Analysis and Modelling of the Dissemination and Social Impacts of Scientific Preprints during Crises
Photo by Scott Graham on Unsplash
Duration: 02/2023–02/2024
Under normal circumstances, cutting-edge research is of little interest to the general public. However, during the COVID-19 crisis, the media began to report on results shared in non-peer-reviewed preprints, and laypeople began to read, share, and comment on them via social media. Combining approaches from data science, machine learning, digital humanities, and history, this project aims to investigate the mechanisms of information flow in this particular crisis and compare it to historical crises. The ultimate goal is to develop a general methodology that can guide the processing of non-peer-reviewed scientific information and inform the communication of scientific information to the public and policymakers during crises.
People
- Michael Piotrowski (Co-PI, UNIL)
- Daniel Probst (Co-PI, EPFL)
- Anaïs Haget (EPFL)
- Thalie Schmidt (UNIL)