# A Deep Learning Framework for Visual Attention Prediction and Analysis of News Interfaces

arXiv: https://arxiv.org/abs/2503.17212 · HTML: https://mlsanity.com/paper/2503.17212/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 21 Mar 2025.

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

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation](https://mlsanity.com/paper/1911.11834/) (2019)
- [The Secrets of Salient Object Segmentation](https://mlsanity.com/paper/1406.2807/) (2014)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Eye Tracking for Everyone](https://mlsanity.com/paper/1606.05814/) (2016)
- [DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling](https://mlsanity.com/paper/2105.12441/) (2021)
- [Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects](https://mlsanity.com/paper/1803.05082/) (2018)
- [Understanding Visual Saliency in Mobile User Interfaces](https://mlsanity.com/paper/2101.09176/) (2021)
- [Relative Saliency and Ranking: Models, Metrics, Data, and Benchmarks](https://mlsanity.com/paper/1810.02426/) (2018)
- [Saliency Prediction for Mobile User Interfaces](https://mlsanity.com/paper/1711.03726/) (2017)
- [AI as a Tool for Fair Journalism: Case Studies from Malta](https://mlsanity.com/paper/2407.15316/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Hybrid Deterministic Framework for Named Entity Extraction in Broadcast News Video](https://mlsanity.com/paper/2602.09154/) (2026)

## Abstract (arXiv)

News outlets' competition for attention in news interfaces has highlighted the need for demographically-aware saliency prediction models. Despite recent advancements in saliency detection applied to user interfaces (UI), existing datasets are limited in size and demographic representation. We present a deep learning framework that enhances the SaRa (Saliency Ranking) model with DeepGaze IIE, improving Salient Object Ranking (SOR) performance by 10.7%. Our framework optimizes three key components: saliency map generation, grid segment scoring, and map normalization. Through a two-fold experiment using eye-tracking (30 participants) and mouse-tracking (375 participants aged 13--70), we analyze attention patterns across demographic groups. Statistical analysis reveals significant age-based variations (p < 0.05, ε2 = 0.042), with older users (36--70) engaging more with textual content and younger users (13--35) interacting more with images. Mouse-tracking data closely approximates eye-tracking behavior (sAUC = 0.86) and identifies UI elements that immediately stand out, validating its use in large-scale studies. We conclude that saliency studies should prioritize gathering data from a larger, demographically representative sample and report exact demographic distributions.