# Intrinsic Image Popularity Assessment

arXiv: https://arxiv.org/abs/1907.01985 · HTML: https://mlsanity.com/paper/1907.01985/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2019.

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

- [Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community](https://mlsanity.com/paper/2402.09872/) (2024)
- [Follow Us and Become Famous! Insights and Guidelines From Instagram Engagement Mechanisms](https://mlsanity.com/paper/2301.06815/) (2023)
- [Improving Social Media Popularity Prediction with Multiple Post Dependencies](https://mlsanity.com/paper/2307.15413/) (2023)
- [DFW-PP: Dynamic Feature Weighting based Popularity Prediction for Social Media Content](https://mlsanity.com/paper/2110.08510/) (2021)

## Abstract (arXiv)

The goal of research in automatic image popularity assessment (IPA) is to develop computational models that can accurately predict the potential of a social image to go viral on the Internet. Here, we aim to single out the contribution of visual content to image popularity, i.e., intrinsic image popularity. Specifically, we first describe a probabilistic method to generate massive popularity-discriminable image pairs, based on which the first large-scale image database for intrinsic IPA (I2PA) is established. We then develop computational models for I2PA based on deep neural networks, optimizing for ranking consistency with millions of popularity-discriminable image pairs. Experiments on Instagram and other social platforms demonstrate that the optimized model performs favorably against existing methods, exhibits reasonable generalizability on different databases, and even surpasses human-level performance on Instagram. In addition, we conduct a psychophysical experiment to analyze various aspects of human behavior in I2PA.