Clickbait Detection in YouTube Videos

TL;DR

This paper investigates methods to detect clickbait videos on YouTube by experimenting with various machine learning techniques and textual features to address the challenge of misleading content.

Contribution

It introduces a comprehensive approach using multiple machine learning models and textual features specifically for clickbait detection in YouTube videos.

Findings

Machine learning techniques can effectively identify clickbait videos.

Textual features significantly improve detection accuracy.

The study provides a benchmark for future clickbait detection research.

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 multiple state-of-the-art machine learning techniques using a variety of textual features.