NEWSGAC: News Genres – Advancing Media History by Transparent Automatic Genre Classification

This project studies how genres in newspapers and television news can be detected automatically using machine learning in a transparent manner. This will enable us to capture the often hypothesized but, due to the highly time consuming nature of manual content analysis, largely understudied shift from opinion-based to fact-centred reporting. Moreover, we will open the black box of machine learning by comparing, predicting and visualizing the effects of applying various algorithms on heterogeneous data with varying quality and genre features that shift over time. This will enable scholars to do large-scale analyses of historic texts and other media types as well as critically evaluate the methodological effects of various machine learning approaches.

Project website:


  • Marcel Boersma, Frank Harbers, Kim Smeenk (RUG, Journalistic Culture and Media)
  • Aysenur Bilgin, Laura Hollink, Jacco van Ossenburggen (CWI, Computer Science)
  • Erik Tjong Kim Sang (NLeSC, Research Engineer)