Master of Science in Computer Science (MSCS)
Fabio Di Troia
clickbait, logistic regression, random forests, and multilayer perceptrons
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. This creates an incentive for people to post clickbait videos, in which the content might deviate significantly from the title, description, or thumbnail. In effect, users are tricked into clicking on clickbait videos. In this research, we consider the challenging problem of detecting clickbait YouTube videos. We experiment with logistic regression, random forests, and multilayer perceptrons, based on a variety of textual features. We obtain a maximum accuracy in excess of 94%.
Gothankar, Ruchira, "Clickbait Detection in YouTube Videos" (2021). Master's Projects. 1017.
Artificial Intelligence and Robotics Commons, Information Security Commons, Other Computer Sciences Commons