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Title:A machine learning-based approach to predicting success of questions on social question-answering
Author(s):Choi, Erik; Kitzie, Vanessa; Shah, Chirag
Subject(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.
Issue Date:2013-02
Citation Info:Choi, E., Kitzie, V., & Shah, C. (2013). A machine learning-based approach to predicting success of questions on social question-answering. iConference 2013 Proceedings (pp. 409-421). doi:10.9776/13224
Genre:Conference Paper / Presentation
Publication Status:published or submitted for publication
Peer Reviewed:is peer reviewed
Rights Information:Copyright © 2013 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.
Date Available in IDEALS:2013-01-28

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