Examining Social Capital, Social Support, and Language Use in an Online Depression Forum: Social Network and Content Analysis

BACKGROUND: The use of peer-to-peer online support groups and communities has grown into a social phenomenon. Many people use online support groups and communities to seek and provide social support. It is essential to examine how users’ participation behaviors may contribute to different outcomes. OBJECTIVE: This study aimed to (1) use the structural positions of online depression forum users in their reply network to predict received support and (2) examine their language use reflecting their health conditions. METHODS: A total of 2061 users and their 62,274 replies posted on a depression forum from July 2004 to July 2014 were extracted using a web crawler written in Python. The content of the forum users’ posts and replies and their reply patterns were examined. A social network analysis method was used to build the reply networks of users. The computerized text analysis method was used to measure features of the forum users’ language styles. RESULTS: Forum users’ bridging social capital (operationalized as network betweenness) was positively associated with the level of communication accommodation in their received replies (P=.04). Forum users’ bonding social capital (operationalized as network constraint) was negatively associated with the level of communication accommodation in their received replies (P<.001). The forum users’ change in their use of self-referent words and words expressing negative emotions were examined as linguistic proxies for their health conditions and mental states. The results revealed a general negative association between the number of received replies and the degree of decrease in the use of words expressing negative emotion (P=.007). CONCLUSIONS: The structural positions of online depression forum users in the reply network are associated with different participation outcomes in the users. Thus, receiving replies can be beneficial to online depression forum users.