# Enhancing Social Media Post Popularity Prediction with Visual Content

arXiv: https://arxiv.org/abs/2405.02367 · HTML: https://mlsanity.com/paper/2405.02367/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 79 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [A Bayesian approach for predicting the popularity of tweets](https://mlsanity.com/paper/1304.6777/) (2013)
- [Predicting the Popularity of Micro-videos with Multimodal Variational Encoder-Decoder Framework](https://mlsanity.com/paper/2003.12724/) (2020)
- [Understanding Image Virality](https://mlsanity.com/paper/1503.02318/) (2015)
- [Predicting popularity of online videos using Support Vector Regression](https://mlsanity.com/paper/1510.06223/) (2015)
- [Predictability of Popularity: Gaps between Prediction and Understanding](https://mlsanity.com/paper/1603.09436/) (2016)
- [Multimodal Deep Learning Framework for Image Popularity Prediction on Social Media](https://mlsanity.com/paper/2105.08809/) (2021)
- [How to Become Instagram Famous: Post Popularity Prediction with Dual-Attention](https://mlsanity.com/paper/1809.09314/) (2018)
- [Cats and Captions vs. Creators and the Clock: Comparing Multimodal Content to Context in Predicting Relative Popularity](https://mlsanity.com/paper/1703.01725/) (2017)

## Abstract (arXiv)

Our study presents a framework for predicting image-based social media content popularity that focuses on addressing complex image information and a hierarchical data structure. We utilize the Google Cloud Vision API to effectively extract key image and color information from users' postings, achieving 6.8% higher accuracy compared to using non-image covariates alone. For prediction, we explore a wide range of prediction models, including Linear Mixed Model, Support Vector Regression, Multi-layer Perceptron, Random Forest, and XGBoost, with linear regression as the benchmark. Our comparative study demonstrates that models that are capable of capturing the underlying nonlinear interactions between covariates outperform other methods.