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Quality guidelines are mostly high-level principles, and many tacit and context-sensitive aspects of the expected quality cannot be easily enforced by a set of explicit rules. Collaborative editing is a reactive mechanism after low-quality posts have been posted. Our study of collaborative editing data on Stack Overflow suggests that tacit and context-sensitive quality-assurance knowledge is manifested in the editing patterns of large numbers of collaborative edits. Inspired by this observation, we develop and evaluate a Convolutional Neural Network based approach to learn editing patterns from historical post edits for predicting the need of editing a post. 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