← 研踪 PaperTrailarXiv:2611.13002 · v1
YTShorts-92K: An Interpretable Multimodal Dataset of YouTube Shorts
首次提交 2026-11-09 00:41
版本更新 2026-11-09 00:41
雷达观测 2026-11-11 11:05
摘要
Short-form video has become one of the dominant formats of online content, yet large-scale datasets enabling the analysis of what makes these videos successful remain limited. Existing resources often focus on post-publication popularity signals or rely on non-interpretable representations such as high-dimensional embeddings. Here, we introduce YTShorts-92K, a dataset of 92,654 YouTube Shorts collected from 345 creators and spanning 12 manually defined content categories. Each video is enriched with an interpretable multimodal feature set covering metadata, textual information, visual characteristics from frames and thumbnails, and cross-modal consistency measures. We describe the collection methodology, data cleaning process, and feature extraction pipeline, followed by descriptive analyses of the diversity of creators, topics, and extracted features. This dataset offers a reusable resource for studying short-form video content, online attention, and multimodal content analysis.
作者备注：10 pages, 3 figures, 3 tables
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v1 · 2026-11-09 00:41 · 最新观测版本
YTShorts-92K: An Interpretable Multimodal Dataset of YouTube Shorts
Short-form video has become one of the dominant formats of online content, yet large-scale datasets enabling the analysis of what makes these videos successful remain limited. Existing resources often focus on post-publication popularity signals or rely on non-interpretable representations such as high-dimensional embeddings. Here, we introduce YTShorts-92K, a dataset of 92,654 YouTube Shorts collected from 345 creators and spanning 12 manually defined content categories. Each video is enriched with an interpretable multimodal feature set covering metadata, textual information, visual characteristics from frames and thumbnails, and cross-modal consistency measures. We describe the collection methodology, data cleaning process, and feature extraction pipeline, followed by descriptive analyses of the diversity of creators, topics, and extracted features. This dataset offers a reusable resource for studying short-form video content, online attention, and multimodal content analysis.
查看此版本解读 →查看此版本原文 ↗