Publication Date

Spring 2021

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Mark Stamp

Second Advisor

Fabio Di Troia

Third Advisor

Mike Wu

Keywords

clickbait, logistic regression, random forests, and multilayer perceptrons

Abstract

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%.

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