News articles often reflect an opinion or point of view, with
certain topics evoking more diverse opinions than others. For analyzing and better understanding public discourses, identifying such contested topics constitutes an interesting research question. In this paper, we describe an approach that combines NLP techniques and background knowledge from DBpedia for fi nding disputed topics in news sites. To identify these topics, we annotate each article with DBpedia concepts, extract their categories, and compute a sentiment score in order to identify those categories revealing signifi cant deviations in polarity across diff erent media. We illustrate our approach in a qualitative evaluation on a sample of six popular British and American news sites.