# Predicting popularity of online videos using Support Vector Regression

arXiv: https://arxiv.org/abs/1510.06223 · HTML: https://mlsanity.com/paper/1510.06223/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2015.

## Built on by (most cited first, 7 of 7)

- [Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks](https://mlsanity.com/paper/2207.00755/) (2022)
- [Generating Robust Adversarial Examples against Online Social Networks (OSNs)](https://mlsanity.com/paper/2310.12708/) (2023)
- [Will You Ever Become Popular? Learning to Predict Virality of Dance Clips](https://mlsanity.com/paper/2111.03819/) (2021)
- [An Estimation of Online Video User Engagement from Features of Continuous Emotions](https://mlsanity.com/paper/2105.01633/) (2021)
- [YouTube and Science: Models for Research Impact](https://mlsanity.com/paper/2209.02380/) (2022)
- [OmniTrend: Content-Context Modeling for Scalable Social Popularity Prediction](https://mlsanity.com/paper/2604.26252/) (2026)
- [Enhancing Social Media Post Popularity Prediction with Visual Content](https://mlsanity.com/paper/2405.02367/) (2024)

## Abstract (arXiv)

In this work, we propose a regression method to predict the popularity of an online video based on temporal and visual cues. Our method uses Support Vector Regression with Gaussian Radial Basis Functions. We show that modelling popularity patterns with this approach provides higher and more stable prediction results, mainly thanks to the non-linearity character of the proposed method as well as its resistance against overfitting. We compare our method with the state of the art on datasets containing over 14,000 videos from YouTube and Facebook. Furthermore, we show that results obtained relying only on the early distribution patterns, can be improved by adding social and visual metadata.