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User comments have become an essential part of online journalism. However,newsrooms are often overwhelmed by the vast number of diverse comments, forwhich a manual analysis is barely feasible. Identifying meta-comments thataddress or mention newsrooms, individual journalists, or moderators and thatmay call for reactions is particularly critical. In this paper, we present anautomated approach to identify and classify meta-comments. We compare commentclassification based on manually extracted features with an end-to-end learningapproach. We develop, optimize, and evaluate multiple classifiers on a commentdataset of the large German online newsroom SPIEGEL Online and the 'One MillionPosts' corpus of DER STANDARD, an Austrian newspaper. Both optimizedclassification approaches achieved encouraging $F_{0.5}$ values between 76% and91%. We report on the most significant classification features with the resultsof a qualitative analysis and discuss how our work contributes to makingparticipation in online journalism more constructive.

Who is Addressed in this Comment? Automatically Classifying Meta-Comments in News Comments