Contrastive Learning for Implicit Social Factors in Social Media Popularity Prediction

TL;DR

This paper introduces a contrastive learning approach to model implicit social factors influencing social media post popularity, demonstrating improved prediction accuracy by incorporating user behavior and social context.

Contribution

It proposes three novel implicit social factors and models them using contrastive learning with tailored encoders, enhancing popularity prediction beyond content-based methods.

Findings

Contrastive learning improves popularity prediction accuracy.

Implicit social factors significantly influence post popularity.

The method outperforms existing content-focused approaches.

Abstract

On social media sharing platforms, some posts are inherently destined for popularity. Therefore, understanding the reasons behind this phenomenon and predicting popularity before post publication holds significant practical value. The previous work predominantly focuses on enhancing post content extraction for better prediction results. However, certain factors introduced by social platforms also impact post popularity, which has not been extensively studied. For instance, users are more likely to engage with posts from individuals they follow, potentially influencing the popularity of these posts. We term these factors, unrelated to the explicit attractiveness of content, as implicit social factors. Through the analysis of users' post browsing behavior (also validated in public datasets), we propose three implicit social factors related to popularity, including content relevance, user influence similarity, and user identity. To model the proposed social factors, we introduce three supervised contrastive learning tasks. For different task objectives and data types, we assign them to different encoders and control their gradient flows to achieve joint optimization. We also design corresponding sampling and augmentation algorithms to improve the effectiveness of contrastive learning. Extensive experiments on the Social Media Popularity Dataset validate the superiority of our proposed method and also confirm the important role of implicit social factors in popularity prediction. We open source the code at https://github.com/Daisy-zzz/PPCL.git.