Exploring the Incentive Function of Virtual Academic Degrees in a Chinese Online Smoking Cessation Community: Qualitative Content Analysis.

BACKGROUND: Previous studies on online smoking cessation communities (OSCCs) have shown how such networks contribute to members' health outcomes from behavior influence and social support perspectives. However, these studies rarely considered the incentive function of OSCCs. One of the ways OSCCs motivate smoking cessation behaviors is through digital incentives. OBJECTIVE: This study aims to explore the incentive function of a novel digital incentive in a Chinese OSCC-the awarding of academic degrees-to promote smoking cessation. It specifically focuses on "Smoking Cessation Bar," an OSCC in the popular web-based Chinese forum Baidu Tieba. METHODS: We collected discussions about the virtual academic degrees (N= 1193) from 540 members of the "Smoking Cessation Bar." The time frame of the data set was from November 15, 2012, to November 3, 2021. Drawing upon motivational affordances theory, 2 coders qualitatively coded the data. RESULTS: We identified five key topics of discussion, including members' (1) intention to get virtual academic degrees (n=38, 2.47%), (2) action to apply for the degrees (n=312, 20.27%), (3) feedback on the accomplishment of goals (n=203, 13.19%), (4) interpersonal interaction (n=794, 51.59%), and (5) expression of personal feelings (n=192, 12.48%). Most notably, the results identified underlying social and psychological motivations behind using the forum to discuss obtaining academic degrees for smoking cessation. Specifically, members were found to engage in sharing behavior (n=423, 27.49%) over other forms of interaction such as providing recommendations or encouragement. Moreover, expressions of personal feelings about achieving degrees were generally positive. It was possible that members hid their negative feelings (such as doubt, carelessness, and dislike) in the discussion. CONCLUSIONS: The virtual academic degrees in the OSCC created opportunities for self-presentation for participants. They also improved their self-efficacy to persist in smoking cessation by providing progressive challenges. They served as social bonds connecting different community members, triggering interpersonal interactions, and inducing positive feelings. They also helped realize members' desire to influence or to be influenced by others. Similar nonfinancial rewards could be adopted in various smoking cessation projects to enhance participation and sustainability.

Rapid Review on Publicly Available Datasets for Health Misinformation Detection.

The proliferation of health misinformation in recent years has prompted the development of various methods for detecting and combatting this issue. This review aims to provide an overview of the implementation strategies and characteristics of publicly available datasets that can be used for health misinformation detection. Since 2020, a large number of such datasets have emerged, half of which are focused on COVID-19. Most of the datasets are based on fact-checkable websites, while only a few are annotated by experts. Furthermore, some datasets provide additional information such as social engagement and explanations, which can be utilized to study the spread of misinformation. Overall, these datasets offer a valuable resource for researchers working to combat the spread and consequences of health misinformation.

Predicting Family Implementation of Complementary and Alternative Medicine in Autism Online Communities.

Complementary and alternative medicine (CAM) is widely adopted by families with autistic children. This study aims to predict family caregivers' CAM implementation in Autism online communities. Dietary interventions were reported as a case study. We extracted behavioral (degree and betweenness), environmental (positive feedback and social persuasion), and personal features (language style) of family caregivers in online communities. The results of the experiment showed that random forests performed well in predicting families' tendency to implement CAM (AUC=0.887). It is promising to use machine learning to predict and intervene in the CAM implementation by family caregivers.

Understanding the Adoption of Dietary Interventions Within a Chinese Autism Online Community: A Diffusion of Innovations Perspective.

Dietary interventions are common but controversial treatments for autistic people. This study aims to understand the adoption of dietary interventions based on diffusion of innovations theory in the autism online community from four aspects: popularity, adoption process, the influence of opinion leaders, and post-adoption feedback. Our data was extracted from a Chinese autism community named Baidu Tieba autism forum. We applied a mixed-method including four analytical approaches: descriptive statistics for popularity analysis; machine learning models for automatic data classification and topic detection; social network analysis for exploring the influence of opinion leaders on the adoption phase; content analysis for revealing the family caregiver-reported feedback after adoption. Dietary interventions have become increasingly popular in the autism online community since 2018. Analysis of the adoption process revealed that family caregivers at different stages of adoption focused on different topics, and the number of interactions with opinion leaders had a significant effect on the highest level (p < .001) and stage span (p < .001) of health information adoption. According to findings from the feedback of family caregivers, the effects of dietary interventions varied from individuals with autism. Our study revealed the diffusion of unproven interventions, which is of great significance in promoting evidence-based practices.

Exploring the landscape, hot topics, and trends of bariatric metabolic surgery with machine learning and bibliometric analysis.

Background: This study aimed to analyze the landscape of publications on bariatric metabolic surgery through machine learning and help experts and scholars from various disciplines better understand bariatric metabolic surgery's hot topics and trends. Methods: In January 2021, publications indexed in PubMed under the Medical Subject Headings (MeSH) term 'Bariatric Surgery' from 1946 to 2020 were downloaded. Python was used to extract publication dates, abstracts, and research topics from the metadata of publications for bibliometric evaluation. Descriptive statistical analysis, social network analysis (SNA), and topic modeling with latent Dirichlet allocation (LDA) were used to reveal bariatric metabolic surgery publication growth trends, landscape, and research topics. Results: A total of 21,798 records of bariatric metabolic surgery-related literature data were collected from PubMed. The number of publications indexed to bariatric metabolic surgery had expanded rapidly. Obesity Surgery and Surgery for Obesity and Related Diseases are currently the most published journals in bariatric metabolic surgery. The bariatric metabolic surgery research mainly included five topics: bariatric surgery intervention, clinical case management, basic research, body contour, and surgical risk study. Conclusion: Despite a rapid increase in bariatric metabolic surgery-related publications, few studies were still on quality of life, psychological status, and long-term follow-up. In addition, basic research has gradually increased, but the mechanism of bariatric metabolic surgery remains to be further studied. It is predicted that the above research fields may become potential hot topics in the future.

Do Informational and Emotional Elements Differ between Online Psychological and Physiological Disease Communities in China? A Comparative Study of Depression and Diabetes.

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.

Privacy Policy Compliance of Chronic Disease Management Apps in China: Scale Development and Content Evaluation.

BACKGROUND: With the development of mobile health (mHealth), chronic disease management apps have brought not only the possibility of reducing the burden of chronic diseases but also huge privacy risks to patients' health data. OBJECTIVE: The purpose of the study was to analyze the extent to which chronic disease management apps in China comply with the Personal Information Security Specification (PI Specification). METHODS: The compliance of 45 popular chronic disease management apps was evaluated from the perspective of the information life cycle. To conduct a fine-grained evaluation, a scale based on the PI Specification was developed. Finally, 6 level 1 indicators, 22 level 2 indicators, and 61 level 3 indicators were defined. RESULTS: There were 33/45 apps (73%) with a privacy policy, and the average score of these apps was 40.4 out of 100. Items of level 1 indicators with high scores included general characteristics (mean 51.9% [SD 28.1%]), information collection and use (mean 51.1% [SD 36.7%]), and information sharing and transfer (mean 50.3% [SD 33.5%]). Information storage and protection had the lowest compliance with PI Specification (mean 29.4% [SD 32.4%]). Few personal information (PI) controllers have stated how to handle security incidents, including security incident reporting (7/33, 21%), security incident notification (10/33, 30%), and commitment to bear corresponding legal responsibility for PI security incidents (1/33, 3%). The performance of apps in the stage of information destruction (mean 31.8% [SD 40.0%]) was poor, and only 21% (7/33) apps would notify third parties to promptly delete PI after individuals cancelled their accounts. Moreover, the scoring rate for rights of PI subjects is generally low (mean 31.2% [SD 35.5%]), especially for obtaining copies of PI (15%) and responding to requests (25%). CONCLUSIONS: Although most chronic disease management apps had a privacy policy, the total compliance rate of the policy content was low, especially in the stage of information storage and protection. Thus, the field has a long way to go with regard to compliance around personal privacy protection in China.

Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study.

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.