# Forecasting Popularity of Videos using Social Media

arXiv: https://arxiv.org/abs/1403.5603 · HTML: https://mlsanity.com/paper/1403.5603/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2014.

## Built on by (most cited first, 4 of 4)

- [Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks](https://mlsanity.com/paper/2207.00755/) (2022)
- [M3TR: Temporal Retrieval Enhanced Multi-Modal Micro-video Popularity Prediction](https://mlsanity.com/paper/2411.15455/) (2024)
- [OmniTrend: Content-Context Modeling for Scalable Social Popularity Prediction](https://mlsanity.com/paper/2604.26252/) (2026)
- [ColoristaNet for Photorealistic Video Style Transfer](https://mlsanity.com/paper/2212.09247/) (2022)

## Abstract (arXiv)

This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media. Social-Forecast explicitly considers the dynamically changing and evolving propagation patterns of videos in social media when making popularity forecasts, thereby being situation and context aware. Social-Forecast aims to maximize the forecast reward, which is defined as a tradeoff between the popularity prediction accuracy and the timeliness with which a prediction is issued. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the prediction performance loss of Social-Forecast as compared to that obtained by an omniscient oracle and prove that the bound is sublinear in the number of video arrivals, thereby guaranteeing its short-term performance as well as its asymptotic convergence to the optimal performance. In addition, we conduct extensive experiments using real-world data traces collected from the videos shared in RenRen, one of the largest online social networks in China. These experiments show that our proposed method outperforms existing view-based approaches for popularity prediction (which are not context-aware) by more than 30% in terms of prediction rewards.