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

2026-06-10

AI Summary

This study addresses the challenge of clickbait content undermining information credibility on video platforms, a problem exacerbated by the scarcity of high-quality multimodal datasets. To bridge this gap, the authors construct a large-scale, cross-regional, and multi-category dataset comprising 21,238 YouTube videos from 40 channels across 29 countries, integrating titles, descriptions, engagement metrics, and thumbnails. High annotation consistency (Cohen’s κ = 0.65) is achieved through independent labeling by three annotators, a standardized decision framework, and majority voting. This dataset represents the most extensive, rigorously annotated, and modality-rich benchmark currently available for clickbait detection, offering critical support for advancing cross-modal semantic understanding and developing robust content moderation systems.

Abstract

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.

Problem

• clickbait detection
• multimodal dataset
• YouTube
• content moderation
• information reliability

Innovation

• multimodal dataset
• clickbait detection
• human annotation
• cross-modal consistency
• content moderation