分离内容吸引力与传播上下文，提升跨平台流行度预测能力
OmniTrend: Content-Context Modeling for Scalable Social Popularity Prediction
- 将内容与传播上下文分开建模，避免平台曝光偏差
- 融合多模态内容特征与外生暴露信号，预测更准确
- 适合需要可解释性与跨平台迁移的社交推荐场景
预测社交媒体流行度需同时理解内容本身的吸引力和影响其触达用户的外部上下文。现有方法侧重内容信号，但未区分内容与曝光模式，导致学习到的表征混入平台特定可见性效应，削弱可解释性与跨平台迁移能力。本文提出OmniTrend，一种统一框架，将流行度建模为内容吸引力与上下文暴露的联合结果。内容模块从视觉、音频和文本线索中学习跨模态表示以量化内在吸引力；上下文模块则基于发布时间、作者活跃度、话题趋势及检索邻域统计等外生信号估计曝光程度。OmniTrend分别学习内容吸引力与上下文暴露的预测器，并在最终流行度估计中整合，使各因素作用明确，支持在图像与视频平台间的稳健迁移。
原文摘要 · Abstract (English)
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.
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