Quality control is critical to open production communities like Wikipedia. Editors enact quality control on the borders of Wikipedia to review edits (counter-vandalism) and new article creations (new page patrolling) shortly after they are saved. In this paper, we describe a long-standing set of inefficiencies that have plagued new page patrolling by drawing a contrast to the more efficient, distributed processes for counter-vandalism. To effect better page review distribution, we develop an effective automated topic model based on a labeling strategy that leverages a folksonomy developed by subject specific working groups in Wikipedia (WikiProject tags) and a flexible ontology (WikiProjects Directory) to arrive at a hierarchical and uniform label set. We are able to attain very high fitness measures (macro ROC-AUC: 95.2%, macro PR-AUC: 74.5%) and real-time performance using word2vec-based features on the intial draft versions of articles. Finally, we present a proposal for how incorporating this model into current tools will shift the dynamics of new article review positively. CCS Concepts: • Human-centered computing → Social recommendation; Computer supported cooperative work; Empirical studies in collaborative and social computing; Wikis; Social tagging systems;

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Wikipedia articles aim to be definitive sources of encyclopedic content. Yet, only 0.6% of Wikipedia articles have high quality according to its quality scale due to insufficient number of Wikipedia editors and enormous number of articles. Supervised Machine Learning (ML) quality improvement approaches that can automatically identify and fix content issues rely on manual labels of individual Wikipedia sentence quality. However, current labeling approaches are tedious and produce noisy labels. Here, we propose an automated labeling approach that identifies the semantic category (e.g., adding citations, clarifications) of historic Wikipedia edits and uses the modified sentences prior to the edit as examples that require that semantic improvement. Highest-rated article sentences are examples that no longer need semantic improvements. We show that training existing sentence quality classification algorithms on our labels improves their performance compared to training them on existing labels. Our work shows that editing behaviors of Wikipedia editors provide better labels than labels generated by crowdworkers who lack the context to make judgments that the editors would agree with.