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research-article

Content-based analysis to detect Arabic web spam

Authors:

Mohammed Al-Kabi,

Heider Wahsheh,

Emad Al-Shawakfa,

Abdullah Wahbeh,

Ahmed Al-HmoudAuthors Info & Claims

Journal of Information Science, Volume 38, Issue 3

Pages 284 - 296

https://doi.org/10.1177/0165551512439173

Published: 01 June 2012 Publication History

Abstract

Search engines are important outlets for information query and retrieval. They have to deal with the continual increase of information available on the web, and provide users with convenient access to such huge amounts of information. Furthermore, with this huge amount of information, a more complex challenge that continuously gets more and more difficult to illuminate is the spam in web pages. For several reasons, web spammers try to intrude in the search results and inject artificially biased results in favour of their websites or pages. Spam pages are added to the internet on a daily basis, thus making it difficult for search engines to keep up with the fast-growing and dynamic nature of the web, especially since spammers tend to add more keywords to their websites to deceive the search engines and increase the rank of their pages. In this research, we have investigated four different classification algorithms (naïve Bayes, decision tree, SVM and K-NN) to detect Arabic web spam pages, based on content. The three groups of datasets used, with 1%, 15% and 50% spam contents, were collected using a crawler that was customized for this study. Spam pages were classified manually. Different tests and comparisons have revealed that the Decision Tree was the best classifier for this purpose.

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Cited By

Chen XYuan YOrgun M(2021)Using Bayesian networks with hidden variables for identifying trustworthy users in social networksJournal of Information Science10.1177/016555151985759046:5(600-615)Online publication date: 25-Feb-2021
https://dl.acm.org/doi/10.1177/0165551519857590
Najadat HAlzubaidi MQarqaz I(2021)Detecting Arabic Spam Reviews in Social Networks Based on Classification AlgorithmsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/347611521:1(1-13)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1145/3476115
Alhaj YWickramaarachchi WHussain AAl-Qaness MAbdelaal H(2018)Efficient Feature Representation Based on the Effect of Words Frequency for Arabic Documents ClassificationProceedings of the 2nd International Conference on Telecommunications and Communication Engineering10.1145/3291842.3291900(397-401)Online publication date: 28-Nov-2018
https://dl.acm.org/doi/10.1145/3291842.3291900
Show More Cited By

Content-based analysis to detect Arabic web spam
1. Information systems

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Published In

cover image Journal of Information Science

Journal of Information Science Volume 38, Issue 3

June 2012

54 pages

ISSN:0165-5515

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Publisher

Sage Publications, Inc.

United States

Publication History

Published: 01 June 2012

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Cited By

Chen XYuan YOrgun M(2021)Using Bayesian networks with hidden variables for identifying trustworthy users in social networksJournal of Information Science10.1177/016555151985759046:5(600-615)Online publication date: 25-Feb-2021
https://dl.acm.org/doi/10.1177/0165551519857590
Najadat HAlzubaidi MQarqaz I(2021)Detecting Arabic Spam Reviews in Social Networks Based on Classification AlgorithmsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/347611521:1(1-13)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1145/3476115
Alhaj YWickramaarachchi WHussain AAl-Qaness MAbdelaal H(2018)Efficient Feature Representation Based on the Effect of Words Frequency for Arabic Documents ClassificationProceedings of the 2nd International Conference on Telecommunications and Communication Engineering10.1145/3291842.3291900(397-401)Online publication date: 28-Nov-2018
https://dl.acm.org/doi/10.1145/3291842.3291900
Al-Badarneh AAl-Shawakfa EBani-Ismail BAl-Rababah KShatnawi S(2017)The impact of indexing approaches on Arabic text classificationJournal of Information Science10.1177/016555151562503043:2(159-173)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1177/0165551515625030
Zhuang XZhu YChang CPeng QKhurshid F(2017)A unified score propagation model for web spam demotion algorithmInformation Retrieval10.1007/s10791-017-9307-920:6(547-574)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s10791-017-9307-9

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