# Quantifying attention via dwell time and engagement in a social media browsing environment

arXiv: https://arxiv.org/abs/2209.10464 · HTML: https://mlsanity.com/paper/2209.10464/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2022.

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

- [Art and the science of generative AI: A deeper dive](https://mlsanity.com/paper/2306.04141/) (2023)
- [A Computational Model of Message Sensation Value in Short Video Multimodal Features that Predicts Sensory and Behavioral Engagement](https://mlsanity.com/paper/2604.19995/) (2026)
- [Reframing AI Loss of Control: What Control Is, How to Have It, How to Lose It](https://mlsanity.com/paper/2606.12442/) (2026)
- [AI Should Facilitate Democratic Deliberation at Scale](https://mlsanity.com/paper/2609.20059/) (2026)

## Abstract (arXiv)

Modern computational systems have an unprecedented ability to detect, leverage and influence human attention. Prior work identified user engagement and dwell time as two key metrics of attention in digital environments, but these metrics have yet to be integrated into a unified model that can advance the theory andpractice of digital attention. We draw on work from cognitive science, digital advertising, and AI to propose a two-stage model of attention for social media environments that disentangles engagement and dwell. In an online experiment, we show that attention operates differently in these two stages and find clear evidence of dissociation: when dwelling on posts (Stage 1), users attend more to sensational than credible content, but when deciding whether to engage with content (Stage 2), users attend more to credible than sensational content. These findings have implications for the design and development of computational systems that measure and model human attention, such as newsfeed algorithms on social media.