# Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

arXiv: https://arxiv.org/abs/1712.04443 · HTML: https://mlsanity.com/paper/1712.04443/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2017.

## Built on by (most cited first, 12 of 12)

- [Skeleton-based Action Recognition through Contrasting Two-Stream Spatial-Temporal Networks](https://mlsanity.com/paper/2301.11495/) (2023)
- [Multimodal Deep Learning Framework for Image Popularity Prediction on Social Media](https://mlsanity.com/paper/2105.08809/) (2021)
- [SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge](https://mlsanity.com/paper/2405.10497/) (2024)
- [Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective](https://mlsanity.com/paper/2105.12918/) (2021)
- [Improving Social Media Popularity Prediction with Multiple Post Dependencies](https://mlsanity.com/paper/2307.15413/) (2023)
- [Contrastive Learning for Implicit Social Factors in Social Media Popularity Prediction](https://mlsanity.com/paper/2410.09345/) (2024)
- [Glance for Context: Learning When to Leverage LLMs for Node-Aware GNN-LLM Fusion](https://mlsanity.com/paper/2510.10849/) (2025)
- [Learning User Embeddings from Temporal Social Media Data: A Survey](https://mlsanity.com/paper/2105.07996/) (2021)
- [DFW-PP: Dynamic Feature Weighting based Popularity Prediction for Social Media Content](https://mlsanity.com/paper/2110.08510/) (2021)
- [On the Overlooked Significance of Underutilized Contextual Features in Recent News Recommendation Models](https://mlsanity.com/paper/2112.14370/) (2021)
- [On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction](https://mlsanity.com/paper/2409.16623/) (2024)
- [HyperFusion: Hierarchical Multimodal Ensemble Learning for Social Media Popularity Prediction](https://mlsanity.com/paper/2507.00926/) (2025)

## Abstract (arXiv)

Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i.e., sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal context and temporal attention into account. Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently learn two adaptive temporal contexts for sequential popularity. Finally, a novel temporal attention is designed to predict new popularity (the popularity of a new user-post pair) with temporal coherence across multiple time-scales. Experiments on our released image dataset with about 600K Flickr photos demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms, with an average of 21.51% relative performance improvement in the popularity prediction (Spearman Ranking Correlation).