Wikipedia is one of the main sources of free knowledge on the Web. During the first few months of the pandemic, over 5,200 new Wikipedia pages on COVID-19 have been created and have accumulated over 400M pageviews by mid June 2020. At the same time, an unprecedented amount of scientific articles on COVID-19 and the ongoing pandemic have been published online. Wikipedia's contents are based on reliable sources such as scientific literature. Given its public function, it is crucial for Wikipedia to rely on representative and reliable scientific results, especially so in a time of crisis. We assess the coverage of COVID-19-related research in Wikipedia via citations to a corpus of over 160,000 articles. We find that Wikipedia editors are integrating new research at a fast pace, and have cited close to 2% of the COVID-19 literature under consideration. While doing so, they are able to provide a representative coverage of COVID-19-related research. We show that all the main topics discussed in this literature are proportionally represented from Wikipedia, after accounting for article-level effects. We further use regression analyses to model citations from Wikipedia and show that Wikipedia editors on average rely on literature which is highly cited, widely shared on social media, and has been peer-reviewed.

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维基百科中的COVID-19研究

维基百科是网络上免费知识的主要来源之一。在大流行的前几个月中，在COVID-19上创建了超过5,200个新的Wikipedia页面，到2020年6月中旬，累计浏览量超过4亿次。与此同时，关于COVID-19的科学文章数量空前，大流行已在线发布。维基百科的内容基于可靠的资源，例如科学文献。鉴于其公共功能，维基百科必须依靠具有代表性的可靠科学结果，尤其是在危机时期。我们通过引用超过160,000篇文章的文献集来评估Wikipedia中与COVID-19相关的研究的覆盖范围。我们发现Wikipedia编辑人员正在迅速整合新研究，并引用了将近2％的COVID-19文献​​。在这样做的同时，他们能够提供与COVID-19相关的研究的代表性报道。我们表明，在考虑了文章级影响之后，维基百科中按比例代表了该文献中讨论的所有主要主题。我们进一步使用回归分析对Wikipedia的引用进行建模，并表明Wikipedia编辑平均而言依赖于被高度引用，在社交媒体上广泛共享并经过同行评审的文献。