An improved framework for content and link-based web spam detection: a combined approach

In the modern digital era, the Web has been utilized for searching information by using different search engines (SE) as a tool. However, web spammers misuse the web for financial benefits by ranking the irrelevant and spam web pages higher than relevant pages in the search engine's results pag...

Full description

Saved in:

Bibliographic Details
Main Author:	Shahzad, Asim
Format:	Thesis
Language:	English English English
Published:	2021
Subjects:	QA76.75-76.765 Computer software
Online Access:	http://eprints.uthm.edu.my/1777/2/ASIM%20SHAHZAD%20-%20declaration.pdf http://eprints.uthm.edu.my/1777/1/ASIM%20SHAHZAD%20-%2024p.pdf http://eprints.uthm.edu.my/1777/3/ASIM%20SHAHZAD%20-%20fulltext.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uthm-ep.1777
record_format	uketd_dc
spelling	my-uthm-ep.17772021-10-11T07:58:48Z An improved framework for content and link-based web spam detection: a combined approach 2021-05 Shahzad, Asim QA76.75-76.765 Computer software In the modern digital era, the Web has been utilized for searching information by using different search engines (SE) as a tool. However, web spammers misuse the web for financial benefits by ranking the irrelevant and spam web pages higher than relevant pages in the search engine's results pages (SERPs) by using web spamming techniques. Furthermore, those top-ranked unrelated web pages contain insufficient or inappropriate information for the user. In addition, web spamming techniques dramatically affect the quality of the search engine. Researchers introduced several web spam detection techniques such as content-based features, link-based features, label propagation, label refinement, click-based web spamming detection, and real-time web spam detection. However, identifying all spam pages on the Web with high accuracy is still remains unsolved. This work proposes a content-based web spam detection framework, link-based web spam detection framework, and a combined approach to identify both types of web spams with high accuracy that can detect the newly evolved link pyramid. The content-based web spam detection framework uses three proposed and two improved content-based algorithms for web spam detection. The link-based web spam detection framework initially exposed the relationship network behind the link spamming and then used the paid-links database algorithm, spam signals algorithm, and improved link farms algorithm for link-based web spam identification. Finally, the combination of both content and link-based frameworks enhance the accuracy of web spam detection. The proposed combined approach's performance has been evaluated and compared with the J48 classifier, C4.5 decision tree classifier, SVM classifier, and heuristic combined approach. Some experiments were conducted to obtain the threshold values using the proposed collection architecture on well-known datasets WEB SPAM-UK2006 and WEB SPAM-UK2007. The results show that the proposed methods outperform other methods with 82.1% precision and an F-measure of 80.6% to illustrate the proposed framework's effectiveness and applicability. 2021-05 Thesis http://eprints.uthm.edu.my/1777/ http://eprints.uthm.edu.my/1777/2/ASIM%20SHAHZAD%20-%20declaration.pdf text en staffonly http://eprints.uthm.edu.my/1777/1/ASIM%20SHAHZAD%20-%2024p.pdf text en public http://eprints.uthm.edu.my/1777/3/ASIM%20SHAHZAD%20-%20fulltext.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA76.75-76.765 Computer software
spellingShingle	QA76.75-76.765 Computer software Shahzad, Asim An improved framework for content and link-based web spam detection: a combined approach
description	In the modern digital era, the Web has been utilized for searching information by using different search engines (SE) as a tool. However, web spammers misuse the web for financial benefits by ranking the irrelevant and spam web pages higher than relevant pages in the search engine's results pages (SERPs) by using web spamming techniques. Furthermore, those top-ranked unrelated web pages contain insufficient or inappropriate information for the user. In addition, web spamming techniques dramatically affect the quality of the search engine. Researchers introduced several web spam detection techniques such as content-based features, link-based features, label propagation, label refinement, click-based web spamming detection, and real-time web spam detection. However, identifying all spam pages on the Web with high accuracy is still remains unsolved. This work proposes a content-based web spam detection framework, link-based web spam detection framework, and a combined approach to identify both types of web spams with high accuracy that can detect the newly evolved link pyramid. The content-based web spam detection framework uses three proposed and two improved content-based algorithms for web spam detection. The link-based web spam detection framework initially exposed the relationship network behind the link spamming and then used the paid-links database algorithm, spam signals algorithm, and improved link farms algorithm for link-based web spam identification. Finally, the combination of both content and link-based frameworks enhance the accuracy of web spam detection. The proposed combined approach's performance has been evaluated and compared with the J48 classifier, C4.5 decision tree classifier, SVM classifier, and heuristic combined approach. Some experiments were conducted to obtain the threshold values using the proposed collection architecture on well-known datasets WEB SPAM-UK2006 and WEB SPAM-UK2007. The results show that the proposed methods outperform other methods with 82.1% precision and an F-measure of 80.6% to illustrate the proposed framework's effectiveness and applicability.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Shahzad, Asim
author_facet	Shahzad, Asim
author_sort	Shahzad, Asim
title	An improved framework for content and link-based web spam detection: a combined approach
title_short	An improved framework for content and link-based web spam detection: a combined approach
title_full	An improved framework for content and link-based web spam detection: a combined approach
title_fullStr	An improved framework for content and link-based web spam detection: a combined approach
title_full_unstemmed	An improved framework for content and link-based web spam detection: a combined approach
title_sort	improved framework for content and link-based web spam detection: a combined approach
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Computer Science and Information Technology
publishDate	2021
url	http://eprints.uthm.edu.my/1777/2/ASIM%20SHAHZAD%20-%20declaration.pdf http://eprints.uthm.edu.my/1777/1/ASIM%20SHAHZAD%20-%2024p.pdf http://eprints.uthm.edu.my/1777/3/ASIM%20SHAHZAD%20-%20fulltext.pdf
_version_	1747830864999350272

An improved framework for content and link-based web spam detection: a combined approach

Similar Items