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Unscrupulous content creators on YouTube employ deceptive techniques such asspam and clickbait to reach a broad audience and trick users into clicking ontheir videos to increase their advertisement revenue. Clickbait detection onYouTube requires an in depth examination and analysis of the intricaterelationship between the video content and video descriptors title andthumbnail. However, the current solutions are mostly centred around the studyof video descriptors and other metadata such as likes, tags, comments, etc andfail to utilize the video content, both video and audio. Therefore, weintroduce a novel model to detect clickbaits on YouTube that consider therelationship between video content and title or thumbnail. The proposed modelconsists of a stacking classifier framework composed of six base models (KNearest Neighbours, Support Vector Machine, XGBoost, Naive Bayes, LogisticRegression, and Multilayer Perceptron) and a meta classifier. The developedclickbait detection model achieved a high accuracy of 92.89% for the novelBollyBAIT dataset and 95.38% for Misleading Video Dataset. Additionally, thestated classifier does not use meta features or other statistics dependent onuser interaction with the video (the number of likes, followers, or comments)for classification, and thus, can be used to detect potential clickbait videosbefore they are uploaded, thereby preventing the nuisance of clickbaitsaltogether and improving the users streaming experience.

Clickbait in YouTube Prevention, Detection and Analysis of the Bait using Ensemble Learning