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News recommender systems are used by online news providers to alleviateinformation overload and to provide personalized content to users. However,algorithmic news curation has been hypothesized to create filter bubbles and tointensify users' selective exposure, potentially increasing their vulnerabilityto polarized opinions and fake news. In this paper, we show how information onnews items' stance and sentiment can be utilized to analyze and quantify theextent to which recommender systems suffer from biases. To that end, we haveannotated a German news corpus on the topic of migration using stance detectionand sentiment analysis. In an experimental evaluation with four differentrecommender systems, our results show a slight tendency of all four models forrecommending articles with negative sentiments and stances against the topic ofrefugees and migration. Moreover, we observed a positive correlation betweenthe sentiment and stance bias of the text-based recommenders and thepreexisting user bias, which indicates that these systems amplify users'opinions and decrease the diversity of recommended news. The knowledge-awaremodel appears to be the least prone to such biases, at the cost of predictiveaccuracy.

Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection