# Clickbait Detection in YouTube Videos

arXiv: https://arxiv.org/abs/2107.12791 · HTML: https://mlsanity.com/paper/2107.12791/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 26 Jul 2021.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media](https://mlsanity.com/paper/1610.09786/) (2016)
- [Some Like it Hoax: Automated Fake News Detection in Social Networks](https://mlsanity.com/paper/1704.07506/) (2017)
- [Misleading Metadata Detection on YouTube](https://mlsanity.com/paper/1901.08759/) (2019)
- [The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength](https://mlsanity.com/paper/1812.10847/) (2018)
- [Clickbait Detection in Tweets Using Self-attentive Network](https://mlsanity.com/paper/1710.05364/) (2017)
- [Detecting Clickbait in Online Social Media: You Won't Believe How We Did It](https://mlsanity.com/paper/1710.06699/) (2017)
- [Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models](https://mlsanity.com/paper/1710.06390/) (2017)
- [Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approach](https://mlsanity.com/paper/1907.07604/) (2019)
- [Semi-Supervised Confidence Network aided Gated Attention based Recurrent Neural Network for Clickbait Detection](https://mlsanity.com/paper/1811.01355/) (2018)

## Abstract (arXiv)

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.