NDLI: Analyzing and predicting question quality in community question answering services

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Analyzing and predicting question quality in community question answering services

Content Provider	ACM Digital Library
Author	Li, Baichuan King, Irwin Jin, Tan Mak, Barley Lyu, Michael R.
Abstract	Users tend to ask and answer questions in community question answering (CQA) services to seek information and share knowledge. A corollary is that myriad of questions and answers appear in CQA service. Accordingly, volumes of studies have been taken to explore the answer quality so as to provide a preliminary screening for better answers. However, to our knowledge, less attention has so far been paid to question quality in CQA. Knowing question quality provides us with finding and recommending good questions together with identifying bad ones which hinder the CQA service. In this paper, we are conducting two studies to investigate the question quality issue. The first study analyzes the factors of question quality and finds that the interaction between askers and topics results in the differences of question quality. Based on this finding, in the second study we propose a Mutual Reinforcement-based Label Propagation (MRLP) algorithm to predict question quality. We experiment with Yahoo!~Answers data and the results demonstrate the effectiveness of our algorithm in distinguishing high-quality questions from low-quality ones.
Starting Page	775
Ending Page	782
Page Count	8
File Format	PDF
ISBN	9781450312301
DOI	10.1145/2187980.2188200
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-04-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Analysis Prediction Question quality Community question answering
Content Type	Text
Resource Type	Article

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