  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.
