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.

在线内容平台通常使用基于参与度的优化来进行推荐，该研究分析了内容创作者之间基于参与度指标竞争的博弈游戏，并研究了投资于质量和采用作弊手段的平衡决策，结果发现在平衡状态下创作者创作的内容质量和作弊行为之间存在正相关关系。同时，该研究还扩展了基于参与度优化的下游性能，并表明相比于随机推荐的基准值，参与度优化在用户效用和实际用户参与度方面均可能表现更差，这些结果强调了在评估平台选择优化指标时需要考虑到内容创作者的激励问题。