Agenda setting is the theory of how issue salience is transferred from the media to media audience. An agenda-setting study requires one to define a set of issues and to measure their salience. We propose a semi-supervised approach based on topic modeling for exploring a news corpus and measuring the media agenda by tagging news articles with issues. The approach relies on an off-the-shelf Latent Dirichlet Allocation topic model, manual labeling of topics, and topic model customization. In preliminary evaluation, the tagger achieves a micro F1-score of 0.85 and outperforms the supervised baselines, suggesting that it could be successfully used for agenda-setting studies.