Background The COVID-19 infodemic has been disseminating rapidly on social media and posing a significant threat to people’s health and governance systems. Objective This study aimed to investigate and analyze posts related to COVID-19 misinformation on major Chinese social media platforms in order to characterize the COVID-19 infodemic. Methods We collected posts related to COVID-19 misinformation published on major Chinese social media platforms from January 20 to May 28, 2020, by using PythonToolkit. We used content analysis to identify the quantity and source of prevalent posts and topic modeling to cluster themes related to the COVID-19 infodemic. Furthermore, we explored the quantity, sources, and theme characteristics of the COVID-19 infodemic over time. Results The daily number of social media posts related to the COVID-19 infodemic was positively correlated with the daily number of newly confirmed (r=0.672, P<.01) and newly suspected (r=0.497, P<.01) COVID-19 cases. The COVID-19 infodemic showed a characteristic of gradual progress, which can be divided into 5 stages: incubation, outbreak, stalemate, control, and recovery. The sources of the COVID-19 infodemic can be divided into 5 types: chat platforms (1100/2745, 40.07%), video-sharing platforms (642/2745, 23.39%), news-sharing platforms (607/2745, 22.11%), health care platforms (239/2745, 8.71%), and Q&A platforms (157/2745, 5.72%), which slightly differed at each stage. The themes related to the COVID-19 infodemic were clustered into 8 categories: “conspiracy theories” (648/2745, 23.61%), “government response” (544/2745, 19.82%), “prevention action” (411/2745, 14.97%), “new cases” (365/2745, 13.30%), “transmission routes” (244/2745, 8.89%), “origin and nomenclature” (228/2745, 8.30%), “vaccines and medicines” (154/2745, 5.61%), and “symptoms and detection” (151/2745, 5.50%), which were prominently diverse at different stages. Additionally, the COVID-19 infodemic showed the characteristic of repeated fluctuations. Conclusions Our study found that the COVID-19 infodemic on Chinese social media was characterized by gradual progress, videoization, and repeated fluctuations. Furthermore, our findings suggest that the COVID-19 infodemic is paralleled to the propagation of the COVID-19 epidemic. We have tracked the COVID-19 infodemic across Chinese social media, providing critical new insights into the characteristics of the infodemic and pointing out opportunities for preventing and controlling the COVID-19 infodemic.

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Disease-specific online health communities provide a convenient and common platform for patients to share experiences, change information, provide and receive social support. This study aimed to compare differences between online psychological and physiological disease communities in topics, sentiment, participation, and emotional contagion patterns using multiple methods as well as to discuss how to satisfy the users’ different informational and emotional needs. We chose the online depression and diabetes communities on the Baidu Tieba platform as the data source. Topic modeling and theme coding were employed to analyze discussion preferences for various topic categories. Sentiment analysis was used to identify the sentiment polarity of each post and comment. The social network was used to represent the users’ interaction and emotional flows to discover the differences in participation and emotional contagion patterns between psychological and physiological disease communities. The results revealed that people affected by depression focused more on their symptoms and social relationships, while people affected by diabetes were more likely to discuss treatment and self-management behavior. In the depression community, there were obvious interveners spreading positive emotions and more core users in the negative emotional contagion network. In the diabetes community, emotional contagion was less prevalent and core users in positive and negative emotional contagion networks were basically the same. The study reveals insights into the differences between online psychological and physiological disease communities, providing a greater understanding of the users’ informational and emotional needs expressed online. These results are helpful for society to provide actual medical assistance and deploy health interventions based on disease types.

Electronic health records (EHRs)-related publications grow rapidly. It is helpful for experts and scholars in various disciplines to better understand the research landscape, hot topics, and trends of EHRs. We collected 13,438 records of EHRs research literature bibliometrics data from the Web of Science. We mainly performed the descriptive statistical analysis, social network analysis, and topic modeling with lda2vec to reveal the publications growth trend, research subjects distribution, and topics of EHRs researches. The EHRs research mainly included four topics: (i) population health, disease risk prediction, and primary care; (ii) technology, ethics, and privacy security; (iii) quality improvement, user acceptance, and engagement; (iv) information systems application and impact. EHRs have gone through the establishment, utilization, and high-level development and application. Research topics emerging in recent years have primarily focused on the social determinants of health, the application of deep learning models, the development and utilization of the patient portal, the mining of explicit and tacit knowledge, and the provision of decision support.