点击诱饵与质量：基于参与度的优化如何塑造在线平台的内容生态
Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms
AI总结：
本文通过博弈模型与Twitter数据实证，研究基于参与度的推荐优化如何影响内容创作者的质量投入与投机行为，揭示其可能导致平均质量下降、用户效用受损及实际参与度次优，强调平台选择优化指标须考虑创作者激励。
摘要：
在线内容平台在做出推荐时通常采用基于参与度的优化。这鼓励内容创作者投资于质量，但同时也奖励点击诱饵等投机取巧手段。为了理解对内容生态的整体影响，我们研究了一个内容创作者基于参与度指标进行竞争博弈的模型，并分析其在质量投资与投机行为上的均衡决策。首先，我们证明在均衡状态下产生的内容在质量与投机行为之间呈现正相关关系，并在一个Twitter数据集上实证验证了这一发现。利用内容生态的均衡结构，我们随后从多个维度考察了基于参与度优化的下游表现。或许与直觉相反，当投机手段对内容创作者而言成本更高时，用户消费内容的平均质量在均衡状态下可能会下降。此外，基于参与度的优化在用户效用方面可能比随机推荐的基线表现更差，并且在实现的实际参与度方面也劣于基于质量的优化。总而言之，我们的结果凸显了在评估平台对优化指标的选择时，需要考虑内容创作者激励的必要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
