UNIVERSITY PARK, Pa. — Social media can expose users to a myriad of misinformation, including fake news — news stories with intentionally false information. In fact, during the 2016 U.S. presidential election, fake news engaged more people than real news, according to a BuzzFeed News analysis.
Numerous deep learning methods currently exist to detect fake news, but these methods are unable to explain why it is recognized as such. Now, a team of researchers from Penn State and Arizona State is working to help explain why any piece of fake news is detected as being false.
The team’s recent findings are to be presented at the Association for Computing Machinery (ACM)’s Knowledge Discovery and Data Mining (KDD), a flagship data mining conference, held Aug. 4-8 in Anchorage, Alaska.
“Detection is one thing, but how to present it to the user to explain why it’s fake is more challenging,” said Dongwon Lee, associate professor in the Penn State College of Information Sciences and Technology and researcher on the project. “If we don’t provide a good explanation, it has a limited impact to curtail the distribution of misinformation because people won’t accept it.”
In their study, the researchers built an explainable fake-news detection framework, which they call dEFEND (Explainable FakE News Detection). The framework consists of three components: (1) a news content encoder, to detect opinionated and sensational language styles commonly found in fake news; (2) a user comment encoder, to detect activities such as skeptical opinions and sensational reactions in comments on news stories; and (3) a sentence-comment, co-attention component, which detects sentences in news stories and user comments that can explain why a piece of news is fake.
The new detection algorithm designed and developed in this novel approach has outperformed seven other state-of-the-art methods in detecting fake news, according to the researchers.
“Among the users’ comments, we can pinpoint the most effective explanation as to why this [piece of news they are reading] is fake news,” explained Lee. “Some users expressed discontent but others provide particular evidence, such as linking to a fact-checking website or to an authentic news article. These techniques can concurrently find such evidence and present it to the user as potential explanation.”