Clickbait Detection in YouTube Videos
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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.
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.
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14 of 39 references.
References · 14
- 2017Attention Is All You Needcited by 60,361
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- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
- 2013Generating Sequences With Recurrent Neural Networkscited by 554
- 2016Stop Clickbait: Detecting and Preventing Clickbaits in Online News Mediacited by 39
- 2017Some Like it Hoax: Automated Fake News Detection in Social Networkscited by 20
- 2019Misleading Metadata Detection on YouTubecited by 12
- 2018The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strengthcited by 6
- 2017Clickbait Detection in Tweets Using Self-attentive Networkcited by 5
- 2017Detecting Clickbait in Online Social Media: You Won't Believe How We Did Itcited by 4
- 2017Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Modelscited by 2
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