Exploring user interaction patterns in an online physician interactive community based on exponential random graph models

Abstract

The online physician interactive community (OPIC) is a platform designed for medical workers to discuss medical issues. Physician users can create content in OPIC by posting and replying to posts to discuss the solutions of medical problems with other users. The OPIC plays an important role in bringing together physicians from different medical specialties and disseminating medical experience. However, most OPIC users are not very active in replying to posts, which makes it difficult to fulfill users’ needs for medical information exchange and the development of OPIC is difficult. Current research has given little attention to the communication of physician users in the OPIC. It is necessary to examine how reposting links are established between users in OPIC. This study builds a user interaction network based on the perspective of social network analysis using user repost data from a well-known OPIC in China. Then, an exponential random graph model (ERGM) was applied to quantitatively analyze this user interaction network. Some reposting patterns among OPIC users were discovered. There is significant reciprocity in OPIC of reposting interactions between users. Users with homogeneous characteristics in terms of professional status, community honor status, and geographic location were more likely to interact with each other. In addition, users who added a profile, had a higher level of social effort, and generated more neutral content were more likely to receive responses from others. This study reveals the interaction patterns between physician users in OPIC, which enriches the related research within the OPIC domain and helps to improve communication between users in OPIC.