Detecting video spammers in YouTube social media - Yusof, Yuhanis; Sadoon, Omar Hadeb (2017)
Social media is any site that provides a network of people with a place 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 unrelated content. Even though the detection of malicious users is based on various features such as content details, social activity, social network analyzing, or hybrid, the detection rate is still considered low (i.e. 46%).This study proposes a new set of features by constructing features based on the Edge Rank algorithm.Experiments were performed using nine classifiers of different learning; decision tree, function-based and Bayesian. The results showed that the proposed video spammers detection feature set is beneficial as the highest accuracy (i.e average) is as high as 98% and the lowest was 74%.The proposed work would benefit YouTube users as malicious users who are sharing non relevant content can be automatically detected.This is because system resources can be optimized as YouTube users are presented with the required content only.