A new study that used artificial intelligence to analyze tweets related to coronavirus found that public attitudes towards COVID-19 and its treatments are “more contagious” than the disease itself.
The researchers examined the impact of Twitter on COVID-19 health beliefs. They also examined the competitive influence of scientific knowledge versus the speeches of politicians.
The results of the study suggest that people’s prejudices are heightened 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 like scientific publications and nonscientific events like speeches by politicians alike influence health awareness trends on social media, according to the study.
“In the pandemic, social media has contributed to much of the information, misinformation and prejudice of the public about disease, treatment and politics,” said study author Yuan Luo, chief artificial intelligence officer at the Institute for Augmented Intelligence in Medicine at Feinberg Northwestern University School of Medicine.
He added, “The study sends a ‘warning’ to the audience that the information they encounter on a daily basis may be right or wrong and guides them to choose the information backed by solid scientific evidence. Us Vendors also wanted to provide useful insights to scientists or healthcare providers so that they could more effectively send their voice to a target audience. “
Inaccurate information from politicians
“Politicians may be inaccurate about the effectiveness of a particular treatment, or say that COVID-19 isn’t a big deal; it’s like the flu. As a scientist, you need to be aware that you need to make science available to people. If you can do not do.” If you do not put energy into this matter, your efforts can easily be balanced by those who speak irresponsibly, “Luo said.
The researchers warned that lay people should become aware of what they are retweeting and do a fact check first. People also need to be aware of news or events before being influenced by other people’s tweets and opinions. Otherwise, they just become part of that megaphone.
Study design
To conduct the study, the researchers integrated machine learning algorithms and classic epidemiological models to retrospectively examine the content on social media.
In total, from January 6th to June 21st, 2020, they accessed 92,687,660 tweets, which equates to 8,967,986 users.
The study was published in the Journal of Medical Internet Research.
