Misinformation online poses a range of threats, from subverting democratic processes to undermining public health measures. Proposed solutions range from encouraging more selective sharing by individuals to removing false content and accounts that create or promote it. Here we provide a framework to evaluate interventions aimed at reducing viral misinformation online both in isolation and when used in combination. We begin by deriving a generative model of viral misinformation spread, inspired by research on infectious disease. By applying this model to a large corpus (10.5 million tweets) of misinformation events that occurred during the 2020 US election, we reveal that commonly proposed interventions are unlikely to be effective in isolation. However, our framework demonstrates that a combined approach can achieve a substantial reduction in the prevalence of misinformation. Our results highlight a practical path forward as misinformation online continues to threaten vaccination efforts, equity and democratic processes around the globe.

网上的错误信息构成了一系列威胁，从颠覆民主进程到破坏公共卫生措施。提议的解决方案范围从鼓励个人更有选择性地分享到删除虚假内容和创建或推广虚假内容的帐户。在这里，我们提供了一个框架来评估旨在减少在线病毒错误信息的干预措施，无论是单独使用还是结合使用。受传染病研究的启发，我们首先推导了病毒错误信息传播的生成模型。通过将该模型应用于 2020 年美国大选期间发生的错误信息事件的大型语料库（1050 万条推文），我们发现通常提议的干预措施不可能单独有效。然而，我们的框架表明，综合方法可以大大减少错误信息的流行。我们的结果突出了一条切实可行的前进道路，因为网上的错误信息继续威胁着全球的疫苗接种工作、公平和民主进程。