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.

Online medical consultation has become a crucial channel for patients seeking health support. Based on data from a psychiatric department in a leading online medical consultation platform in China, this study examines two possible types of online self-presentation strategies (positive impression management strategy and blending-in impression management strategy) employed by doctors in three dimensions: informational management, affective management, and image management, and explores their impact on patient selection. Meanwhile, an information integration perspective was incorporated and the interaction effects between impression management strategies taken by doctors and patient reviews expressed by online patients are also explored. Results indicate that the information quantity (representing the informational management dimension) in doctors’ profiles has a negative impact on patient selection (β = −0.142, p < 0.01), while the positive emotion expression (representing affective management) (β = 0.423, p < 0.01) and profile photo (representing image management) (β = 1.098, p < 0.01) positively influence patient selection. Patient reviews related to expertise positively moderate the effect of information quantity in doctors’ introduction on patient selection (β = −0.632, p < 0.05). In contrast, patient reviews concerning attitude (β = −0.882, p < 0.01) and credibility (β = −0.488, p < 0.01) negatively moderate the effect of emotion expression and profile photos on patient selection, respectively. The findings extend the applicability of impression management theory, providing a novel perspective for comprehending the impact of doctors’ self-presentation on patient selection and its interaction effect with patient impressions.

Background: To promote sustained contributions by physicians to online health care communities, these platforms have introduced a content payment model that offers economic incentives for physicians’ online knowledge activities. However, the impact of these paid features on unpaid knowledge activities remains unexplored. Objective: This study investigated how the introduction of economic incentives in online medical communities affects physicians’ unpaid knowledge activities in the community. Methods: The data for this study were obtained from the Haodf Online platform in China, which has implemented paid scenarios for its science popularization function, providing economic benefits to physicians. The dataset, which comprises panel data, includes 7453 physicians who participated in both paid and unpaid knowledge contributions on the website. This study examined the impact of paid knowledge activities on physicians’ free knowledge contributions, focusing on dimensions including knowledge quantity, quality, and diversity. To address the timing discrepancies in physicians’ participation in paid activities, we used a quasi-experimental design that combined the approach of propensity score matching and multi-period difference in differences. Results: In the balance test results of the propensity score matching, the absolute values of the SDs of all matching variables were mostly <5% after matching, ensuring the accuracy of the results obtained from the difference in differences method. This study found that participation in paid knowledge activities had a positive spillover effect on physicians’ free knowledge contributions, which manifested in the increase in post quantity (473.1%; P<.001), article length (108%; P=.009), function word frequency (0.6%; P=.001), causal word frequency (0.2%; P<.001), and content information entropy (6.6%; P=.006). The paid function led to a decrease in the consistency between titles and content (–115.5%; P<.001). Conclusions: The findings of this study contribute to the existing literature on the impact of economic incentives in the medical context. For the platform, providing economic incentives to physicians can have positive significance in promoting the development of the platform’s knowledge ecosystem and can effectively encourage physicians to contribute to both paid and free knowledge activities. This study provides a valuable reference for the platform to introduce a paid knowledge model, which is beneficial to the sustainable development of the platform.

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.

BACKGROUND To promote sustained contributions by physicians to online health care communities, these platforms have introduced a content payment model that offers economic incentives for physicians’ online knowledge activities. However, the impact of these paid features on unpaid knowledge activities remains unexplored. OBJECTIVE This study investigated how the introduction of economic incentives in online medical communities affects physicians’ unpaid knowledge activities in the community. METHODS The data for this study were obtained from the Haodf Online platform in China, which has implemented paid scenarios for its science popularization function, providing economic benefits to physicians. The dataset, which comprises panel data, includes 7453 physicians who participated in both paid and unpaid knowledge contributions on the website. This study examined the impact of paid knowledge activities on physicians’ free knowledge contributions, focusing on dimensions including knowledge quantity, quality, and diversity. To address the timing discrepancies in physicians’ participation in paid activities, we used a quasi-experimental design that combined the approach of propensity score matching and multi-period difference in differences. RESULTS In the balance test results of the propensity score matching, the absolute values of the SDs of all matching variables were mostly <5% after matching, ensuring the accuracy of the results obtained from the difference in differences method. This study found that participation in paid knowledge activities had a positive spillover effect on physicians’ free knowledge contributions, which manifested in the increase in post quantity (473.1%; P<.001), article length (108%; P=.009), function word frequency (0.6%; P=.001), causal word frequency (0.2%; P<.001), and content information entropy (6.6%; P=.006). The paid function led to a decrease in the consistency between titles and content (–115.5%; P<.001). CONCLUSIONS The findings of this study contribute to the existing literature on the impact of economic incentives in the medical context. For the platform, providing economic incentives to physicians can have positive significance in promoting the development of the platform’s knowledge ecosystem and can effectively encourage physicians to contribute to both paid and free knowledge activities. This study provides a valuable reference for the platform to introduce a paid knowledge model, which is beneficial to the sustainable development of the platform.