An algorithm-based system that identifies telltale linguistic cues in fake news stories could provide news aggregator and social media sites like Google News with a new weapon in the fight against misinformation. Led by computer science and engineering professor Rada Mihalcea, the U-M researchers who developed the system have demonstrated that it’s comparable to and sometimes better than humans at correctly identifying fake news stories.
In a recent study, it successfully found fakes up to 76 percent of the time, compared to a human success rate of 70 percent. In addition, their linguistic analysis approach could be used to identify fake news articles that are too new to be debunked by cross-referencing their facts with other stories. “You can imagine any number of applications for this on the front or back end of a news or social media site,” Mihalcea said. “It could provide users with an estimate of the trustworthiness of individual stories or a whole news site. Or it could be a first line of defense on the back end of a news site, flagging suspicious stories for further review.”
