Science used to be done by a select few in high-tech laboratories and dusty university offices. But this was before we had a pandemic on our handsby Becca Muir / June 14, 2020 / Leave a comment
Science used to be done by a select few in high-tech laboratories and dusty university offices. Studies would go on for years, and when they were published, results would be locked behind journal paywalls, ready to be read by a handful of fellow specialists. But this was before we had a pandemic on our hands.
Now, everyone from billionaire Elon Musk to your high school friend on Facebook is an “armchair epidemiologist.” Data, analyses and opinions on Covid-19 are flooding social media feeds and news sites. And why not? Even public health experts are struggling to make sense of this extremely unusual situation. This crisis affects everyone, so why not offer your views on how to dampen the R rate?
But, according to the World Health Organisation, we are fighting not only a pandemic; we are also fighting an infodemic. And, it’s much harder to identify fake news than previously thought. Most of the Covid-19 misinformation contains some truth and authority, blurring the boundaries between fact and fiction.
Rise of the data bros
Certain posts exacerbate the problem. In March, Aaron Ginn, a Silicon Valley product manager, published a Medium article titled “Evidence Over hysteria—Covid-19.” He argued that the world was in panic mode, and that this hysteria “is pushing aside our protections as individual citizens and permanently harming our free, tolerant, open civil society.” Facing backlash, the article was soon taken down from Medium and Ginn was chastised by public health experts on Twitter. Renowned epidemiologist Carl Bergstrom tore apart Ginn’s analysis in a thread of 30 tweets. Bad modelling can lead to unintentional misinformation, which is especially dangerous when the novice modeller has a big online platform.
This is far from a unique case. Mehmet Alpaslan, an astrophysicist and science communicator in New York, tweeted that “a significant majority of online bros” believe that they are as skilled as epidemiologists because they have taken statistic courses and learned how to code. These so-called “data bros” speak over experts and offer mathematically complicated, misinformed analysis.
“I haven’t really seen a single actual statistician come out with these absurd Medium essays about how epidemiologists are all wrong” Alpaslan told me through Twitter. Data science…