An Efficient Algorithm for Exploitative Monetization Scam Video Detection Over Social Media Platforms

ABSTRACT In recent years, there has been a surge in scam videos on YouTube which make false assurances of monetary benefits to the viewers by performing trivial tasks such as watching videos, downloading dubious apps, and sharing personal identifiable information (PII). The present work aims to detect such videos referred to as exploitative monetization scam videos. In the first step of the proposed two‐step algorithm, two new features are derived, named as scam–parent comment ratio features and nonscam parent comment ratio features using Word2Vec‐based comment classifier. The next step combines these features and other video metadata features to perform the final labeled video classification using machine learning algorithms. Five different algorithms, namely Gaussian Naive Bayes, logistic regression, support vector machine, adaboost and random forest have been used in the video classification task. The results illustrate that the area‐under‐curve (AUC) score of 91% is obtained for the video classification. A comparative analysis of the proposed model with the other state‐of‐the‐art methods shows that our model outperforms others in the exploitative monetization scam video classification task.