From bad to good: An investigation of question quality and transformation

Vanessa Kitzie, Erik Choi, Chirag Shah

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Social question answering (SQA) services are a popular way for people to exchange information. Unfortunately, the quality of information exchanged can be variable and few studies focus on the quality of questions asked. To address this, we explored the influence of textual features on question quality based on 126 questions taken from five different categories of Yahoo! Answers labeled as Bad by human assessors and then revised to be Good by them. Findings indicate significant differences between the means of each feature before and after revision, suggesting the potential for an automated system that could flag questions of poor quality. In addition, by exploring the relationship between features contributing to good quality questions, we suggest a simple set of strategies askers can take when writing a question in order to improve its chances of receiving a satisfactory answer.

Original languageEnglish (US)
JournalProceedings of the ASIST Annual Meeting
Issue number1
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Library and Information Sciences


  • Feature extraction
  • Question quality
  • Social question answering


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