arXiv:2607.06875v1 Announce Type: cross Abstract: 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 r
The proliferation of video content and advanced AI models makes audience reaction prediction increasingly feasible and valuable, driving the creation of specialized datasets.
Predicting audience reactions to video content is crucial for optimizing media, improving recommendation systems, and understanding public sentiment at scale.
The introduction of a large-scale, multimodal dataset specifically linking video content to real-world audience emotion distributions provides a new benchmark and training resource for content engagement AI.
- · Content creators
- · Social media platforms
- · Advertising technology
- · AI developers in media
- · Manual content analysis services
- · Inefficient recommendation systems
More accurately tailored video content and recommendations.
Potential for real-time content moderation and algorithmic editing based on predicted emotional impact.
Enhanced ability for propaganda and persuasion by precisely engineering emotional responses to video.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG