You tube spam comment detection using support vector machine and k–nearest neighbor

Social networking such as YouTube, Facebook and others are very popular nowadays. The best thing about YouTube is user can subscribe also giving opinion on the comment section. However, this attract the spammer by spamming the comments on that videos. Thus, this study develop a YouTube detection fra...

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Main Authors: Aziz, Aqliima, Mohd Foozy, Cik Feresa, Palaniappan, Shamala, Suradi, Zurinah
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science (IAES) 2017
Subjects:
Online Access:http://eprints.uthm.edu.my/4905/1/AJ%202017%20%28693%29.pdf
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author Aziz, Aqliima
Mohd Foozy, Cik Feresa
Palaniappan, Shamala
Suradi, Zurinah
author_facet Aziz, Aqliima
Mohd Foozy, Cik Feresa
Palaniappan, Shamala
Suradi, Zurinah
author_sort Aziz, Aqliima
collection UTHM
description Social networking such as YouTube, Facebook and others are very popular nowadays. The best thing about YouTube is user can subscribe also giving opinion on the comment section. However, this attract the spammer by spamming the comments on that videos. Thus, this study develop a YouTube detection framework by using Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). There are five (5) phases involved in this research such as Data Collection, Pre-processing, Feature Selection, Classification and Detection. The experiments is done by using Weka and RapidMiner. The accuracy result of SVM and KNN by using both machine learning tools show good accuracy result. Others solution to avoid spam attack is trying not to click the link on comments to avoid any problems
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spelling uthm.eprints-49052021-12-23T06:57:13Z http://eprints.uthm.edu.my/4905/ You tube spam comment detection using support vector machine and k–nearest neighbor Aziz, Aqliima Mohd Foozy, Cik Feresa Palaniappan, Shamala Suradi, Zurinah T Technology (General) TK7800-8360 Electronics Social networking such as YouTube, Facebook and others are very popular nowadays. The best thing about YouTube is user can subscribe also giving opinion on the comment section. However, this attract the spammer by spamming the comments on that videos. Thus, this study develop a YouTube detection framework by using Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). There are five (5) phases involved in this research such as Data Collection, Pre-processing, Feature Selection, Classification and Detection. The experiments is done by using Weka and RapidMiner. The accuracy result of SVM and KNN by using both machine learning tools show good accuracy result. Others solution to avoid spam attack is trying not to click the link on comments to avoid any problems Institute of Advanced Engineering and Science (IAES) 2017 Article PeerReviewed text en http://eprints.uthm.edu.my/4905/1/AJ%202017%20%28693%29.pdf Aziz, Aqliima and Mohd Foozy, Cik Feresa and Palaniappan, Shamala and Suradi, Zurinah (2017) You tube spam comment detection using support vector machine and k–nearest neighbor. Indonesian Journal of Electrical Engineering and Computer Science, 12 (2). pp. 612-619. ISSN 2502-4752 http://doi.org/10.11591/ijeecs.v12.i2.pp612-619
spellingShingle T Technology (General)
TK7800-8360 Electronics
Aziz, Aqliima
Mohd Foozy, Cik Feresa
Palaniappan, Shamala
Suradi, Zurinah
You tube spam comment detection using support vector machine and k–nearest neighbor
title You tube spam comment detection using support vector machine and k–nearest neighbor
title_full You tube spam comment detection using support vector machine and k–nearest neighbor
title_fullStr You tube spam comment detection using support vector machine and k–nearest neighbor
title_full_unstemmed You tube spam comment detection using support vector machine and k–nearest neighbor
title_short You tube spam comment detection using support vector machine and k–nearest neighbor
title_sort you tube spam comment detection using support vector machine and k nearest neighbor
topic T Technology (General)
TK7800-8360 Electronics
url http://eprints.uthm.edu.my/4905/1/AJ%202017%20%28693%29.pdf
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