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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 qualityscale due to insufficient number of Wikipedia editors and enormous number ofarticles. Supervised Machine Learning (ML) quality improvement approaches thatcan automatically identify and fix content issues rely on manual labels ofindividual Wikipedia sentence quality. However, current labeling approaches aretedious and produce noisy labels. Here, we propose an automated labelingapproach that identifies the semantic category (e.g., adding citations,clarifications) of historic Wikipedia edits and uses the modified sentencesprior to the edit as examples that require that semantic improvement.Highest-rated article sentences are examples that no longer need semanticimprovements. We show that training existing sentence quality classificationalgorithms on our labels improves their performance compared to training themon existing labels. Our work shows that editing behaviors of Wikipedia editorsprovide better labels than labels generated by crowdworkers who lack thecontext to make judgments that the editors would agree with.

Automatically Labeling Low Quality Content on Wikipedia by Leveraging Patterns in Editing Behaviors