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

Mindfulness metacognitive practice that can be performed in the workplace. Drawing on the theory of conservation of resources, we test a moderated mediating model of how and when employee mindfulness has a positive effect on work engagement. Via analysis of data from 311 employees from 83 teams at different times, this study investigates the relationship between employee mindfulness and work engagement as well as the moderating effect of team mindfulness and the mediating effect of recovery level. The results from this multi-wave field study show that the mindfulness of the individual employee has a positive influence on work engagement and that recovery level plays a mediating role. Team mindfulness positively moderates the relationship between individual mindfulness and work engagement. This conclusion may bridge the relationship between mindfulness and work engagement theory.

Background: In recent years, online pharmacies have been accepted by increasingly more consumers, and the prospects for online pharmacies are optimistic. This article explores the consumers’ satisfaction factors addressed in Business to Customer (B2C) online pharmacy reviews and analyzes the sentiments expressed in the reviews. The goal of this work is to help B2C online pharmacy enterprises identify consumers’ concerns, continuously improve the health services level. Methods: This article was based on the Latent Dirichlet Allocation (LDA) topic model. From a third-party platform-based B2C online pharmacy and a proprietary B2C online pharmacy (JD Pharmacy and J1.COM, respectively), 136,630 pieces of over-the-counter (OTC) drug review data posted from January 1, 2015 to December 31, 2018 were selected as samples and used to explore the satisfaction factors of B2C online pharmacy consumers regarding the entire drug purchasing process. Then, the sentiments expressed in the drug reviews were analyzed with SnowNLP. Result: Categorization of the 12 factors identified by LDA showed that 5 factors were related to logistics; these 5 factors, which also included the most drug reviews, made up 38.5% of the reviews. The number of factors related to drug prices was second, with 3 factors, and reviews of drug prices made up 25.5% of the reviews. Customer service and drug effects each had two related factors, and a smaller percentage of these reviews (13.95%) were related to drug effects. Consumers still maintain positive opinions of JD Pharmacy and J1.COM. However, some opinions on logistics and drug prices are expressed. Conclusion: The most important task for online pharmacies is to improve logistics. It is better to develop self-built logistics. Both types of B2C online pharmacies can improve consumer viscosity by implementing marketing strategies. With regard to customer service, focusing on improving employees’ service attitudes is necessary.

Introduction: Online mental health communities may provide new opportunities for rehabilitation for people with mental illness, so it is important to understand the factors that influence the continued use of online mental health communities by people with mental illness. Methods: From the perspective of empowerment, based on the theory of health self-efficacy and expectation confirmation, this study explored the mediating role of health self-efficacy and expectation confirmation in the empowerment process of patients in online mental health communities and users’ intention to continue using online mental health communities. To verify this model, we obtained 272 valid questionnaires. The SmartPLS 3.0 software was selected for model construction and empirical analysis. Results: Health self-efficacy completely mediates the relationship between the empowerment process (i.e., emotional support, information support, helping others and sharing experiences) and users’ intention to continue using an online mental health community. Expectation confirmation partially mediates the relationship between the empowerment process (i.e., information support and finding recognition) and users’ intention to continue using an online mental health community. Conclusion: The empowerment process is the main predictor of user health self-efficacy and expectation confirmation. This study has certain theoretical and practical significance for online mental health community research.

Suicide has become a serious problem, and how to prevent suicide has become a very important research topic. Social media provides an ideal platform for monitoring suicidal ideation. This paper presents an integrated model for multidimensional information fusion. By integrating the best classification models determined by single and multiple features, different feature information is combined to better identify suicidal posts in online social media. This approach was assessed with a dataset formed from 40,222 posts annotated by Weibo. By integrating the best classification model of single features and multidimensional features, the proposed model ((BSC + RFS)-fs, WEC-fs) achieved 80.61% accuracy and a 79.20% F1-score. Other representative text information representation methods and demographic factors related to suicide may also be important predictors of suicide, which were not considered in this study. To the best of our knowledge, this is the good try that feature combination and ensemble algorithms have been fused to detect user-generated content with suicidal ideation. The findings suggest that feature combinations do not always work well, and that an appropriate combination strategy can make classification models work better. There are differences in the information contained in different functional carriers, and a targeted choice classification model may improve the detection rate of suicidal ideation.