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.

Understanding online patient dissatisfaction is essential for improving the quality of healthcare services, patient satisfaction, and physician career development. This study is the first to apply the structural topic model to patient satisfaction research based on patient online reviews from a mobile health communication platform, revealing eight negative topics of patient concerns. These topics include under-explored areas such as “go to the hospital for check-ups,” “incomplete counseling,” and “language expression.” Additionally, we incorporated the doctor's title as a covariate in the model to examine how specific topics varied across different conditions. The results indicated that higher-titled doctors were more likely to receive complaints about the cost of treatment and whether the question was answered, whereas lower-titled doctors were more likely to receive complaints related to physician's knowledge, incomplete counseling, and response speed. This study not only enhances our understanding of mobile health services but also provides targeted insights for healthcare providers to improve their services, thereby contributing to the advancement of patient-centered care.

The public demand for popular science knowledge regarding health is increasing, and physicians’ popular science practices on online medical platforms are becoming frequent. Few studies have been conducted to address the relationship between specific characteristics of popular science articles by physicians and their performance. This study explored the impact of the characteristics of popular science articles on physicians’ performance based on the elaboration likelihood model (ELM) from the central path (topic focus and readability) and the peripheral path (form diversity). Data on four diseases, namely, lung cancer, brain hemorrhage, hypertension, and depression, were collected from an online medical platform, resulting in relevant personal data from 1295 doctors and their published popular science articles. Subsequently, the independent variables were quantified using thematic analysis and formula calculation, and the research model and hypotheses proposed in this paper were verified through empirical analysis. The results revealed that the topic focus, readability, and form diversity of popular science articles by physicians had a significant positive effect on physicians’ performance. This study enriches the research perspective on the factors influencing physicians’ performance, which has guiding implications for both physicians and platforms, thereby providing a basis for patients to choose physicians and enabling patients to receive popular science knowledge regarding health in an effective manner.

Online health counseling (OHC) is increasingly important in modern healthcare. This development has attracted considerable attention from researchers. However, the reality of the lack of physician–patient communication and dissatisfaction with online health services remains prevalent, and more research is needed to raise awareness about important issues related to OHC services, especially in terms of patient satisfaction and depth of interaction (i.e., the product of the number of interactions and the relevance of the content). This study constructs an empirical model to explore the relationship between physicians’ online writing language style (inclusive language and emojis), depth of physician–patient interactions, and patient satisfaction. The study obtained 5064 online health counseling records from 337 pediatricians and analyzed them using text mining and empirical methods. The results showed that physicians’ inclusive language (β = 0.3198, p < 0.05) and emojis (β = 0.6059, p < 0.01) had a positive impact on patient satisfaction. In addition, the depth of the physician–patient interaction partially mediated this effect. This study promotes a better understanding of the mechanisms of physician–patient interactions in online settings and has important implications for how online physicians and platforms can better provide online healthcare services.

The convenience of mobile devices has driven the widespread use of voice technology in mobile health communication, significantly improving the timeliness of online service. However, the issue of listening to therapeutic content, which requires great cognitive effort and may exceed the patient's information processing capacity (i.e., information overload), is of concern. Based on information processing theory, this study reports how online physicians' voice characteristics (pitch range and filled pauses) affect patient satisfaction. We obtained 10,585 mobile voice consultation records of 1,416 doctors from China's largest mHealth platform and analyzed them using audio mining and empirical methods. Results showed that pitch range (β = 0.0539, p < .01) and filled pauses (β = 0.0365, p < .01) in doctors' voice positively influenced online patient satisfaction. However, the effect of filled pauses becomes weaker for patients with higher health literacy and higher disease risk. This suggests that there is heterogeneity in the way different patients process audio information. This study provides important insights for guiding online physician behaviors, enhancing patient satisfaction, and improving mobile health platform management.