[Objective] This paper studies the decision-making rules for knowledge sharing and hiding among users of online health communities. It tries to improve the users’ overall health knowledge levels. [Methods] First, we constructed an evolutionary game model for the decision-making mechanism of lurkers and sharers’ knowledge-sharing and hiding behaviors. Then, we retrieved data on breast cancer topics from the Zhihu platform to assign model parameters. Finally, we conducted numerical experiments with Matlab to explore the impacts of parameter changes. [Results] The transformation of users from knowledge hiding to sharing was positively affected by the benefits of knowledge innovation, emotion, and community rewards. Privacy risks and coding costs posed negative impacts on user behaviors. The two user groups had different sensitivity levels to the factors. [Limitations] We did not set a nonlinear utility function; manually labeling data may yield errors. [Conclusions] This paper could help the online health community transform users from knowledge hiding to sharing.
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