Understanding and modeling the popularity of User Generated Content (UGC) short videos on social media platforms presents a critical challenge with broad implications for content creators and recommendation systems. This study delves deep into the intricacies of predicting engagement for newly published videos with limited user interactions. Surprisingly, our findings reveal that Mean Opinion Scores from previous video quality assessment datasets do not strongly correlate with video engagement levels. To address this, we introduce a substantial dataset comprising 90,000 real-world UGC short videos from Snapchat. Rather than relying on view count, average watch time, or rate of likes, we propose two metrics: normalized average watch percentage (NAWP) and engagement continuation rate (ECR) to describe the engagement levels of short videos. Comprehensive multi-modal features, including visual content, background music, and text data, are investigated to enhance engagement prediction. With the proposed dataset and two key metrics, our method demonstrates its ability to predict engagements of short videos purely from video content.

翻译：理解和建模社交媒体平台上用户生成内容（UGC）短视频的流行度，是一项对内容创作者和推荐系统具有广泛影响的关键挑战。本研究深入探讨了预测用户互动有限的新发布视频参与度的复杂性。令人惊讶的是，我们的研究结果表明，以往视频质量评估数据集中的平均意见分数与视频参与度水平并无强相关性。为解决此问题，我们引入了一个包含来自Snapchat的90,000个真实世界UGC短视频的大规模数据集。我们并未依赖观看次数、平均观看时长或点赞率，而是提出了两个指标：归一化平均观看百分比（NAWP）和参与持续率（ECR）来描述短视频的参与度水平。为提升参与度预测，我们研究了全面的多模态特征，包括视觉内容、背景音乐和文本数据。借助所提出的数据集和两个关键指标，我们的方法证明了其仅从视频内容即可预测短视频参与度的能力。