Recently, health-related social media services, especially online health communities, have rapidly emerged. Patients with various health conditions participate in online health communities to share their experiences and exchange healthcare knowledge. Exploring hot topics in online health communities helps us better understand patients’ needs and interest in health-related knowledge. However, the statistical topic analysis employed in previous studies is becoming impractical for processing the rapidly increasing amount of online data. Automatic topic detection based on document clustering is an alternative approach for extracting health-related hot topics in online communities. In addition to the keyword-based features used in traditional text clustering, we integrate medical domain-specific features to represent the messages posted in online health communities. Three disease discussion boards, including boards devoted to lung cancer, breast cancer and diabetes, from an online health community are used to test the effectiveness of topic detection. Experiment results demonstrate that health-related hot topics primarily include symptoms, examinations, drugs, procedures and complications. Further analysis reveals that there also exist some significant differences among the hot topics discussed on different types of disease discussion boards.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Purpose: The purpose of this paper is to examine how the usage of enterprise social media (ESM) affects eventual employee turnover. Design/methodology/approach: This study developed a theoretical model based on the proposition that different ESM usage behaviors (utilitarian use, hedonic use and social use) have different effects on employee turnover, and job type and job level can moderate the effect of ESM usage on turnover. The model was examined empirically using 1,791 employee samples from a large high-tech manufacturing enterprise deploying ESM. Findings: The results indicate that the utilitarian and social use of ESM has negative effects on turnover, but the hedonic use of ESM has positive effects on turnover. Furthermore, for employees working in different job types and job levels, there are significant differences concerning the effect of ESM usage on their turnover. Practical implications: ESM managers should encourage employees to use ESM for utilitarian needs and social support but restrict excessive use of ESM for leisure. In addition, different ESM use policies depending upon job types and job levels could be adopted to retain valuable employees. Originality/value: Few studies have focused on how usage of ESM affects eventual employee turnover. Given the lack of theoretical research and empirical evidence, the authors developed a theoretical model and conducted an empirical study to fill the research gap.

Background: In recent years, people with mental health problems are increasingly using online social networks to receive social support. For example, in online depression communities, patients can share their experiences, exchange valuable information, and receive emotional support to help them cope with their disease. Therefore, it is critical to understand how patients with depression develop online social support networks to exchange informational and emotional support.Objective: Our aim in this study was to investigate which user attributes have significant effects on the formation of informational and emotional support networks in online depression communities and to further examine whether there is an association between the two social networks.Methods: We used social network theory and constructed exponential random graph models to help understand the informational and emotional support networks in online depression communities. A total of 74,986 original posts were retrieved from 1077 members in an online depression community in China from April 2003 to September 2017 and the available data were extracted. An informational support network of 1077 participant nodes and 6557 arcs and an emotional support network of 1077 participant nodes and 6430 arcs were constructed to examine the endogenous (purely structural) effects and exogenous (actor-relation) effects on each support network separately, as well as the cross-network effects between the two networks.Results: We found significant effects of two important structural features, reciprocity and transitivity, on the formation of both the informational support network (r=3.6247, P<.001, and r=1.6232, P<.001, respectively) and the emotional support network (r=4.4111, P<.001, and r=0.0177, P<.001, respectively). The results also showed significant effects of some individual factors on the formation of the two networks. No significant effects of homophily were found for gender (r=0.0783, P=.20, and r=0.1122, P=.25, respectively) in the informational or emotional support networks. There was no tendency for users who had great influence (r=0.3253, P=.05) or wrote more posts (r=0.3896, P=.07) or newcomers (r=–0.0452, P=.66) to form informational support ties more easily. However, users who spent more time online (r=0.6680, P<.001) or provided more replies to other posts (r=0.5026, P<.001) were more likely to form informational support ties. Users who had a big influence (r=0.8325, P<.001), spent more time online (r=0.5839, P<.001), wrote more posts (r=2.4025, P<.001), or provided more replies to other posts (r=0.2259, P<.001) were more likely to form emotional support ties, and newcomers (r=–0.4224, P<.001) were less likely than old-timers to receive emotional support. In addition, we found that there was a significant entrainment effect (r=0.7834, P<.001) and a nonsignificant exchange effect (r=–0.2757, P=.32) between the two networks.Conclusions: This study makes several important theoretical contributions to the research on online depression communities and has important practical implications for the managers of online depression communities and the users involved in these communities.

Online medical consultation (OMC) allows doctors and patients to communicate with each other in an online synchronous or asynchronous setting. Unlike face-to-face consultations in which doctors are only passively chosen by patients with appointments, doctors engaging in voluntary online consultation have the option of choosing patients they hope to treat when faced with a large number of online questions from patients. It is necessary to characterize doctors’ preferences for patient selection in OMC, which can contribute to their more active participation in OMC services. We proposed to exploit a bipartite graph to describe the doctor–patient interaction and use an exponential random graph model (ERGM) to analyze the doctors’ preferences for patient selection. A total of 1404 doctor–patient consultation data retrieved from an online medical platform in China were used for empirical analysis. It was found that first, mildly ill patients will be prioritized by doctors, but the doctors with more professional experience may be more likely to prefer more severely ill patients. Second, doctors appear to be more willing to provide consultation services to patients from urban areas, but the doctors with more professional experience or from higher-quality hospitals give higher priority to patients from rural and medically underserved areas. Finally, doctors generally prefer asynchronous communication methods such as picture/text consultation, while the doctors with more professional experience may be more willing to communicate with patients via synchronous communication methods, such as voice consultation or video consultation.

This study focuses on employee involvement in enterprise social media (ESM) and the impact of ESM on job performance. Few studies found empirical support for this perspective due to limited sample size either the difficulty of accessing the data on user behavior in ESM and their job performance. We addressed this research gap through a data-sharing agreement with a large, high-tech manufacturing enterprise with internal social media and conducted a cooperative study on the impact of employee behaviors involved in ESM on their job performance. We divided online activities on ESM into information-sharing behaviors, information-seeking behaviors and general usage behaviors and then employed hierarchical regression analysis to investigate how the various usage behaviors of employees on ESM affect their job performance significantly. We find that information-seeking behaviors on ESM have positive effects on job performance, but information-sharing behaviors on ESM have negative effects on job performance. Overall, the usage of social media within enterprise social media use could help improve employee job performance. Moreover, to test whether the timing of ESM usage (during working hours or off-work hours) may affect employee job performance, we add a variety of moderator variables into the model. The results show that information-sharing behaviors, information-seeking behaviors and general usage behaviors during working hours negatively moderate the relationship between ESM usage and job performance. The findings have valuable managerial implications for the use of ESM.