Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild
Understanding and forecasting audience reactions to video content are crucial for improving content creation, recommendation systems, and media analysis. To enable audience reaction prediction and other content engagement applications, we introduce $\textbf{Video2Reaction}$, a multimodal dataset that maps short movie segments to a distribution of $\textit{induced emotions}$ of viewers in the wild, as expressed through social media. $\textbf{Video2Reaction}$ spans more than 10,000 videos and serves as a reliable benchmark as well as a training resource for audience reaction prediction. To enable cost-effective continuous annotations as reactions may change over time, we develop a two-stage...
| Authors | Trang Nguyen, Sidong Zhang, Shiv Shankar et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
Models and datasets referencing this paper
- infofusionlab/Video2ReactionDataset
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