Social Support and User Roles in a Chinese Online Health Community: A LDA Based Text Mining Study

摘要

Online health communities (OHCs) have become increasingly popular for people with health issues in China, which have been regarded as one of the major sources of social support. In this study, we designed a Chinese content analysis process to understand social support and user engagement in OHCs. Based on the social support theory, the process used Chinese text mining and machine learning techniques. Using a case study of an OHC among diabetics, we first divided users' posts and replies into different types of social support. Then, we aggregated each user's texts of different social support types. At last, we revealed the roles of the users by clustering. Considering the high dimensions of Vector Space Model (VSM) transformed from user texts, we proposed a new method to extract features based on LDA. In order to improve the effect of user clustering, we optimized the clustering algorithm with the principle of Maximum Distance and Elbow Method. Results showed that the process performed well in classification and clustering.