Improving Social Media Popularity Prediction with Multiple Post Dependencies

TL;DR

This paper introduces a novel framework called Dependency-aware Sequence Network (DSN) that leverages both intra- and inter-post dependencies to improve social media popularity prediction accuracy, demonstrating superior performance on a large dataset.

Contribution

The paper proposes a new prediction framework that exploits multiple dependencies between social media posts, including intra-post content and inter-post relationships, using multimodal features, hierarchical propagation, and attention mechanisms.

Findings

DSN outperforms existing models on the Social Media Popularity Dataset.

Incorporating multiple post dependencies improves prediction accuracy.

Hierarchical and attention-based modules effectively capture content and temporal dependencies.

Abstract

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.