Spam
title_sort PolaritySpam: Propagating Content-based Information Through a Web-Graph to Detect Web Spam
dc.creator.none.fl_str_mv Ortega Rodríguez, Francisco Javier
Troyano Jiménez, José Antonio
Cruz Mata, Fermín
García Vallejo, Carlos Antonio
author Ortega Rodríguez, Francisco Javier
author_facet Ortega Rodríguez, Francisco Javier
Troyano Jiménez, José Antonio
Cruz Mata, Fermín
García Vallejo, Carlos Antonio
author_role author
author2 Troyano Jiménez, José Antonio
Cruz Mata, Fermín
García Vallejo, Carlos Antonio
author2_role author
author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
TIC134: Sistemas Informáticos
Ministerio de Educación y Ciencia (MEC). España
dc.subject.none.fl_str_mv Information retrieval
Web spam detection
Graph algorithms
PageRank
Web search
topic Information retrieval
Web spam detection
Graph algorithms
PageRank
Web search
description Spam web pages have become a problem for Information Retrieval systems due to the negative effects that this phenomenon can cause in their results. In this work we tackle the problem of detecting these pages with a propagation algorithm that, taking as input a web graph, chooses a set of spam and not-spam web pages in order to spread their spam likelihood over the rest of the network. Thus we take advantage of the links between pages to obtain a ranking of pages according to their relevance and their spam likelihood. Our intuition consists in giving a high reputation to those pages related to relevant ones, and giving a high spam likelihood to the pages linked to spam web pages. We introduce the novelty of including the content of the web pages in the computation of an a priori estimation of the spam likelihood of the pages, and propagate this information. Our graph-based algorithm computes two scores for each node in the graph. Intuitively, these values represent how bad or good (spam-like or not) is a web page, according to its textual content and its relations in the graph. The experimental results show that our method outperforms other techniques for spam detection
publishDate 2012
dc.date.none.fl_str_mv 2012
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/130681
url https://hdl.handle.net/11441/130681
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv International Journal of Innovative Computing, Information and Control, 8 (4), 2915-2928.
HUM2007-66607-C04-04
http://www.ijicic.org/contents.htm
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv ICIC International
publisher.none.fl_str_mv ICIC International
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301629

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