# YTClickbait21K: Human-Annotated Multimodal Dataset for YouTube Clickbait Detection Across Diverse Channels and Content Categories

arXiv: https://arxiv.org/abs/2606.14780 · HTML: https://mlsanity.com/paper/2606.14780/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 10 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media](https://mlsanity.com/paper/1610.09786/) (2016)
- [The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength](https://mlsanity.com/paper/1812.10847/) (2018)
- [Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines](https://mlsanity.com/paper/2310.13859/) (2023)
- [Detecting Clickbait in Online Social Media: You Won't Believe How We Did It](https://mlsanity.com/paper/1710.06699/) (2017)
- [Baitradar: A Multi-Model Clickbait Detection Algorithm Using Deep Learning](https://mlsanity.com/paper/2505.17448/) (2025)
- [Clickbait in YouTube Prevention, Detection and Analysis of the Bait using Ensemble Learning](https://mlsanity.com/paper/2112.08611/) (2021)
- [BaitBuster-Bangla: A Comprehensive Dataset for Clickbait Detection in Bangla with Multi-Feature and Multi-Modal Analysis](https://mlsanity.com/paper/2310.11465/) (2023)
- [ThumbnailTruth: A Multi-Modal LLM Approach for Detecting Misleading YouTube Thumbnails Across Diverse Cultural Settings](https://mlsanity.com/paper/2509.04714/) (2025)

## Abstract (arXiv)

Clickbait content on video-sharing platforms poses a significant challenge to information reliability, yet progress in automated detection has been constrained by the lack of large-scale, high-quality multimodal datasets. We present YTClickbait21K, a human-annotated YouTube clickbait dataset comprising 21,238 videos collected from 40 channels across 29 countries, covering diverse content categories such as news, entertainment, education, and gaming. Each sample includes structured metadata (title, description, engagement statistics) along with associated thumbnail images, enabling comprehensive multimodal analysis. To ensure annotation quality, every video was independently labeled by three annotators using a standardized decision framework that incorporates textual, visual, and cross-modal consistency cues, with final labels determined through majority voting. The dataset exhibits substantial inter-annotator agreement (k=0.65), confirming reliable labeling despite the inherent subjectivity of clickbait detection. By combining scale, annotation rigor, and multimodal richness, this dataset provides a robust benchmark for developing and evaluating machine learning models, facilitating research in cross-modal semantic understanding, and advancing automated content moderation systems.