Misinformation undermines trust in the integrity of democratic elections, the safety of vaccines, and the authenticity of footage from war zones. Social scientists have proposed many solutions to reduce individuals' demand for fake news, but it is unclear how to evaluate them. Efficacy can mean that an intervention increases (the ability to distinguish true from false content), works over a delay, scales up, and engages users. I argue that experts should also consider differences in exposure prevalence before declaring success. Misleading content makes up a small fraction of the average person's news diet, but some groups are at increased risk – conservatives and older adults see and share the most fake news. Targeting the whole population () could concentrate benefits among the users who already see the least misinformation to begin with. In complement to these approaches, we should design interventions for the people who need them most – conservatives and older adults (), as well as users who have already shared low-quality news ().

中文翻译：

---

打击最弱势用户中的错误信息


错误信息破坏了人们对民主选举公正性、疫苗安全性以及战区录像真实性的信任。社会科学家提出了许多解决方案来减少个人对假新闻的需求，但目前尚不清楚如何评估它们。有效性可能意味着干预措施的增强（区分真实内容和虚假内容的能力）、在延迟的情况下发挥作用、扩大规模并吸引用户。我认为专家们在宣布成功之前还应该考虑暴露率的差异。误导性内容只占普通人新闻饮食的一小部分，但某些群体面临的风险更大——保守派和老年人看到并分享的假新闻最多。针对整个人群（）可以将利益集中在那些一开始就看到最少错误信息的用户身上。作为这些方法的补充，我们应该为最需要的人设计干预措施——保守派和老年人（），以及已经分享过低质量新闻的用户（）。