Examining Patterns of Information Exchange and Social Support in a Web-Based Health Community: Exponential Random Graph Models.

Abstract

Although an increasing number of studies have attempted to understand how people interact with others in web-based health communities, studies focusing on understanding individuals' patterns of information exchange and social support in web-based health communities are still limited. In this paper, we discuss how patients' social interactions develop into social networks based on a network exchange framework and empirically validate the framework in web-based health care community contexts. This study aims to explore various patterns of information exchange and social support in web-based health care communities and identify factors that affect such patterns. Using social network analysis and text mining techniques, we empirically validated a network exchange framework on a 10-year data set collected from a popular web-based health community. A reply network was extracted from the data set, and exponential random graph models were used to discover patterns of information exchange and social support from the network. Results showed that reciprocated information exchange was common in web-based health communities. The homophily effect existed in general conversations but was weakened when exchanging knowledge. New members in web-based health communities tended to receive more support. Furthermore, polarized sentiment increases the chances of receiving replies, and optimistic users play an important role in providing social support to the entire community. This study complements the literature on network exchange theories and contributes to a better understanding of social exchange patterns in the web-based health care context. Practically, this study can help web-based patients obtain information and social support more effectively.