The production, consumption, and dissemination of online news is of growing interest among scholars studying democracy, but significant challenges remain for the study of media quality in a comparative perspective, including varying political environments, language barriers, cultural contexts, and differing media regulation. To address these concerns, we leverage a novel comparative data set of links shared on Facebook, made accessible to the research community through the Social Science One research initiative, to study the prevalence of unreliable online news in 27 countries in Europe. We use a supervised model (trained on US data) to predict the credibility of a given news domain based on users’ feedback and behavior. We show that interactions with links to news websites on social media allow us to predict the credibility of news and that a model that learns such relationships is portable across national contexts. Using this model we find an East-West divide between countries in Europe with higher prevalence of unreliable news in former socialist countries, as well as in the UK. Furthermore, we find that more recently registered news domains and those registered outside of the country are more likely to be predicted to be the sources of unreliable information.