OmniTrend: Content-Context Modeling for Scalable Social Popularity Prediction

arXiv cs.CV / 4/30/2026

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Key Points

The paper argues that social media popularity prediction must account for both intrinsic content appeal and external exposure context, rather than relying mainly on content signals.
It introduces OmniTrend, which separates learning of content attractiveness from contextual exposure to prevent representations from absorbing platform-specific visibility biases.
OmniTrend uses a content module that learns cross-modal embeddings from visual, audio, and textual cues to quantify intrinsic appeal.
It also uses a context module that estimates exposure using signals like posting time, author activity, topical trends, and retrieval-based neighborhood statistics.
The framework combines separate predictors for attractiveness and exposure into a unified popularity estimate to improve interpretability and cross-platform transfer, especially across image and video settings.

Abstract

Predicting social media popularity requires understanding both the intrinsic appeal of content and the external context that determines how it is exposed to users. Existing methods focus on content signals but do not separate them from exposure-related patterns, which causes the learned representations to absorb platform-specific visibility effects and weakens both interpretability and cross-platform transfer. This paper introduces OmniTrend, a unified framework that models popularity as the joint outcome of content attractiveness and contextual exposure. The content module learns cross-modal representations from visual, audio, and textual cues to quantify intrinsic appeal, while the context module estimates exposure from exogenous signals such as posting time, author activity, topical trends, and retrieval-based neighborhood statistics. OmniTrend learns separate predictors for content attractiveness and contextual exposure and integrates them in the final popularity estimate, which makes the role of each factor explicit and supports robust transfer across image and video platforms.