Investigating Users’ Tagging Behavior in Online Academic Community Based on Growth Model: Difference between Active and Inactive Users

Yunhong Xu (), Dehu Yin and Duanning Zhou
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Yunhong Xu: Kunming University of Science and Technology
Dehu Yin: Kunming University of Science and Technology
Duanning Zhou: Eastern Washington University

Information Systems Frontiers, 2019, vol. 21, issue 4, No 3, 772 pages

Abstract: Abstract With the development of social interaction techniques and social tagging mechanisms, online academic community as a new platform has greatly changed the way users organize and share knowledge. The large amount of social tagging data occurred on online academic community provides us a channel to systematically understand users’ tagging behavior. Based on data collected from a specific online academic community, this research first classifies users into two categories: active and inactive users. After that, growth models (damped exponential model, normal model and fluctuating model) are employed to investigate tagging behavior for both active and inactive users. Factors that might influence the likelihood of the growth models are also identified based on multinomial logistic regression. This research expands our understanding on users’ tagging behavior and factors that may affect their tagging behavior in the context of online academic community.

Keywords: Online academic community; Growth models; Tagging behavior; Active users; Inactive users (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10796-018-9891-2

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