It doesn’t take a scientific study to reveal that doom-and-gloom sells in American media. The news coverage surrounding the coronavirus is more negative in the U.S. obviously due to the amount of censorship at hand. The press reports bad news about the virus most of the time or they don’t get exposure. News publication editors or mediums, like YouTube, won’t allow the story to get out. That also includes un-personing or demonizing people who go against the grain.
U.S. news coverage of COVID has been more negative than in other countries, researchers find
By Geoff Colvin, November 29, 2020, Fortune
The Centers for Disease Control and Prevention offers abundant advice for coping with COVID-19, including this recommendation for those troubled by stress: “Take breaks from watching, reading, or listening to news stories.”
Just-published research reveals why that advice may be especially apt for people who consume major media content in the U.S. Its central finding: While the pandemic is definitely no picnic, coverage of it in America’s most-viewed and most-read media outlets is vastly more negative than coverage in U.S. media broadly defined, or in non-U.S. media.
You may wonder how coverage of a deadly global pandemic could be anything other than negative, but day-to-day developments over the past eight months have been good as well as bad. Case counts sometimes decline, therapies are discovered, vaccine research advances. Through it all, America’s most popular media outlets have proved extraordinarily adept at finding the clouds in a blue sky and making them the focus of the story.
The new research paper, “Why is all COVID-19 news bad news?” comes from Bruce Sacerdote and Ranjan Sehgal of Dartmouth College and Molly Cook of Brown University. They analyzed 20,000 COVID-related articles and TV transcripts from U.S. and English-language media in the U.K., India, Canada, and Australia. The researchers measured negativity using established methods, augmenting those results with their own system based on two- and three-word phrases combined with machine learning “to find the phrases that best predict whether the human reader will classify an article as strongly negative.” The articles and transcripts fell into three subtopics—vaccines, case counts, and reopenings.