2019冠状病毒病信息流揭示了新的临界点流行病学和修订后的$R$公式
Covid-19 infodemic reveals new tipping point epidemiology and a revised $R$ formula
摘要：
许多国家的政府都通过一个简单的信息成功控制了COVID-19的爆发：保持有效的“$R$号码”$R<1$，以防止广泛传染并使曲线变平。这就提出了一个问题，即类似的政策是否可以控制危险的在线信息、错误信息和虚假信息。在这里，我们使用来自COVID-19信息流行病的多平台数据显示，其在线传播相反包含一个不同的动态机制，其中独立平台内和跨平台的社区和用户偶尔在类似的时间尺度上形成临时的活跃链接。这使得可能已经灭绝的物质得以进化甚至变异。这使得已经成功传播仇恨和反疫苗接种材料的利基网络迅速成为全球超级传播者，讲述虚假的COVID-19治疗，反亚洲情绪和阴谋论。我们获得了新的工具，将这些耦合的社会病毒动力学，包括在线$R$，以帮助防止在所有规模的信息流行病传播：从跨平台传播（例如Facebook，4Chan）到在给定的亚群，社区或主题内传播。通过考虑类似的社会和病毒时间尺度，同样的数学理论也提供了其他非常规感染特征的定量描述，如金融市场中的谣言传播和学校中的感冒传播。
英文摘要：
Many governments have managed to control their COVID-19 outbreak with a simple message: keep the effective '$R$ number' $R<1$ to prevent widespread contagion and flatten the curve. This raises the question whether a similar policy could control dangerous online 'infodemics' of information, misinformation and disinformation. Here we show, using multi-platform data from the COVID-19 infodemic, that its online spreading instead encompasses a different dynamical regime where communities and users within and across independent platforms, sporadically form temporary active links on similar timescales to the viral spreading. This allows material that might have died out, to evolve and even mutate. This has enabled niche networks that were already successfully spreading hate and anti-vaccination material, to rapidly become global super-spreaders of narratives featuring fake COVID-19 treatments, anti-Asian sentiment and conspiracy theories. We derive new tools that incorporate these coupled social-viral dynamics, including an online $R$, to help prevent infodemic spreading at all scales: from spreading across platforms (e.g. Facebook, 4Chan) to spreading within a given subpopulation, or community, or topic. By accounting for similar social and viral timescales, the same mathematical theory also offers a quantitative description of other unconventional infection profiles such as rumors spreading in financial markets and colds spreading in schools.
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。
