Micro-video popularity prediction (MVPP) forecasts the popularity a newly uploaded short-form video will attract within a fixed number of days after upload. This task supports downstream applications in recommendation, advertising, and creator analytics, yet the problem is hard since virality depends on external trends rather than video content alone. Prior MVPP methods incorporate context by retrieving similar videos from platform-internal corpora, however historical neighbors cannot reveal whether a topic is currently trending, controversial, or already saturated across the open web. To this end, we reformulate MVPP as open-web grounded prediction and introduce WEBSHORTS, the first micro-video dataset that couples 14K videos with real-time open-web context collected at upload time, alongside daily view counts tracked over 7 days. The context for each video is organized as a structured evidence-card that captures the external attention landscape along three complementary web-context dimensions. We further propose SHORTS-CAST, a framework that generates dimension-wise rationales from the evidence-card to guide popularity regression, then adapts at deployment by selectively updating the context-to-popularity mapping when delayed labels reveal genuine trend shifts. In our experiments, SHORTS-CAST consistently outperforms content-only, video corpus retrieval-augmented, and online adaptation baselines under both offline and delayed-label online protocols, confirming that structured web context and trend-aware adaptation are jointly necessary for popularity forecasting under realistic deployment constraints in fast-evolving short-form video ecosystems.

翻译：微视频流行度预测（MVPP）旨在预测新上传的短视频在上传后固定天数内所能吸引的流行度。该任务支持推荐、广告和创作者分析等下游应用，然而此问题颇具挑战性，因为病毒式传播更多地依赖于外部趋势而非视频内容本身。先前的MVPP方法通过从平台内部语料库中检索相似视频来融入上下文信息，但历史邻近项无法揭示某一话题在当前开放网络中是否正流行、存在争议或已经饱和。为此，我们将MVPP重新定义为基于开放网络的预测，并提出了WEBSHORTS——首个将14K视频与上传时收集的实时开放网络上下文以及7天内每日观看次数追踪数据相结合的微视频数据集。每个视频的上下文被组织为结构化的证据卡，从三个互补的网络上下文维度捕捉外部关注景观。我们进一步提出了SHORTS-CAST框架，该框架从证据卡生成各维度的理由以指导流行度回归，然后在部署时通过选择性更新上下文到流行度的映射来适应延迟标签所揭示的真实趋势变化。在我们的实验中，SHORTS-CAST在离线协议和延迟标签在线协议下均始终优于仅基于内容、基于视频语料库检索增强以及在线自适应基线，证实了在快速演变的短视频生态系统中，面对现实部署约束下的流行度预测，结构化网络上下文和趋势自适应协同不可或缺。