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Early prediction of promising expert users on community question answering sites

Author

Listed:
  • Pradeep Kumar Roy

    (Indian Institute of Information Technology)

  • Jyoti Prakash Singh

    (National Institute of Technology Patna)

Abstract

Community question answering (CQA) sites have become a popular medium for exchanging knowledge with other members of the community. Users can publish questions, answers, and comments on these sites. Furthermore, users of the CQA sites are able to express their thoughts on a post by voting positively or negatively. People anticipate rapid and high-quality answers from these CQA sites, which are often provided by a small group of users known as experts. A large number of queries remain unanswered on these forums, emphasising the scarcity of experts. To address this problem, we presented a methodology for predicting promising expert users for CQA sites. Promising experts are individuals that have just joined the community and have shown glimpses of producing high-quality content to the site. The suggested method looks at the first month of a user’s postings to determine whether or not the individual is a promising expert. The experimental findings revealed that the suggested approach accurately predicts future experts.

Suggested Citation

  • Pradeep Kumar Roy & Jyoti Prakash Singh, 2024. "Early prediction of promising expert users on community question answering sites," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(7), pages 2902-2913, July.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:7:d:10.1007_s13198-024-02303-0
    DOI: 10.1007/s13198-024-02303-0
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    References listed on IDEAS

    as
    1. 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.
    2. Saida Kichou & Omar Boussaid & Abdelkrim Meziane, 2020. "Tag's Depth-Based Expert Profiling Using a Topic Modeling Technique," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 16(4), pages 81-99, October.
    3. 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.
    Full references (including those not matched with items on IDEAS)

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