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.

翻译：社交媒体流行度预测因其在推荐系统和多媒体广告等诸多应用中的深远影响而受到广泛关注。尽管近期研究尝试利用社交媒体帖子的内容来提高预测精度，但许多现有模型未能充分挖掘帖子之间的多重依赖关系，而这些依赖关系对于全面提取帖子内容信息至关重要。为解决这一问题，我们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，该框架同时利用帖子内部和帖子间的依赖关系。对于帖子内部依赖，DSN采用多模态特征提取器，并结合高效的微调策略，从帖子的图像和文本信息中获取任务特定表示。对于帖子间依赖，DSN使用层次化信息传播方法学习能够更好描述帖子差异的类别表示。DSN还利用带有一系列门控层的循环网络来增强局部时间处理的灵活性，并通过多头注意力机制捕获长期依赖关系。在社交媒体流行度数据集上的实验结果表明，与现有最先进模型相比，我们的方法具有优越性。