Modality-Aligned Hierarchical Attention Network for Multi-Modal Popularity Prediction on Social Media

Social media popularity prediction is essential for content optimization and platform management. Existing approaches often struggle to capture the intricate semantic relationships among heterogeneous content modalities. To solve this problem, we propose a hierarchical attention fusion framework with cross-modal semantic alignment, which integrates text, visual, and user behavior features for enhanced popularity prediction. This design enables the model to adaptively emphasize the most informative features across modalities. We systematically evaluate various regression models and their ensemble strategies on the SMPD dataset, which contains 486,000 social media posts. Experimental results demonstrate that our hierarchical attention fusion consistently outperforms existing fusion methods. These findings highlight the effectiveness of cross-modal semantic alignment and provide valuable insights for advancing multi-modal social media popularity prediction.