Analyzing knowledge demand and supply of community question answering with tf-pidf

[Objective] This paper propose a new method to study the knowledge demand and supply of community question answering, aiming to make effective targeted interventions. [Methods] First, we constructed novel word weight calculation models (TF-PIDF) for the questions and answers. Then, we obtained the main categories of demanded and supplied knowledge by clustering questions and answers, as well as the popularity of topics. Third, we paired the categories of knowledge demand and their supply counterparts. Fourth, we proposed an algorithm to calculate the popularity of knowledge demands. [Results] The proposed model was examined with topis on influenza from the community of ZHIHU. We found six categories of topics for knowledge demand and supply. The trending one was“epidemic”, which represented the most popular real time needs. [Limitations] The identified topics rely on the topic meaning from feature word clustering. [Conclusions] The proposed method could effectively manage the knowledge demand and supply of community question answering.