If you’re having difficulty discerning real from fake news on social media, you aren’t alone. Surveys suggest it’s a struggle for 75 percent of American adults.
Research by Christian Janze, a Ph.D. student from Goethe University in Frankfurt, Germany, and Marten Risius, an associate professor of management at Clemson, may be of help. “A lot has been written and said about fake news since the 2016 U.S. presidential campaign,” Risius said. “Our explorative study investigates how to automatically identify fake news using information immediately apparent on social media platforms.”
The study examined more than 2,000 news article posts on Facebook from left, right and mainstream media outlets during the 2016 election campaign, as well as responses from the user community. Articles were fact-checked to determine fake from real. Researchers then used 230 samples of fake news and 230 of real news and applied variables to predict those that were fake, with an 80 percent success rate. They then trained the algorithm so it could correctly detect 90 percent of the 230 fake stories.
Risius said the word count, or using all caps, exclamation marks or question marks in a post, are strong predictors of a story being fake. A person being quoted is a pretty good indicator the story is real, while if a story is shared more often with strong emotional responses, the likelihood of it being fake increases.
According to Risius, the process they used to determine authenticity is fairly simple, and he wonders why a social media outlet with a multitude of data capabilities wouldn’t flag stories they know to be fake for their users.
“Though they have many resources to determine what is real and isn’t, they may be more inclined to prefer the community engagement and public attention rather than solve an issue over what is real or fake news on their platforms,” he said.