TY  - JOUR
T1  - Detecting Malicious User in YouTube Using Edge Rank Based Feature Set
AU - Sadoon, Omar Hadeb AU - Yusof, Yuhanis 
JO  - International Journal of Soft Computing
VL  - 12
IS  - 1
SP  - 7
EP  - 12
PY  - 2017
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2017.7.12
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2017.7.12
KW  - Social network
KW  -spam detection
KW  -malicious users
KW  -edge rank
KW  -feature construction
AB  - Social media are websites that provide a network of people channels to make connections. An
example of the media is YouTube that connects people through video sharing. Unfortunately, due to the
explosive number of users and various content sharing there exist malicious users who aim to self-promote their
videos or broadcast viruses and malwares. Even though the detection of malicious users is based on various
features such as content details, social activity, social network analysing or a hybrid of features, the detection
rate is still considered low (i.e., 46%). This study proposes a new set of features which is based on edge rank
concept that focuses on affinity, weight and decay. The research is realized by analysing a set of YouTube
users and their shared video prior to classifying the users using seven classifiers. Evaluation is performed by
comparing the classification results of the proposed features against the existing feature set. Experiments
showed that 86% of the classifiers obtained better results when using the proposed feature set as compared
to using the existing features. The average classification accuracy is at 95.6%. Such a result indicates that the
proposed work would benefit YouTube users in obtaining the required multimedia content and creating trust
among users. In addition, system resources can be optimized as malicious accounts do not exist.
ER  -