Clickbait vs. Quality: How Engagement-Based Optimization Shapes the
  Content Landscape in Online Platforms
Nicole Immorlica Meena Jagadeesan Brendan Lucier
Online content platforms commonly use engagement-based optimization when
making recommendations. This encourages content creators to invest in quality,
but also rewards gaming tricks such as clickbait. To understand the total
impact on the content landscape, we study a game between content creators
competing on the basis of engagement metrics and analyze the equilibrium
decisions about investment in quality and gaming. First, we show the content
created at equilibrium exhibits a positive correlation between quality and
gaming, and we empirically validate this finding on a Twitter dataset. Using
the equilibrium structure of the content landscape, we then examine the
downstream performance of engagement-based optimization along several axes.
Perhaps counterintuitively, the average quality of content consumed by users
can decrease at equilibrium as gaming tricks become more costly for content
creators to employ. Moreover, engagement-based optimization can perform worse
in terms of user utility than a baseline with random recommendations, and
engagement-based optimization is also suboptimal in terms of realized
engagement relative to quality-based optimization. Altogether, our results
highlight the need to consider content creator incentives when evaluating a
platform's choice of optimization metric.