Here are examples of such signals:
– Authors cite expert sources (positive)
– Title is clickbaity (negative)
And my favorite:
– Authors acknowledge uncertainty (positive)
Will the news ecosystem ever be able to label stories automatically based on automatic detection of such signals, and if so, should it? These are open questions. The best way to improve news literacy may be the SIFT method advocated by Mike Caulfield, which shifts attention away from intrinsic properties of individual news stories and advises readers to:
– Investigate the source
– Find better coverage
– Trace claims, quotes, and media to original context
“The goal of SIFT,” writes Charlie Warzel in Don’t Go Down the Rabbit Hole, “isn’t to be the arbiter of truth but to instill a reflex that asks if something is worth one’s time and attention and to turn away if not.”
SIFT favors extrinsic signals over the intrinsic ones that were the focus of the W3C Credible Web Community Group. But intrinsic signals may yet play an important role, if not as part of a large-scale automated labeling effort then at least as another kind of news literacy reflex.
This morning, in How public health officials can convince those reluctant to get the COVID-19 vaccine, I read the following:
What made these Trump supporters shift their views on vaccines? Science — offered straight-up and with a dash of humility.
The unlikely change agent was Dr. Tom Frieden, who headed the Centers for Disease Control and Prevention during the Obama administration. Frieden appealed to facts, not his credentials. He noted that the theory behind the vaccine was backed by 20 years of research, that tens of thousands of people had participated in well-controlled clinical trials, and that the overwhelming share of doctors have opted for the shots.
He leavened those facts with an acknowledgment of uncertainty. He conceded that the vaccine’s potential long-term risks were unknown. He pointed out that the virus’s long-term effects were also uncertain.
“He’s just honest with us and telling us nothing is 100% here, people,” one participant noted.
Here’s evidence that acknowledgement of uncertainty really is a powerful signal of credibility. Maybe machines will be able to detect it and label it; maybe those labels will matter to people. Meanwhile, it’s something people can detect and do care about. Teaching students to value sources that acknowledge uncertainty, and discount ones that don’t, ought to be part of any strategy to improve news literacy.