麦糠：堆栈溢出问题的表征与建模
Chaff from the Wheat : Characterization and Modeling of Deleted Questions on Stack Overflow
摘要：
Stack Overflow是网络上最受程序员欢迎的CQA，拥有205万用户，510万问题和940万答案。Stack Overflow对如何发布问题和热情洋溢的审核社区有明确，详细的指导方针。尽管有这些精确的沟通和保障措施，但在Stack Overflow上发布的问题可能会非常偏离主题或质量非常差。这些问题可以由经验丰富的社区成员和版主自行决定从Stack Overflow中删除。我们提出了第一个研究删除的问题堆栈溢出。我们将我们的研究分为两个部分（一）删除问题的表征约。5年（2008-2013年）的数据，（ii）在创建问题时删除的预测。我们的表征研究揭示了问题删除现象的多个见解。我们观察到，随着时间的推移，被删除的问题数量显著增加。我们发现，投票决定删除一项问题需要相当长的时间，但一旦投票，社会就迅速采取行动。我们还看到问题作者删除他们的问题以挽救声誉点。我们注意到一些意外删除高质量问题的情况，但此类问题会被投票回迅速恢复删除。我们在Stack Overflow上发现了一个问题质量的金字塔结构，并发现删除的问题位于金字塔的底部（质量最低）。我们还建立了一个预测模型来检测创建时的问题删除。我们对基于用户简档、社区生成、问题内容和句法风格的47个特征进行了实验，并报告了66%的准确率。我们的特征分析表明，所有四类特征对预测任务都很重要。我们的研究结果揭示了基于社区的问答网站的内容质量维护的重要建议。
英文摘要：
Stack Overflow is the most popular CQA for programmers on the web with 2.05M users, 5.1M questions and 9.4M answers. Stack Overflow has explicit, detailed guidelines on how to post questions and an ebullient moderation community. Despite these precise communications and safeguards, questions posted on Stack Overflow can be extremely off topic or very poor in quality. Such questions can be deleted from Stack Overflow at the discretion of experienced community members and moderators. We present the first study of deleted questions on Stack Overflow. We divide our study into two parts (i) Characterization of deleted questions over approx. 5 years (2008-2013) of data, (ii) Prediction of deletion at the time of question creation. Our characterization study reveals multiple insights on question deletion phenomena. We observe a significant increase in the number of deleted questions over time. We find that it takes substantial time to vote a question to be deleted but once voted, the community takes swift action. We also see that question authors delete their questions to salvage reputation points. We notice some instances of accidental deletion of good quality questions but such questions are voted back to be undeleted quickly. We discover a pyramidal structure of question quality on Stack Overflow and find that deleted questions lie at the bottom (lowest quality) of the pyramid. We also build a predictive model to detect the deletion of question at the creation time. We experiment with 47 features based on User Profile, Community Generated, Question Content and Syntactic style and report an accuracy of 66%. Our feature analysis reveals that all four categories of features are important for the prediction task. Our findings reveal important suggestions for content quality maintenance on community based question answering websites.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
