Finding and Ranking High-Quality Answers in Community Question Answering Sites

Pradeep Kumar Roy (), Zishan Ahmad (), Jyoti Prakash Singh (), Mohammad Abdallah Ali Alryalat (), Nripendra P. Rana () and Yogesh K. Dwivedi ()
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Pradeep Kumar Roy: National Institute of Technology Patna
Zishan Ahmad: National Institute of Technology Patna
Jyoti Prakash Singh: National Institute of Technology Patna
Mohammad Abdallah Ali Alryalat: Al-Balqa’ Applied University
Nripendra P. Rana: Swansea University Bay Campus
Yogesh K. Dwivedi: Swansea University Bay Campus

Global Journal of Flexible Systems Management, 2018, vol. 19, issue 1, No 5, 53-68

Abstract: Abstract Community Question Answering (CQA) sites have become a very popular place to ask questions and give answers to a large community of users on the Internet. Stack Exchange is one of the popular CQA sites where a large amount of contents are posted every day in the form of questions, answers and comments. The answers on Stack Exchange are listed by their recent occurrences, time of posting or votes obtained by peer users under three tabs called active, oldest and votes, respectively. Votes tab is the default setting on the site and is also preferred tab of users because answers under this tab are voted as good answers by other users. The problem of voting-based sorting is that new answers which are yet to receive any vote are placed at the bottom in vote tab. The new answer may be of sufficiently high-quality to be placed at the top but no or fewer votes (later posting) have made them stay at the bottom. We introduce a new tab called promising answers tab where answers are listed based on their usefulness, which is calculated by our proposed system using the classification and regression models. Several textual features of answers and users reputation are used as features to predict the usefulness of the answers. The results are validated with good values of precision, recall, F1-score, area under the receiver operating characteristic curve (AUC) and root mean squared error. We also compare the top ten answers predicted by our system to the actual top ten answers based on votes and found that they are in high agreement.

Keywords: Answer ranking; Classification; Community Question Answering; Data imbalance; Regression (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s40171-017-0172-6

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