Predicting Collaborative Edits of Questions and Answers in Online Q&A Sites
Collaborative editing can play an important role in online Q&A sites,including iteratively advancing solutions and significantly improving the quality of questions and answers.However,the value of collaborative editing has not been fully utilized.Currently,there is no way for users to easily distinguish questions and answers which need be collaboratively edited from other ones in many online Q&A sites.For example,in Stack Overflow,there is no indicator to tell users whether the question/answer being seen need be edited or not.Thus,to make better use of collaborative editing,in this paper,we propose a framework to predict whether questions and answers need be collaboratively edited just after they are posted.The framework mainly extracts features from questions,answers,and posters(of questions and answers),and adopts machine learning techniques(e.g.,LDA,SVM)to do prediction.To evaluate the framework,we chose Stack Overflow as our study platform and conducted experiments with millions of questions and answers.The results show that the proposed framework can achieve very high accuracy and be efficiently adopted in different online Q&A sites.
Collaborative Editing SVM LDA Q&A Site Stack Overflow
Guo Li Tun Lu Xianghua Ding Ning Gu
School of Computer Science,Fudan University Shanghai Key Laboratory of Data Science,Fudan University Shanghai,China
国内会议
第10届全国计算机支持的协同工作学术会议暨中国计算机学会协同计算专委年度工作会议
太原
英文
502-508
2015-08-28（万方平台首次上网日期，不代表论文的发表时间）