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

  • Yunhong XuEmail author
  • Dehu Yin
  • Duanning Zhou
Article
  • 46 Downloads

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 

Notes

Acknowledgements

This research was partially supported by the National Natural Science Foundation of China (71861019, 71361017, 71640021).

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Management and EconomicsKunming University of Science and TechnologyKunmingChina
  2. 2.Department of Information Systems & Business AnalyticsEastern Washington UniversitySpokaneUSA

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