  Unscrupulous content creators on YouTube employ deceptive techniques such as
spam and clickbait to reach a broad audience and trick users into clicking on
their videos to increase their advertisement revenue. Clickbait detection on
YouTube requires an in depth examination and analysis of the intricate
relationship between the video content and video descriptors title and
thumbnail. However, the current solutions are mostly centred around the study
of video descriptors and other metadata such as likes, tags, comments, etc and
fail to utilize the video content, both video and audio. Therefore, we
introduce a novel model to detect clickbaits on YouTube that consider the
relationship between video content and title or thumbnail. The proposed model
consists of a stacking classifier framework composed of six base models (K
Nearest Neighbours, Support Vector Machine, XGBoost, Naive Bayes, Logistic
Regression, and Multilayer Perceptron) and a meta classifier. The developed
clickbait detection model achieved a high accuracy of 92.89% for the novel
BollyBAIT dataset and 95.38% for Misleading Video Dataset. Additionally, the
stated classifier does not use meta features or other statistics dependent on
user interaction with the video (the number of likes, followers, or comments)
for classification, and thus, can be used to detect potential clickbait videos
before they are uploaded, thereby preventing the nuisance of clickbaits
altogether and improving the users streaming experience.
