Bait Detector: YouTube Video Recommendation

Abstract: We attempt to detect clickbait with our design model. YouTube videos often include captivating descriptions and intriguing thumbnails designed to increase the number of views, and thereby increase the revenue for the person who posted the video. Initially, in the proposed system, we gather data like the audio transcript from YouTube along with Title, Comments, likes, views, and Statistics. we train, pre-process and evaluate the data sets. In Multi-Model Architecture, we apply the SVM algorithm for titles, Comments, likes, and Statistics. According to the output obtained by this algorithm, we classify video as clickbait or not.

Keywords: Scikit-learn (Sklearn), Regular Expression (Regex)