Social Media Popularity Prediction has drawn a lot of attention because of its profound impact on many different applications, such as recommendation systems and multimedia advertising. Despite recent efforts to leverage the content of social media posts to improve prediction accuracy, many existing models fail to fully exploit the multiple dependencies between posts, which are important to comprehensively extract content information from posts. To tackle this problem, we propose a novel prediction framework named Dependency-aware Sequence Network (DSN) that exploits both intra- and inter-post dependencies. For intra-post dependency, DSN adopts a multimodal feature extractor with an efficient fine-tuning strategy to obtain task-specific representations from images and textual information of posts. For inter-post dependency, DSN uses a hierarchical information propagation method to learn category representations that could better describe the difference between posts. DSN also exploits recurrent networks with a series of gating layers for more flexible local temporal processing abilities and multi-head attention for long-term dependencies. The experimental results on the Social Media Popularity Dataset demonstrate the superiority of our method compared to existing state-of-the-art models.

 提出了一种名为依赖感知序列网络(Dependency-aware Sequence Network, DSN)的新型预测框架，该框架充分利用了社交媒体帖子之间的多个依赖关系，通过多模态特征提取器和层级信息传播方法提高了预测准确度，并使用循环网络和多头注意力机制进行本地时间处理和长期依赖建模。在Social Media Popularity Dataset上的实验结果表明，与现有的最先进模型相比，本方法表现出更高的优越性。

利用多个帖子依赖性改进社交媒体知名度预测