Abstract: [Purpose/Significance] Online health communities（OHCs）have become important platforms for patients with chronic diseases to obtain social support. The aim of this study is to analyze the posts in OHCs to figure out the impact of social support on user's continued engagement, which helps to grasp the psychology of users in the using process, and promotes users' continued engagement in OHCs. [Design/Methodology] Taking "Sweet Home" as an example,the study used text mining method to identify the social support types in posts of the online diabetes community, and employed the survival analysis of lifespan to analyze the impact of different social support types of posts and replies on the continued engagement of the post initiator in online chronic diseases community. [Findings/Conclusion] The results of this study demonstrate that（1）The postings that requiring for support in OHCs have an impact of "Demand-driven", that is, a normalized posting for informational support or companionship support has a positive impact on users’continued engagement, while an emergent posting for emotion support has a negative impact. A Posting that providing informational support or emotional support has no significant impact.（2）The posts replied from others have the influence of "Supply-Demand Connection", "Collaborative Feedback" and "Subtle Influence", that is, the information seekers and community companions are respectively affected to a higher degree by replied posts that providing informational support and companionship, and the information providers are positively influenced by replied posts that providing informational support or emotional support, and the emotional support seekers are positively influenced by replied posts that belong to companionship. [Originality/Value] This study reveals the effect of social support on the user's behaviors in online chronic disease community, and provides insightful advice for OHCs platforms to optimize their service.

Keywords: Online health communities, Continued engagement, Social support, Chronic disease, Survival analysis