The TUWienKBS system for abusive tweet detection in the GermEval 2018 competition is a stacked classifier. Five disjoint sets of features are used: token and character n-grams, relatedness to the, according to TFIDF, most important tokens and character n-grams within each class, and the average of the embedding vectors of all tokens in a tweet. Three base classifiers (maximum entropy and two random forest ensembles) are trained independently on each of these features, which yields 15 predictions for the type and/or level of abusiveness of the given tweets. One maximum entropy meta-level classifier performs the final classification. As word embedding fallback for out-of-vocabulary tokens we use the embeddings of the largest prefix and suffix of the token, if such embeddings can be found.