Clickbait Detection for YouTube Videos

Authors

Ruchira Gothankar, San Jose State University
Fabio Di Troia, San Jose State UniversityFollow
Mark Stamp, San Jose State UniversityFollow

Publication Date

1-1-2022

Document Type

Contribution to a Book

Publication Title

Advances in Information Security

Volume

54

DOI

10.1007/978-3-030-97087-1_11

First Page

261

Last Page

284

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.

Department

Computer Science

Recommended Citation

Ruchira Gothankar, Fabio Di Troia, and Mark Stamp. "Clickbait Detection for YouTube Videos" Advances in Information Security (2022): 261-284. https://doi.org/10.1007/978-3-030-97087-1_11