  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.
