Title: HyperFusion: Hierarchical Multimodal Ensemble Learning for Social Media Popularity Prediction Show Chinese title

Title: 超融合：面向社交媒体受欢迎程度预测的分层多模态集成学习

Abstract: Social media popularity prediction plays a crucial role in content optimization, marketing strategies, and user engagement enhancement across digital platforms. However, predicting post popularity remains challenging due to the complex interplay between visual, textual, temporal, and user behavioral factors. This paper presents HyperFusion, a hierarchical multimodal ensemble learning framework for social media popularity prediction. Our approach employs a three-tier fusion architecture that progressively integrates features across abstraction levels: visual representations from CLIP encoders, textual embeddings from transformer models, and temporal-spatial metadata with user characteristics. The framework implements a hierarchical ensemble strategy combining CatBoost, TabNet, and custom multi-layer perceptrons. To address limited labeled data, we propose a two-stage training methodology with pseudo-labeling and iterative refinement. We introduce novel cross-modal similarity measures and hierarchical clustering features that capture inter-modal dependencies. Experimental results demonstrate that HyperFusion achieves competitive performance on the SMP challenge dataset. Our team achieved third place in the SMP Challenge 2025 (Image Track). The source code is available at https://anonymous.4open.science/r/SMPDImage. Show Chinese abstract

Abstract: 社交媒体流行度预测在内容优化、营销策略和用户参与度提升方面在数字平台上起着至关重要的作用。 然而，由于视觉、文本、时间和用户行为因素之间的复杂相互作用，预测帖子的流行度仍然具有挑战性。 本文提出了HyperFusion，这是一个用于社交媒体流行度预测的分层多模态集成学习框架。 我们的方法采用三层融合架构，逐步整合不同抽象层次的特征：从CLIP编码器获得的视觉表示，从Transformer模型获得的文本嵌入，以及结合用户特征的时间空间元数据。 该框架实施了一种分层集成策略，结合了CatBoost、TabNet和自定义的多层感知机。 为了解决标记数据有限的问题，我们提出了一种两阶段训练方法，包括伪标签和迭代优化。 我们引入了新颖的跨模态相似性度量和分层聚类特征，以捕捉模态间的依赖关系。 实验结果表明，HyperFusion在SMP挑战数据集上取得了有竞争力的性能。 我们的团队在SMP Challenge 2025（图像赛道）中获得了第三名。 源代码可在https://anonymous.4open.science/r/SMPDImage获取。