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.
@article{arxiv.2512.21402,
title = {Understanding Virality: A Rubric based Vision-Language Model Framework for Short-Form Edutainment Evaluation},
author = {Arnav Gupta and Gurekas Singh Sahney and Hardik Rathi and Abhishek Chandwani and Ishaan Gupta and Pratik Narang and Dhruv Kumar},
journal= {arXiv preprint arXiv:2512.21402},
year = {2025}
}