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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Pradeep Kumar Roy & Zishan Ahmad & Jyoti Prakash Singh & Mohammad Abdallah Ali Alryalat & Nripendra P. Rana & Yogesh K. Dwivedi, 2018. "Finding and Ranking High-Quality Answers in Community Question Answering Sites," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 19(1), pages 53-68, March.
    • Handle: RePEc:spr:gjofsm:v:19:y:2018:i:1:d:10.1007_s40171-017-0172-6
    DOI: 10.1007/s40171-017-0172-6
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    References listed on IDEAS

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    1. Nripendra P. Rana & Yogesh K. Dwivedi & Banita Lal & Michael D. Williams & Marc Clement, 2017. "Citizens’ adoption of an electronic government system: towards a unified view," Information Systems Frontiers, Springer, vol. 19(3), pages 549-568, June.
    2. Elvira Ismagilova & Yogesh K. Dwivedi & Emma Slade & Michael D. Williams, 2017. "Electronic Word-of-Mouth (eWOM)," SpringerBriefs in Business, in: Electronic Word of Mouth (eWOM) in the Marketing Context, chapter 0, pages 17-30, Springer.
    3. Singh, Jyoti Prakash & Irani, Seda & Rana, Nripendra P. & Dwivedi, Yogesh K. & Saumya, Sunil & Kumar Roy, Pradeep, 2017. "Predicting the “helpfulness” of online consumer reviews," Journal of Business Research, Elsevier, vol. 70(C), pages 346-355.
    4. Nripendra P. Rana & Yogesh K. Dwivedi & Banita Lal & Michael D. Williams & Marc Clement, 0. "Citizens’ adoption of an electronic government system: towards a unified view," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
    5. Yogesh K. Dwivedi & David Wastell & Sven Laumer & Helle Zinner Henriksen & Michael D. Myers & Deborah Bunker & Amany Elbanna & M. N. Ravishankar & Shirish C. Srivastava, 2015. "Research on information systems failures and successes: Status update and future directions," Information Systems Frontiers, Springer, vol. 17(1), pages 143-157, February.
    6. Kawaljeet Kaur Kapoor & Yogesh K. Dwivedi, 2015. "Metamorphosis of Indian electoral campaigns: Modi's social media experiment," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 11(4), pages 496-516.
    7. Elvira Ismagilova & Yogesh K. Dwivedi & Emma Slade & Michael D. Williams, 2017. "Electronic Word of Mouth (eWOM) in the Marketing Context," SpringerBriefs in Business, Springer, number 978-3-319-52459-7, October.
    8. Walayat Hussain & Omar Khadeer Hussain & Farookh Khadeer Hussain & Muhammad Qasim Khan, 2017. "Usability Evaluation of English, Local and Plain Languages to Enhance On-Screen Text Readability: A Use Case of Pakistan," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(1), pages 33-49, March.
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    1. Raquel L. Pérez-Nicolás & Carlos Alario-Hoyos & Iria Estévez-Ayres & Pedro Manuel Moreno-Marcos & Pedro J. Muñoz-Merino & Carlos Delgado Kloos, 2021. "Evaluation of an Algorithm for Automatic Grading of Forum Messages in MOOC Discussion Forums," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    2. Roy, Pradeep K. & Singh, Jyoti P. & Baabdullah, Abdullah M. & Kizgin, Hatice & Rana, Nripendra P., 2018. "Identifying reputation collectors in community question answering (CQA) sites: Exploring the dark side of social media," International Journal of Information Management, Elsevier, vol. 42(C), pages 25-35.

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