Social media popularity prediction task aims to predict the popularity of posts on social media platforms, which has a positive driving effect on application scenarios such as content optimization, digital marketing and online advertising. Though many studies have made significant progress, few of them pay much attention to the integration between popularity prediction with temporal alignment. In this paper, with exploring YouTube's multilingual and multi-modal content, we construct a new social media temporal popularity prediction benchmark, namely SMTPD, and suggest a baseline framework for temporal popularity prediction. Through data analysis and experiments, we verify that temporal alignment and early popularity play crucial roles in social media popularity prediction for not only deepening the understanding of temporal dynamics of popularity in social media but also offering a suggestion about developing more effective prediction models in this field. Code is available at https://github.com/zhuwei321/SMTPD.

翻译：社交媒体流行度预测任务旨在预测社交媒体平台上帖子的流行程度，该任务对内容优化、数字营销和在线广告等应用场景具有积极的推动作用。尽管已有许多研究取得了显著进展，但其中很少关注流行度预测与时序对齐的整合。本文通过探索YouTube的多语言与多模态内容，构建了一个新的社交媒体时序流行度预测基准，即SMTPD，并提出了一个时序流行度预测的基线框架。通过数据分析和实验，我们验证了时序对齐和早期流行度在社交媒体流行度预测中的关键作用，这不仅深化了对社交媒体流行度时序动态的理解，也为开发该领域更有效的预测模型提供了参考。代码发布于https://github.com/zhuwei321/SMTPD。