Popularity Predictability Depends on Information Scale

Micro-video popularity prediction draws on information ranging from broad context to item content and historical references. However, existing methods largely ignore that information at different levels contributes differently to predictability, and instead collapse them into a single prediction. We propose Scale-Structured Predictability Modeling (SSPM), which treats popularity predictability as a function of information scale. SSPM organizes increasingly informative evidence into a nested prediction process, where each level explains only the variation that becomes predictable beyond the preceding one. (1) Context Prior Modeling establishes the predictable component supported by population or temporal context. (2) Multimodal Evidence Refinement identifies the additional variation explained by item-level multimodal evidence. (3) Historical Analogy Refinement further captures recurrent local variation shared with similar historical instances, using out-of-fold residuals to separate genuinely new information from what earlier levels have already explained. Together, these components form a progressive decomposition of predictability rather than three independent predictors. Experiments on three micro-video popularity benchmarks show that SSPM improves prediction performance by up to 19.77% in nMSE while revealing how predictive gains emerge across information scales. These results support our main finding: popularity predictability depends on information scale, and explicitly modeling this structure leads to both stronger prediction and better understanding of where predictive gains come from.