算法注意力与社交媒体平台上的内容创作
Algorithmic Attention and Content Creation on Social Media Platforms
- SC Johnson Graduate School of Management, Cornell University（康奈尔大学约翰逊管理学院）
- Department of Economics, University of North Carolina（北卡罗来纳大学经济学系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过两边模型分析广告资助社交媒体平台的注意力分配，发现最优推荐组合利用网络外部性，针对性排除低能力创作者并补贴高能力创作者，影响内容多样性与监管。
AI中文摘要：
我们研究了由推荐算法管理的广告资助社交媒体平台上收益最大化的注意力分配问题。注意力是昂贵的，可以通过广告变现，或分配给创作者以增加其曝光度，从而在变现与生产激励之间形成权衡。在一个具有异质观众和创作者且存在私人信息的两边模型中，最优推荐组合包括对某些观众事后次优的内容，以利用网络外部性。这些扭曲具有针对性：低能力创作者被排除，而高能力创作者通过曝光或货币支付获得补贴。两边互补性重塑了内容的多样性和质量，这对个性化监管和广告市场具有影响。
英文摘要：
We study revenue-maximizing attention allocation on an ad-funded social media platform governed by recommendation algorithms. Attention is costly and can be monetized through advertising or allocated to increase creators' exposure, creating a trade-off between monetization and production incentives. In a two-sided model with heterogeneous viewers and creators under private information, the optimal recommendation mix includes content that is ex post suboptimal for some viewers to leverage network externalities. These distortions are targeted: low-ability creators are excluded, while high-ability creators are subsidized through exposure or monetary payments. Two-sided complementarities reshape content variety and quality, with implications for personalization regulation and advertising markets.
