Data-driven proactive policy assurance of post quality in community Q&A sites

To ensure the post quality, Q&A sites usually develop a list of quality assurance guidelines for “dos and don’ts”, and adopt the collaborative editing mechanism to fix violations of community norms. Guidelines are mostly high-level principles, and many tacit and context-sensitive aspects of the expected community norms 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 norm-meeting 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 mid-level editing patterns from historical post edits for predicting the need of editing a post. Our approach provides a proactive policy assurance mechanism that warns users potential issues in a post before it is posted.