A fake news detection framework

In today’s digital world, social media accelerates the spread of information, offering both advantages and serious risks. While it promotes open access to knowledge, it also enables the rapid circulation of fake news, misinformation, and disinformation. Fake news—deliberately false content designed to mimic legitimate media—threatens democratic values and public trust. Misinformation, shared without harmful intent, and disinformation, spread deliberately to mislead, further complicate the issue. Advanced AI technologies have made it harder to separate truth from falsehood. Combating fake news demands more than technical detection tools; it also requires promoting digital literacy and critical thinking. While current research often focuses on algorithmic and statistical detection, less attention has been given to the role of reasoning. This study proposes a new framework using counterfactual reasoning—imagining alternative scenarios—to evaluate information credibility. It also addresses modal fallacies, which occur when people confuse what is possible with what is necessary. Counterfactuals can reveal inconsistencies in fake news, but if used without care, they may lead to faulty logic. By identifying and correcting these reasoning errors, this approach enhances the detection of deceptive content and encourages more thoughtful information analysis.