FakeNewsPerception: An Eye Movement Dataset on the Perceived Believability of News Stories

FakeNewsPerception: An Eye Movement Dataset on the Perceived Believability of News Stories

Extensive use of the internet has enabled easy access to many different sources, such as news and social media. Content shared on the internet cannot be fully fact-checked, and as a result, misinformation can spread in a fast and easy way. Recently, psychologists and economists have shown in many experiments that prior beliefs, knowledge, and the willingness to think deliberately are important determinants to explain who falls for fake news. Many of these studies only rely on self-reports, which suffer from social desirability, and that we need more objective measures of information processing such as eye movements during reading news. To provide the research community the opportunity to study human behaviors on the news truthness, we propose the FakeNewsPerception dataset. FakeNewsPerception consists of eye movements during reading, perceived believability scores, questionnaires including Cognitive Reflection Test (CRT) and News-Find-Me (NFM) perception, and political orientation, collected from 25 participants with 60 news items. Initial analyses of the eye movements revealed that human perception differs when viewing true and fake news.