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TL;DR

This paper investigates how the quality of questions and answers in Community Question Answering sites like Stack Overflow are interconnected and proposes algorithms to jointly predict their quality.

Contribution

It introduces a novel approach to jointly predict question and answer quality, leveraging their positive correlation, with extensive evaluation demonstrating effectiveness.

Findings

Answer quality is strongly positively correlated with question quality.

Joint prediction algorithms outperform independent methods.

Proposed methods are effective and efficient.

Abstract

Community Question Answering (CQA) websites have become valuable repositories which host a massive volume of human knowledge. To maximize the utility of such knowledge, it is essential to evaluate the quality of an existing question or answer, especially soon after it is posted on the CQA website. In this paper, we study the problem of inferring the quality of questions and answers through a case study of a software CQA (Stack Overflow). Our key finding is that the quality of an answer is strongly positively correlated with that of its question. Armed with this observation, we propose a family of algorithms to jointly predict the quality of questions and answers, for both quantifying numerical quality scores and differentiating the high-quality questions/answers from those of low quality. We conduct extensive experimental evaluations to demonstrate the effectiveness and efficiency of our methods.