With the growing number of information sources, the problem of media bias becomes worrying for a democratic society. This paper explores the task of predicting the political orientation of news articles, with a goal of analyzing how bias is expressed. We demonstrate that integrating rhetorical dimensions via latent structures over sub-sentential discourse units allows for large improvements, with a +7.4 points difference between the base LSTM model and its discourse-based version, and +3 points improvement over the previous BERT-based stateof-the-art model. We also argue that this gives a new relevant handle for analyzing political bias in news articles.