Analyzing Question Quality Through Intersubjectivity: World Views and Objective Assessments of Questions on Social Question-Answering
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.
Published in iSchools, Spring 2013, pages 409-421.
© Choi, E., Kitzie, V., & Shah, C.
Choi, E., Kitzie, V., & Shah, C. (2019). A Machine Learning - Based Approach to Predicting Success of Questions on Social Question - Answering. ISchools, 409-421. doi: 10.9776/13224