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http://dx.doi.org/10.6109/jkiice.2020.24.5.576

Ensemble Machine Learning Model Based YouTube Spam Comment Detection  

Jeong, Min Chul (Department of Digital Media, Ajou University)
Lee, Jihyeon (Department of English Language and Literature, Ajou University)
Oh, Hayoung (Global Convergence, Sungkyunkwan University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.5, 2020 , pp. 576-583 More about this Journal
Abstract
This paper proposes a technique to determine the spam comments on YouTube, which have recently seen tremendous growth. On YouTube, the spammers appeared to promote their channels or videos in popular videos or leave comments unrelated to the video, as it is possible to monetize through advertising. YouTube is running and operating its own spam blocking system, but still has failed to block them properly and efficiently. Therefore, we examined related studies on YouTube spam comment screening and conducted classification experiments with six different machine learning techniques (Decision tree, Logistic regression, Bernoulli Naive Bayes, Random Forest, Support vector machine with linear kernel, Support vector machine with Gaussian kernel) and ensemble model combining these techniques in the comment data from popular music videos - Psy, Katy Perry, LMFAO, Eminem and Shakira.
Keywords
Data analysis; Classification; Spam Comment; Ensemble Machine Learning; Youtube comment;
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