Crisis events are inherently times of uncertainty and anxiety, and those conditions give rise to collective sensemaking. This communal form of comprehension has benefits, but it also makes us vulnerable to the spread of rumors and misinformation. Unfortunately, the COVID-19 pandemic is taking place at a time when we are already struggling with pervasive mis- and disinformation and diminished trust in our institutions. This project aimed to increase our understanding of how scientific knowledge, expertise, data, and communication affect the spread and correction of online misinformation about an emerging pandemic. It focused on how claims and statistics related to scientific knowledge and/or expertise shape—and are shaped by—these information and influence dynamics. It also made contributions by expanding and extending socio-technical infrastructure for collecting and analyzing social media data in a time- and safety-critical information environment and developing techniques for aggregating variable statistics, moderated by scientific expertise, over large-scale social media data. 

To increase understanding of COVID-related misinformation, we conducted two primary empirical studies. In the first, we explored how social media users invoke science to either support or oppose salient scientific conversations online, and in this case we explore discussion about the efficacy of widespread wearing of  masks. We analyzed the sources that these users cite to support their claims and the political communities that users align with. We found that as social media users construct consensus they both rely on references to scientific material and argue against, or denigrate, scientific material used by one’s opponents. We also find notable differences in how groups use recent research published in high-quality journals, versus literature that includes articles retracted, corrected, outdated, not peer-reviewed, and since publicly contradicted by their authors. In a second empirical case study, we looked at the use of COVID-related statistics, specifically R-values, on social media. Our analyses looked at the rates of usage and context of use of these statistics among experts and non-experts. Interestingly, use of these sophisticated and technical statistics is not restricted to subject matter experts but rather used by experts and non-experts alike. 

In the course of the project activities, graduate and undergraduate students were able to gain valuable research and educational skills. Students participated in the research through research assistantships and for-credit research groups. More than half of the participating students have been from groups underrepresented in STEM.

Last Modified: 02/14/2022
Modified by: Emma Spiro