Research On Knowledge-Sharing Behavior Of Token Knowledge Community Based On Social Network

With the development of knowledge sharing economy,virtual knowledge community has become an important environment for people to create and spread ideas.To continuously provide valuable content to users,the knowledge community needs to attract a large number of high-quality content contributors and motivate them to export high-quality content continuously.Based on block chain technology and token economy,the token knowledge community is a virtual knowledge community that rewards user behavior in the form of token,typical platforms include Steemit and Bihu.This type of platform uses token incentive mechanism to provide material incentives to users,and make users become shareholders of the community's token,in order to encourage the writers,optimize the quality of content,and improve the platform in the way of user autonomy.Understanding the rules of knowledge dissemination behavior in token knowledge community can help us to test the effect of token incentive mechanism,discover the actual distribution of tokens and provide suggestions for promoting knowledge dissemination in the community.User behavior is embedded in community relationship network.Therefore,based on SNA and real data of token knowledge community,this study analyzes the characteristics and formation mechanism of knowledge sharing network in token knowledge community,then identifies the main factors that affect users' knowledge sharing.Based on existing research,this study selects endogenous structural variables including degree centrality,betweenness centrality,clustering coefficient,and node attribute variables including user's follow number,fan number,token income level as factors affecting the formation of user knowledge sharing network.Then the framework of interpretation of knowledge sharing network is established.The empirical part uses the software Bazhuayu to crawl the real data such as posting and follow list of users on Bihu,and uses the software Gephi to establish a visual network of user follower relationship with 22323 follower relationships between 798 users.The statnet package in the R is used to estimate the sample network by ERGM.The results show that:there is preferential attachment phenomenon in the network.The influence of intermediary effect is relatively weak,the speed of knowledge dissemination is fast.Users tend to form small groups.Users with a great number of fans and high token income are more likely to be noticed.And the main users in the community tend to follow each other.In view of the knowledge communication management in the community,this paper suggests that the platform should prevent opinion leaders from monopolizing the resources and also notice potential users.In view of the token incentive mechanism,this paper suggests that platform constantly improve the distribution rules of community tokens in practice and suppress inferior users so as to make the token incentive mechanism more equitable and effective.This paper has certain research value,the research conclusions provide a new research angle for the existing token knowledge community related research and are helpful for the community to optimize its content push strategy and improve its user incentive mechanism.