SMPV: Social Media Prediction for Videos

摘要

With the explosive growth of video-centric social media platforms, understanding and predicting video popularity has become a crucial problem in both academia and industry. This year, the Social Media Prediction (SMP) Challenge expands its scope by introducing a dedicated video track, shifting the focus from static images to dynamic, multimodal video content. We introduce the Social Media Prediction for Videos (SMPV) task and release a large-scale, multimodal benchmark dataset, SMPD-Video, with more than 6K short-form videos, including vision language metadata, user profiles, and popularity labels. This challenge invites global researchers to develop predictive algorithms that integrate spatial-temporal dynamics, multimodal learning, and user-video interactions to forecast video popularity in real-world social temporal streams. With the participation and contribution of top teams around the world, the challenge has seen continuous performance improvements in recent years, driven by technological advancements. SMP Challenge Homepage: www.smp-challenge.com.