This paper focuses on the automated extraction of argument components from user content in the German online participation project Tempelhofer Feld. We adapt existing argumentation models into a new model for decision-oriented online participation. Our model consists of three categories: major positions, claims, and premises. We create a new German corpus for argument mining by annotating our dataset with our model. Afterwards, we focus on the two classification tasks of identifying argumentative sentences and predicting argument components in sentences. We achieve macro-averaged F 1 measures of 69.77% and 68.5%, respectively.