Background 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. Objective 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. Methods 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 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. Conclusions 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.

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Background: In the post-epidemic era, online medical care is developing rapidly, and online doctor teams are attracting attention as a high-quality online medical service model that can provide more social support for patients. Methods: Using online doctor teams on the Haodf.com platform as the research subject, this study investigates the key factors in the process of doctor–patient communication, which affects patients’ emotional well-being. We also explore the different roles played by doctors as leaders and non-leaders in doctor–patient communication. From the perspective of language style, we select representative factors in the process of doctor–patient communication, namely the richness of health vocabulary, the expression of emotions, and the use of health-related terms (including perceptual words and biological words). We extract both team-level and individual-level linguistic communication styles through textual and sentiment analysis methods and empirically analyze their effects on patients’ emotional well-being using multiple linear regression models. Results: The results show that the expression of positive emotions by the team and attention to patients’ perceptions and biological conditions benefit patients’ emotional well-being. Leaders should focus on the emotional expression, whereas non-leaders should focus on the use of perceptual and biological words. Conclusions: This study expands the application of linguistic styles in the medical field and provides a practical basis for improving patients’ emotional well-being.

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.