A machine learning-based approach to predicting success of questions on social question-answering

Choi, Erik; Kitzie, Vanessa; Shah, Chirag

Permalink

https://hdl.handle.net/2142/36040 Copy

Description

Title

A machine learning-based approach to predicting success of questions on social question-answering

Author(s)

• Choi, Erik
• Kitzie, Vanessa
• Shah, Chirag

Issue Date

2013-02

Keyword(s)

• Social Q&A
• fact-based questions
• machine learning
• question success prediction
• human-computer interaction
• information behavior

Abstract

While social question-answering (SQA) services are becoming increasingly popular, there is often an issue of unsatisfactory or missing information for a question posed by an information seeker. This study creates a model to predict question failure, or a question that does not receive an answer, within the social Q&A site Yahoo! Answers. To do so, observed shared characteristics of failed questions were translated into empirical features, both textual and non-textual in nature, and measured using machine extraction methods. A classifier was then trained using these features and tested on a data set of 400 questions – half of them successful, half not – to determine the accuracy of the classifier in identifying failed questions. The results show the substantial ability of the approach to correctly identify the likelihood of success or failure of a question, resulting in a promising tool to automatically identify ill-formed questions and/or questions that are likely to fail and make suggestions on how to revise them.

Publisher

iSchools

Type of Resource

text

Language

en

Permalink

http://hdl.handle.net/2142/36040

DOI

https://doi.org/10.9776/13224

Copyright and License Information

Copyright © 2013 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.

Owning Collections