Authors : Omer Benjakob, Rona Aviram, Jonathan Sobel
With the COVID-19 pandemic’s outbreak at the beginning of 2020, millions across the world flocked to Wikipedia to read about the virus. Our study offers an in-depth analysis of the scientific backbone supporting Wikipedia’s COVID-19 articles.
Using references as a readout, we asked which sources informed Wikipedia’s growing pool of COVID-19-related articles during the pandemic’s first wave (January-May 2020). We found that coronavirus-related articles referenced trusted media sources and cited high-quality academic research.
Moreover, despite a surge in preprints, Wikipedia’s COVID-19 articles had a clear preference for open-access studies published in respected journals and made little use of non-peer-reviewed research up-loaded independently to academic servers.
Building a timeline of COVID-19 articles on Wikipedia from 2001-2020 revealed a nuanced trade-off between quality and timeliness, with a growth in COVID-19 article creation and citations, from both academic research and popular media.
It further revealed how preexisting articles on key topics related to the virus created a frame-work on Wikipedia for integrating new knowledge. This “scientific infrastructure” helped provide context, and regulated the influx of new information into Wikipedia.
Lastly, we constructed a network of DOI-Wikipedia articles, which showed the landscape of pandemic-related knowledge on Wikipedia and revealed how citations create a web of scientific knowledge to support coverage of scientific topics like COVID-19 vaccine development.
Understanding how scientific research interacts with the digital knowledge-sphere during the pandemic provides insight into how Wikipedia can facilitate access to science. It also sheds light on how Wikipedia successfully fended of disinformation on the COVID-19 and may provide insight into how its unique model may be deployed in other contexts.