The prevalence of depression has increased significantly over the past few years both in developed and developing countries. However, many people with symptoms of depression still remain untreated or undiagnosed. Social media may be a tool to help researchers and clinicians to identify and support individuals who experience depression. More than 394,000,000 postings were collected from China’s most popular social media website, Sina Weibo. 1000 randomly selected depression-related postings was coded and analyzed to learn the themes of these postings, and a text classifier was built to identify the postings indicating depression. The identified depressed users were compared with the general population on demographic characteristics, diurnal patterns, and patterns of emoticon usage. We found that disclosure of depression was the most popular theme; depression displayers were more engaged with social media compared to non-depression displayers, the depression postings showed geographical variations, depression displayers tended to be active during periods of leisure and sleep, and depression displayers used negative emoticons more frequently than non-depression displayers. This study offers a broad picture of depression references on China’s social media, which may be cost effectively developed to detect and help individuals who may suffer from depression disorders.

Negative emotions are prevalent in the online depression community (ODC), which potentially puts members at risk, according to the theory of emotional contagion. However, emotional contagion in the ODC has not been confirmed. The generalized estimating equation (GEE) was used to verify the extent of emotional contagion using data from 1548 sample users in China’s popular ODC. During interaction, the emotional themes were analyzed according to language use. The diurnal patterns of the interaction behaviors were also analyzed. We identified the susceptible groups and analyzed their characteristics. The results confirmed the occurrence of emotional contagion in ODC, that is, the extent to which the user’s emotion was affected by the received emotion. Our study also found that when positive emotional contagion occurred, the replies contained more hopefulness, and when negative emotional contagion occurred, the replies contained more hopelessness and fear. Second, positive emotions were easier to spread, and people with higher activity in ODC were more susceptible. In addition, nighttime was an active period for user interaction. The results can help community managers and support groups take measures to promote the spread of positive emotions and reduce the spread of negative emotions.

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Drawing on consumer inferences theory, our research examines the spillover effects of positive reviews from a multi‐generation product perspective and mainly explores the moderating role of time interval and price growth rate. The results show the spillover effects of positive reviews would be affected or even changed by entering timing and prices of new products. We find that the number of a previous‐generation product's positive reviews has an inverted U‐shaped relation with sales performance of a next‐generation product when the time interval between the two generational products is short. Further, we reveal that this relation is positive when the time interval between the two generations is long. Moreover, the positive influence of the number of a previous‐generation product's positive reviews on the sales performance of a next‐generation product will be weaker as the price growth rate of a next‐generation product rises. We test our hypotheses using online sales data of the Chinese mobile market. Our empirical analysis contributes to the research of multi‐generational products and online reviews, and enriches the consumer inferences theory.

Introduction: Online depression-focused communities (ODCs) are popular avenues that help people cope with depression. However, to the best of our knowledge, research on online behavior and differences among users from managed and unmanaged ODCs has not been explored. Methods: We collected data from the most popular managed depression-focused community (MDC) and unmanaged depression-focused community (UDC) in China. Text classifiers were built using deep-learning methods to identify social support (ie, informational and emotional support) and companionship expressed in the posts of these communities. Based on the content of their posts, community users were clustered into supporters and ordinary members. Econometrics was used to analyze the factors that influence supporters' contributions and ordinary members' participation in MDCs and UDCs. Results: Community response has a positive impact on supporters' social support and time span in the UDC, but this impact is not significant in the MDC. Supporters expressing positive emotions provide more social support, and they are more willing to serve in the MDC. Supporters expressing negative emotions tend to have longer engagement with the UDC. In addition, community response has a positive effect on ordinary members' participation in both communities, and this effect is greater in the UDC. Ordinary members expressing positive emotions are more active in the MDC, and ordinary members expressing negative emotions are more active in the UDC. Conclusion:This study improves the understanding of users' online behaviors in ODCs, provides decision-making support for designers and managers of ODCs, and provides information that can be used to help improve aid for people with depression provided by community and mental health professionals.