A Deep Learning Framework for Visual Attention Prediction and Analysis of News Interfaces
Matthew Kenely, Dylan Seychell, Carl James Debono, Chris Porter · 2025
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 ($\mathbf{3 0}$participants) and mousetracking ($\mathbf{3 7 5}$participants aged 13-70), we analyze attention patterns across demographic groups. Statistical analysis reveals significant age-based variations ($\mathbf{p}<0.05, \epsilon^{2}=0.042$), 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.