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This paper presents the contribution of the Data Science Kitchen at GermEval2021 shared task on the identification of toxic, engaging, and fact-claimingcomments. The task aims at extending the identification of offensive language,by including additional subtasks that identify comments which should beprioritized for fact-checking by moderators and community managers. Ourcontribution focuses on a feature-engineering approach with a conventionalclassification backend. We combine semantic and writing style embeddingsderived from pre-trained deep neural networks with additional numericalfeatures, specifically designed for this task. Ensembles of Logistic Regressionclassifiers and Support Vector Machines are used to derive predictions for eachsubtask via a majority voting scheme. Our best submission achievedmacro-averaged F1-scores of 66.8%, 69.9% and 72.5% for the identification oftoxic, engaging, and fact-claiming comments.

Data Science Kitchen at GermEval 2021: A Fine Selection of Hand-Picked Features, Delivered Fresh from the Oven