JMIR Publications

ABSTRACT

Background:

Although an increasing number of studies have attempted to understand how people interact with others in online health communities, our understanding of online activities that explain patterns of information exchange and social support in online health communities is still limited. In this paper we discuss how patients’ social interactions develop into social networks based on network exchange framework, and empirically validate the framework in online healthcare community contexts.

Objective:

The aim of our research is to explore various patterns of information exchange and social support in online healthcare communities, and identify factors that affect such patterns.

Methods:

Using social network analysis and text mining techniques, we empirically validated network exchange framework on a 10-year dataset collected from a popular online 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:

Results showed that reciprocated information exchange (|estimate/standard error |>2.0, coefficient>0) is common in online health communities. Information exchange occurred more frequently between users of different types (|estimate/standard error |>2.0, coefficient<0) and between friends (|estimate/standard error |>2.0, coefficient>0). New members in online health communities tended to receive more support (|estimate/standard error |>2.0, coefficient>0). Furthermore, emotional factors (|estimate/standard error |>2.0) affected users’ chances of obtaining social support.

Conclusions:

This study complements literature on network exchange theories and contributes to a better understanding of social exchange patterns in online healthcare context. Practically, this study can help online patients obtain information and social support more effectively.