SMTPD: A New Benchmark for Temporal Prediction of Social Media Popularity
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917947092500480 |
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| author | Xu, Yijie Zheng, Bolun Zhu, Wei Pan, Hangjia Yao, Yuchen Xu, Ning Liu, Anan Zhang, Quan Yan, Chenggang |
| author_facet | Xu, Yijie Zheng, Bolun Zhu, Wei Pan, Hangjia Yao, Yuchen Xu, Ning Liu, Anan Zhang, Quan Yan, Chenggang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04446 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SMTPD: A New Benchmark for Temporal Prediction of Social Media Popularity Xu, Yijie Zheng, Bolun Zhu, Wei Pan, Hangjia Yao, Yuchen Xu, Ning Liu, Anan Zhang, Quan Yan, Chenggang Social and Information Networks Multimedia 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. |
| title | SMTPD: A New Benchmark for Temporal Prediction of Social Media Popularity |
| topic | Social and Information Networks Multimedia |
| url | https://arxiv.org/abs/2503.04446 |