Data-Driven Proactive Policy Assurance of Post Quality in Community q&a Sites

Abstract

To ensure the post quality, Q&A sites usually develop a list of quality assurance guidelines for "dos and don'ts", and adopt collaborative editing mechanism to fix quality violations. 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. Our approach provides a proactive policy assurance mechanism that warns users potential quality issues in a post before it is posted.