Understanding Virality: A Rubric based Vision-Language Model Framework for Short-Form Edutainment Evaluation

• URL: http://arxiv.org/abs/2512.21402v1
• Date: Wed, 24 Dec 2025 19:43:59 GMT
• Title: Understanding Virality: A Rubric based Vision-Language Model Framework for Short-Form Edutainment Evaluation
• Authors: Arnav Gupta, Gurekas Singh Sahney, Hardik Rathi, Abhishek Chandwani, Ishaan Gupta, Pratik Narang, Dhruv Kumar,
• Abstract summary: VideoScore-2 does not capture how specific audiovisual attributes drive real audience engagement.<n>We propose a data-driven evaluation framework that uses Vision-Language Models (VLMs) to extract unsupervised audiovisual features.<n>Our approach advances toward robust and explainable video understanding.
• Score: 8.15791379444665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evaluating short-form video content requires moving beyond surface-level quality metrics toward human-aligned, multimodal reasoning. While existing frameworks like VideoScore-2 assess visual and semantic fidelity, they do not capture how specific audiovisual attributes drive real audience engagement. In this work, we propose a data-driven evaluation framework that uses Vision-Language Models (VLMs) to extract unsupervised audiovisual features, clusters them into interpretable factors, and trains a regression-based evaluator to predict engagement on short-form edutainment videos. Our curated YouTube Shorts dataset enables systematic analysis of how VLM-derived features relate to human engagement behavior. Experiments show strong correlations between predicted and actual engagement, demonstrating that our lightweight, feature-based evaluator provides interpretable and scalable assessments compared to traditional metrics (e.g., SSIM, FID). By grounding evaluation in both multimodal feature importance and human-centered engagement signals, our approach advances toward robust and explainable video understanding.

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