A new study that employed artificial intelligence to analyse tweets related to coronavirus has revealed that public attitude toward Covid-19 and its treatments are more “infectious” than the disease itself.

The researchers studied the influence of Twitter on Covid-19 health beliefs. They also looked at the competing influence of scientific evidence versus the speeches of politicians.

The findings of the study suggested that people’s biases are magnified when they read tweets about Covid-19 from other users. The more times a particular tweet has been retweeted, the more they tend to believe it and retweet it themselves.

Scientific events, such as scientific publications, and non-scientific events, such as speeches of politicians, equally influence health belief trends on social media, the study observed.

"In the pandemic, social media has contributed to much of the information and misinformation and bias of the public's attitude toward the disease, treatment, and policy," said corresponding study author Yuan Luo, chief Artificial Intelligence officer at the Institute for Augmented Intelligence in Medicine at Northwestern University Feinberg School of Medicine.

He added: "The study sends an ‘alert’ to the audience that the information they encounter daily might be right or wrong and guide them to pick the information endorsed by solid scientific evidence. We also wanted to provide useful insights for scientists or health care providers, so that they can more effectively broadcast their voice to targeted audiences."

Inaccurate information from politicians

"Politicians may talk inaccurately about a certain treatment's effectiveness or say that Covid-19 is no big deal; it's just like the flu. As a scientist, you need to be aware that you need to get the science out to people. If you don’t put energy into this, your efforts can be easily offset by those who talk irresponsibly," said Luo.

The researchers cautioned that as laypersons, people should become aware of what they retweet and do a fact-check first. People also need to become aware of any news or events before they let others tweets and opinions shape their mindset, or else they just become part of that megaphone.

Study design

To conduct the study, researchers integrated machine learning algorithms and classic epidemiology models to retrospectively investigate the contents on social media.

In total, they retrieved 92,687,660 tweets corresponding to 8,967,986 users from January 6 to June 21, 2020.

The study was published in the Journal of Medical Internet Research.

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