为真相设计政策：以透明度和信息设计对抗错误信息
Designing Policies for Truth: Combating Misinformation with Transparency and Information Design
AI总结：
本文提出贝叶斯说服分支过程模型，研究在线社交平台如何通过信息设计预防错误信息传播，并证明完全透明的打标签政策是平台最优策略，能激励内容创作者减少错误信息。
摘要：
错误信息已成为在线社交平台（OSP）上日益严重的问题，尤其是在选举或疫情期间。为应对这一问题，OSP 已实施各种政策，例如打标签，以通知用户可能具有误导性的信息。然而，这些政策通常是透明的，因此容易被内容创作者利用，后者可能不愿投入精力制作真实内容，从而导致错误信息的病毒式传播。与减轻现有错误信息传播范围的做法不同，本工作聚焦于一种预防性解决方案，旨在阻止错误信息在获得传播势头之前扩散。我们提出了一种贝叶斯说服分支过程（$\operatorname{BP}^2$）来建模 OSP、内容创作者和用户之间的策略性交互。OSP 上的错误信息传播通过多类型分支过程建模，其中用户的正面和负面评论会影响错误信息的传播。利用由贝叶斯可信性导出的拉格朗日函数，我们在完美贝叶斯均衡下刻画了 OSP 的最优政策。该拉格朗日函数的凸性意味着 OSP 的最优政策就是完全信息化的打标签政策：向用户揭示内容的准确性。尽管 OSP 对内容创作者没有直接控制权，但这种打标签政策能够促使内容创作者在减少错误信息方面付出最大努力。我们通过数值模拟验证了上述发现。
英文摘要：
Misinformation has become a growing issue on online social platforms (OSPs), especially during elections or pandemics. To combat this, OSPs have implemented various policies, such as tagging, to notify users about potentially misleading information. However, these policies are often transparent and therefore susceptible to being exploited by content creators, who may not be willing to invest effort into producing authentic content, causing the viral spread of misinformation. Instead of mitigating the reach of existing misinformation, this work focuses on a solution of prevention, aiming to stop the spread of misinformation before it has a chance to gain momentum. We propose a Bayesian persuaded branching process ($\operatorname{BP}^2$) to model the strategic interactions among the OSP, the content creator, and the user. The misinformation spread on OSP is modeled by a multi-type branching process, where users' positive and negative comments influence the misinformation spreading. Using a Lagrangian induced by Bayesian plausibility, we characterize the OSP's optimal policy under the perfect Bayesian equilibrium. The convexity of the Lagrangian implies that the OSP's optimal policy is simply the fully informative tagging policy: revealing the content's accuracy to the user. Such a tagging policy solicits the best effort from the content creator in reducing misinformation, even though the OSP exerts no direct control over the content creator. We corroborate our findings using numerical simulations.
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