research-article

Web spam identification through content and hyperlinks

Authors:

Jacob Abernethy,

Olivier Chapelle,

Carlos CastilloAuthors Info & Claims

AIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web

Pages 41 - 44

https://doi.org/10.1145/1451983.1451994

Published: 22 April 2008 Publication History

Abstract

We present an algorithm, witch, that learns to detect spam hosts or pages on the Web. Unlike most other approaches, it simultaneously exploits the structure of the Web graph as well as page contents and features. The method is efficient, scalable, and provides state-of-the-art accuracy on a standard Web spam benchmark.

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Cited By

Wang XZong C(2023)Learning Category Distribution for Text ClassificationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/358527922:4(1-13)Online publication date: 12-Apr-2023
https://dl.acm.org/doi/10.1145/3585279
Gharebaghi FAmiri A(2022)LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised ClassificationIEEE Access10.1109/ACCESS.2021.313992910(13738-13752)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2021.3139929
Chatterjee MNamin A(2021)A fuzzy Dempster–Shafer classifier for detecting Web spamsJournal of Information Security and Applications10.1016/j.jisa.2021.10279359(102793)Online publication date: Jun-2021
https://doi.org/10.1016/j.jisa.2021.102793
Show More Cited By

Index Terms

Web spam identification through content and hyperlinks
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications

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Published In

cover image ACM Other conferences

AIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web

April 2008

81 pages

ISBN:9781605581590

DOI:10.1145/1451983

Editors:
Carlos Castillo
Yahoo! Research
,
Kumar Chellapilla
Microsoft Live Labs
,
Dennis Fetterly
Microsoft Research

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 April 2008

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AIRWeb'08

AIRWeb'08: AIRWeb '08, Third International Workshop on Adversarial Information Retrieval on the Web

April 22, 2008

Beijing, China

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Citations

Cited By

Wang XZong C(2023)Learning Category Distribution for Text ClassificationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/358527922:4(1-13)Online publication date: 12-Apr-2023
https://dl.acm.org/doi/10.1145/3585279
Gharebaghi FAmiri A(2022)LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised ClassificationIEEE Access10.1109/ACCESS.2021.313992910(13738-13752)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2021.3139929
Chatterjee MNamin A(2021)A fuzzy Dempster–Shafer classifier for detecting Web spamsJournal of Information Security and Applications10.1016/j.jisa.2021.10279359(102793)Online publication date: Jun-2021
https://doi.org/10.1016/j.jisa.2021.102793
Ren PXiao YChang XPrakash MNie FWang XChen X(2020)Structured Optimal Graph-Based Clustering With Flexible EmbeddingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2019.294632931:10(3801-3813)Online publication date: Oct-2020
https://doi.org/10.1109/TNNLS.2019.2946329
Wang YHan JShen YXue H(2020)Pointwise manifold regularization for semi-supervised learningFrontiers of Computer Science10.1007/s11704-019-9115-z15:1Online publication date: 13-Aug-2020
https://doi.org/10.1007/s11704-019-9115-z
Dornaika F(2019)Sparse graphs using global and local smoothness constraintsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-019-01035-zOnline publication date: 25-Nov-2019
https://doi.org/10.1007/s13042-019-01035-z
Cui YSparkman CLee HLoguinov D(2018)Unsupervised Domain Ranking in Large-Scale Web CrawlsACM Transactions on the Web10.1145/318218012:4(1-29)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3182180
Halawi BMourad AOtrok HDamiani E(2018)Few are as Good as Many: An Ontology-Based Tweet Spam Detection ApproachIEEE Access10.1109/ACCESS.2018.28776856(63890-63904)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2877685
Liu HHan JNie F(2017)Semi-supervised orthogonal graph embedding with recursive projectionsProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3172077.3172209(2308-2314)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3172077.3172209
Choo EYu TChi M(2017)Detecting opinion spammer groups and spam targets through community discovery and sentiment analysisJournal of Computer Security10.3233/JCS-1694125:3(283-318)Online publication date: 29-May-2017
https://doi.org/10.3233/JCS-16941
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