Covid-19: Too much data — is it good or bad? Here’s what MIT study says

Since the beginning of the Covid-19 pandemic, numbers, graphs, charts, and data visualisation have helped authorities, health experts, and policymakers to understand the infection rates, pattern, deaths, etc and have on certain occasion helped them find out how wearing a mask of social distancing has affected these numbers. But a study from the Massachusetts Institute of Technology (MIT) has found that these data visualizations techniques are being used by coronavirus sceptics to argue against public health dictates.

The researchers have to search through thousands of social media posts found that these coronavirus sceptics use the same data to provide counter-visualisations rhetoric and suggest radically different policies while arguing with public health experts. The researchers, hence, concluded that graphs and data are not enough to explain the urgency of the situation as cynics can use the same data to establish a different belief system.

As data visualisation became a prominent part of spreading awareness early in the pandemic, PhD student and lead author of the paper, Crystal Lee and her fellow researchers started studying how these graphs are being used through all social media platforms. The initial understanding was more data led to better understanding and to test that belief, the team used a computational model on Twitter. The tweet referred to half a million tweets that referred to both Covid-19 and data and found the sceptics who are rallying against using masks too are creating and sharing data visualisations as much or even more to support their stand.

The study further finds that these “counter-visualisations” are most time quite sophisticated, made using official resources and virtually indistinguishable from the mainstream sources. In fact, their supportive arguments are ‘nuanced’, said Lee. They also discussed how the data was collected, how to download it and why

The study further said that the same division in belief runs through climate change and vaccination as well where data visualisation plays out varied dynamics in social media discussions.

The research will be presented at the ACM Conference on Human Factors in Computing Systems.