How User Learning Behavior Affects Answer Quality in Online Q&A Communities?

Yinzhi Luo, Feng Hu () and Jianmei Xiao ()
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Yinzhi Luo: Beijing University of Technology
Feng Hu: Beijing University of Technology
Jianmei Xiao: University of Sheffield

A chapter in Proceedings of the 2025 5th International Conference on Informatization Economic Development and Management (IEDM 2025), 2025, pp 206-215 from Springer

Abstract: Abstract The quality of answers in online Q&A communities is essential for their sustainable development. Existing research has mainly focused on factors such as answer features, user characteristics, and community mechanisms, but the impact of user learning behavior on answer quality remains underexplored. This study, based on data from the Zhihu platform, applies social cognitive theory and the SBERT model to calculate the semantic similarity between answers. By quantifying user learning behavior, the study examines how it affects answer quality. The results show a negative correlation between user learning behavior and answer quality, primarily due to content homogeneity and a lack of innovation. Information entropy moderates this relationship, with higher entropy mitigating the negative effects of learning behavior and improving answer quality. This study introduces a new definition of user learning behavior and offers a quantitative method, providing theoretical support and practical recommendations for improving the answer recommendation system and user experience on online Q&A platforms.

Keywords: Online Q&A Community; User Learning Behavior; Answer Quality; Social Cognitive Theory; SBERT Model (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-724-3_21

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DOI: 10.2991/978-94-6463-724-3_21

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