JMIR infodemiology最新文献

{"title":"Measuring Safety Risks in Online Drug Sales: Empirical Study Based on Complex Network Theory.","authors":"Rong Jiang, Bingke Xing, Lixin Shu","doi":"10.2196/86876","DOIUrl":"10.2196/86876","url":null,"abstract":"<p><strong>Background: </strong>With the rapid development of pharmaceutical e-commerce, online drug sales have led to additional safety risks, while providing convenient services to the public. These risks pose challenges to public health security and regulatory systems.</p><p><strong>Objective: </strong>This study aimed to quantitatively measure the safety risks of online drug sales.</p><p><strong>Methods: </strong>We collected data on cases of violation in online drug sales from government websites and established a complex network model consisting of 116 nodes and 152 edges for the safety risks in online drug sales based on complex network theory. We explored network topological properties through complex network characteristic indicators (eg, average path length, diameter, degree, and degree distribution). In addition, we followed the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to identify key risk factors in online drug sales safety, calculate the relative nearness degree value of each risk node, and rank them accordingly.</p><p><strong>Results: </strong>Data on 458 cases of violation were collected. The key risk nodes in the complex network model for safety risks in online drug sales were identified as the failure to (1) strictly perform prescription review and dispensing duties, (2) issue prescriptions through internet hospitals, (3) establish drug procurement and acceptance records, and (4) establish drug sales records, in addition to (5) consumers failing to provide prescriptions.</p><p><strong>Conclusions: </strong>Based on the risk measurement results, regulatory authorities can formulate targeted risk prevention and control measures for key risks, such as failure to strictly perform prescription review and dispensing duties and consumers failing to provide prescriptions. By building a tighter and stronger regulatory network for online drug sales and improving regulatory efficiency, this approach could maintain order in the online drug sales market and enhance the overall level of public health security.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e86876"},"PeriodicalIF":4.0,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13389469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148400270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Reported Tianeptine Experiences on Reddit: Natural Language Processing-Assisted Qualitative Study.","authors":"Christopher J Counts, Anthony V Spadaro, Sahithi Lakamana, Abeed Sarker, Rachel Wightman, Jennifer Love, Diane Calello, Jeanmarie Perrone","doi":"10.2196/86683","DOIUrl":"10.2196/86683","url":null,"abstract":"<p><strong>Unlabelled: </strong>This study, using natural language processing and manual thematic analysis of Reddit posts, revealed a rapid rise in discussions about tianeptine, with posts frequently reporting dependence, withdrawal, and coingestion with other unregulated substances, highlighting tianeptine as an emerging public health concern.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e86683"},"PeriodicalIF":4.0,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13340079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148400278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mpox on Instagram: Content Analytic Study.","authors":"Elizabeth E Havron, Kylee Chenault, Danny Valdez, Rebecca F Houghton, Eric R Walsh-Buhi","doi":"10.2196/85379","DOIUrl":"10.2196/85379","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mpox was declared a public health emergency of international concern in 2022. Instagram is widely used by age groups and communities disproportionately affected by mpox; yet platform-specific evidence on mpox information characteristics and engagement remains limited.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of this study is to characterize sources, content, and engagement features of mpox-related Instagram posts, to describe prevention and treatment framing, and to compare the top 10% most-liked posts with the remaining corpus.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We retrieved English-language public Instagram posts via CrowdTangle containing \"mpox\" or \"monkeypox\" dated May 5, 2022, to January 17, 2023 (initial N=18,616). Using a pretested, deductive codebook adapted from prior Instagram health studies, 2 coders completed 2 pilot rounds; variables with low agreement were excluded. A randomized analytic sample of 1000 posts was coded for source type, content features, and prevention/treatment framing. Descriptive statistics were computed. For engagement contrasts, we compared the top 10% most-liked posts with the bottom 90% using tests of differences in independent proportions (mean differences [MD] with P values).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Most posts originated from organizations (760/1000, 76%) versus individuals (240/1000, 24%). Organizational sources most commonly included businesses (436/760, 57.4%) and news/media outlets (401/760, 52.8%); government (174/760, 22.9%), nonprofits (131/760, 17.3%), and health care organizations (70/760, 9.2%) were less frequent. About one-third of posts cited a source (344/1000, 34.4%), most often the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC)/other federal entity. Posts predominantly used illustrated images/infographics (827/1000, 82.7%); photos appeared in 47.3% (473/1000) and videos in 12.4% (124/1000) of posts. Prevention content appeared in 38.4% (384/1000) of posts, most commonly vaccination (684/1000, 68.5% of prevention posts), followed by avoiding close contact (145/1000, 14.5%), avoiding contact with objects (83/1000, 8.3%), abstaining from sexual activity (76/1000, 7.6%), and condom use (13/1000, 1.3%); 28.9% (289/1000) of prevention posts noted barriers. Treatment mentions were uncommon (25/1000, 2.5% traditional biomedical; 2/1000, 0.2% alternative). Compared with the bottom 90%, the top 10% most-liked posts (1) were more likely to originate from public figures/celebrities among individuals (MD=-0.591; P&lt;.001) and from businesses (MD=-0.299; P&lt;.001) or news/media (MD=-0.350; P&lt;.001) among organizations; (2) were less likely to be from government (P&lt;.001) , nonprofit (P=.006), or health care organizations (P=.005); and (3) more often included nonmoving images (MD=-0.119; P=.024), visible lesion depictions (MD=-0.081; P=.035), prevalence mentions (MD=-0.180; P&lt;.001), and citations (MD=-0.162; P=.001).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusio","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e85379"},"PeriodicalIF":4.0,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13318204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tetanus Health Information on YouTube and TikTok: Cross-Sectional Analysis of Quality, Reliability, and Public Health Implications.","authors":"Rui Huang, Yajing Shen, Guangqing Zou, Jun Zhou, Xiaolu Deng, Qian Li, Xiaoxiong Chen","doi":"10.2196/85397","DOIUrl":"10.2196/85397","url":null,"abstract":"<p><strong>Background: </strong>Tetanus is a severe but vaccine-preventable neurological disease that remains a public health concern, especially in resource-limited settings. As social media becomes an important source of health information, concerns persist regarding the quality and reliability of tetanus-related content online.</p><p><strong>Objective: </strong>This study aimed to evaluate the quality, reliability, and thematic characteristics of tetanus-related videos on YouTube and TikTok and to examine the relationship between engagement metrics and information quality.</p><p><strong>Methods: </strong>A cross-sectional study was conducted using tetanus-related videos retrieved from YouTube and TikTok on August 1, 2025. The top 100 eligible videos from each platform were included (n=200). Video quality was assessed using the Global Quality Scale, whereas reliability and transparency were evaluated using the modified DISCERN tool and the Journal of the American Medical Association benchmark criteria. A thematic content analysis based on predefined coding categories was also performed. Video characteristics, source types, and engagement metrics were also collected. Spearman correlation analysis was used to examine associations between engagement indicators and quality scores.</p><p><strong>Results: </strong>YouTube videos showed significantly higher quality and reliability than TikTok videos, with higher median Global Quality Scale, modified DISCERN, and Journal of the American Medical Association scores (all P<.001). Compared with TikTok, YouTube videos more frequently discussed symptoms (92% vs 81%, P=.02), prevention (95% vs 78%, P<.001), treatment (88% vs 70%, P=.002), and wound management (77% vs 38%, P<.001). Lower-quality videos commonly contained incomplete prevention information, vague symptom descriptions, and limited source attribution. Videos produced by official medical organizations and professional health care creators generally achieved higher quality scores. Although engagement indicators were strongly correlated with each other, their associations with informational quality were relatively limited.</p><p><strong>Conclusions: </strong>YouTube provided more comprehensive and reliable tetanus-related information than TikTok, although content quality on both platforms remained inconsistent. Greater involvement from health care professionals and clearer evidence-based communication may help improve the quality of health information shared on social media platforms.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e85397"},"PeriodicalIF":4.0,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13292983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148320808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Information Behavior Regarding COVID-19 Vaccination and Its Association With Vaccination Behavior Based on Cluster Analysis of User Groups: Cross-Sectional Study.","authors":"Lea Liebner, Birgit Babitsch, Lisa Schmidt","doi":"10.2196/82221","DOIUrl":"10.2196/82221","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The COVID-19 pandemic highlighted the importance of effective health communication and reliable information for crisis management, particularly following the introduction of vaccinations. Varied attitudes toward COVID-19 vaccination and an overwhelming amount of online information complicated communication and pandemic management. Previous studies have often focused on general vaccination behavior and its correlation with vaccination attitudes, establishing a link between information-seeking and vaccination decisions. However, there is insufficient analysis distinguishing specific user groups based on their actual online information behavior regarding COVID-19 vaccination and examining its correlation with vaccination behavior.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to fill this research gap by identifying user groups based on their information behavior and investigating its influence on vaccination uptake.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;As part of the \"Internetnutzung zur COVID-19-Impfung\" (INCOVI) study, 1000 individuals in Germany were surveyed online (November 26 to December 8, 2021) regarding their internet usage related to COVID-19 vaccination. A hierarchical cluster analysis was conducted to identify user groups. Logistic regression analyses were then used to explore correlations among the user groups and their demographic characteristics, readiness to vaccinate, knowledge of vaccination, and health literacy. Additionally, a logistic regression analysis was performed to identify the influence of user groups and other factors on vaccination behavior.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 3 user groups were identified: frequent and critical information evaluators (454/778, 58.4%), who primarily relied on official information sources, exhibited a higher level of health literacy, and were older than the other groups; infrequent and passive recipients (222/778, 28.5%), who rarely sought information actively and were younger than the other groups; and frequent and multichannel, interaction-focused users (102/778, 13.1%), who actively searched across multiple channels and engaged in information exchange. Notably, the user groups did not significantly differ in knowledge or willingness to vaccinate. User group affiliation, knowledge, and health literacy did not significantly influence vaccination behavior. The strongest predictor of vaccination was preexisting willingness to vaccinate. Additionally, women were more likely to be vaccinated than men, and individuals with medium or higher education levels were 6-11 times more likely to be vaccinated compared to those with only a basic level of education.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Segmenting the population into different user groups allows for more targeted communication tailored to the specific needs and beliefs of each group. Because these groups stem from observable usage patterns, they constitute a transferable framework for other health t","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e82221"},"PeriodicalIF":4.0,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13263656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148058640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early Depression Detection in Social Media: Monitoring of Individual Nighttime Dynamics and Large Language Model Analysis.","authors":"Bicheng Yu, Zhichang Zhang, Lulu Ma, Jiongfu Cai, Yuanyuan Zhang","doi":"10.2196/87138","DOIUrl":"10.2196/87138","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Depression has become a major global public health challenge, and early intervention is critical for improving patient outcomes. Current depression detection techniques based on social media data (traditional risk detection) rely heavily on users' complete historical information, which cannot meet the timeliness requirements of early intervention. This underscores the need for early risk detection (ERD) methods emphasizing early-stage, real-time warning. However, existing ERD studies have notable limitations such as (1) they overlook temporal activity patterns hidden in posting time stamps, missing vital warning signals; and (2) they depend on static templates or resource-intensive sequence models, resulting in limited interpretability and inefficient use of early data, ultimately constraining their clinical applicability for early intervention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to develop an efficient, reliable, and interpretable ERD model. The core objectives are to extract temporal activity patterns features from posting time stamps, thereby enriching feature dimensions for risk detection; to leverage large language models (LLMs) for improved text filtering precision and depression-related factor analysis; and to achieve accurate early detection of depression to support clinical intervention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We propose the Monitoring of Individual Nighttime Dynamics (MIND) and LLM analysis model, which integrates two key innovations: (1) circadian activity dynamics: posting time stamps are transformed into temporal activity patterns, analyzing fluctuations in posting frequency and timing to derive sleep-related features, thereby compensating for the limitations of text-only approaches, and (2) LLM depression profiler: LLMs are used for dynamic text filtering, automatically removing irrelevant noise and focusing on potential depression-related cues. Based on LLM semantic understanding, latent depression risk factors are identified, enhancing interpretability for clinical treatment and robustness to noise.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Experiments on the eRisk2017 (data source: Reddit [Reddit Inc]) benchmark dataset demonstrated that MIND significantly outperformed existing baseline models in early detection sensitivity, specificity, and accuracy. By combining sleep-related features with text analysis, the model achieved interpretable, traceable predictions that can support clinical treatment. ALL relevant experimental code is publicly available.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The MIND model combines temporal activity pattern features with LLM-based text analysis, addressing the challenges of poor interpretability and inefficient use of early-stage data in existing ERD methods. It significantly enhances early detection performance, offering a new paradigm for applying social media data in ERD, thereby enabling earlier intervention and reducing the public health burden of depr","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e87138"},"PeriodicalIF":4.0,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13263661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148058533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Japanese-Language AI Agent System for Human Papillomavirus Vaccine Infoveillance and Public Communication: Development and Feasibility Evaluation.","authors":"Junyu Liu, Siwen Yang, Dexiu Ma, Qian Niu, Zequn Zhang, Momoko Nagai-Tanima, Tomoki Aoyama","doi":"10.2196/90295","DOIUrl":"10.2196/90295","url":null,"abstract":"<p><strong>Background: </strong>Human papillomavirus (HPV) vaccine hesitancy remains a significant public health challenge in Japan, where proactive vaccination recommendations were suspended between 2013 and 2021. The resulting information gap between medical institutions and vaccine-hesitant populations is exacerbated by misinformation on social media platforms. Traditional public health communication strategies cannot address individual queries while simultaneously monitoring population-level discourse.</p><p><strong>Objective: </strong>This study aimed to develop and conduct a feasibility evaluation of a dual-purpose artificial intelligence agent system that delivers verified HPV vaccine information to the public through a conversational interface while generating infoveillance reports for medical institutions based on user interactions and social media discourse.</p><p><strong>Methods: </strong>We implemented a system with 3 components: a vector database integrating 139,803 documents, including academic papers, Japanese government sources, news media, and social media posts; a retrieval-augmented generation chatbot using a ReAct agent architecture with iterative multitool orchestration across 5 specialized knowledge sources; and an automated report generation system with modules for news analysis, research synthesis, social media sentiment analysis, including stance classification and topic modeling, and user interaction pattern identification. System performance was assessed using both automated and manual evaluation protocols on a scale from 0 to 5.</p><p><strong>Results: </strong>The entire system functioned as expected. For single-turn evaluation, the chatbot achieved mean scores of 4.83 (SD 0.67; 95% CI 4.71-4.93) for relevance, 4.89 (SD 0.53; 95% CI 4.79-4.97) for routing, 4.50 (SD 1.29; 95% CI 4.27-4.70) for reference quality, 4.90 (SD 0.62; 95% CI 4.78-4.99) for correctness, and 4.88 (SD 0.54; 95% CI 4.78-4.96) for professional identity, with an overall mean of 4.80. Multiturn evaluation yielded higher mean scores: 4.94 for context memory (SD 0.25; 95% CI 4.84-5.00) and an overall mean of 4.98, with topic centering and identity achieving 5.00. The report generation system achieved high scores across all sections: 4.83 for completeness (SD 0.37; 95% CI 4.73-4.94), 4.88 for correctness (SD 0.33; 95% CI 4.77-4.96), and 4.12 for helpfulness (SD 0.48; 95% CI 3.98-4.27). Reference validity achieved perfect scores (5.00) across all periods, with citation correctness averaging 4.21 (SD 0.58; 95% CI 3.96-4.46).</p><p><strong>Conclusions: </strong>This feasibility study demonstrated that an integrated artificial intelligence agent system can support both public HPV vaccine communication and social media infoveillance in a Japanese-language context. Prospective deployment with real users is needed to assess actual public health impact.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e90295"},"PeriodicalIF":4.0,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13193703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147990271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of Large Language Models in the Cognitive Analysis of Misinformation: Evaluation Study.","authors":"Dominika Nadia Wojtczak, Cheryl McQuire, Luisa Zuccolo, Claudia Peersman, Ryan McConville","doi":"10.2196/72524","DOIUrl":"10.2196/72524","url":null,"abstract":"<p><strong>Background: </strong>Public discourse is significantly impacted by the rapid spread of misinformation on social media platforms. Human moderators, while capable of performing well, face many challenges due to scalability. While large language models (LLMs) show great potential across various language tasks, their capacity for cognitive and contextual analysis, in detecting and interpreting misinformation, remains less evaluated.</p><p><strong>Objective: </strong>This study evaluates the effectiveness of LLMs in detecting and interpreting misinformation compared to human annotators, focusing on tasks requiring cognitive analysis and complex judgment. Additionally, we analyze the influence of different prompt engineering strategies on model performance and discuss ethical considerations for using LLMs in content moderation systems.</p><p><strong>Methods: </strong>We evaluated 4 OpenAI models against a panel of human annotators using a subset of posts from the MuMiN dataset. Each model and human annotator responded to structured questions on misinformation, following an established cognitive framework. Both human annotators and LLMs also provided scores indicating how confident they were in their responses. Various prompting strategies were used in this research, including: 0-shot, few-shot, and chain-of-thought, with performance evaluated through precision, recall, F₁-score, and accuracy. We used statistical tests, including the McNemar test, to quantitatively assess differences between LLM and human ratings of misinformation.</p><p><strong>Results: </strong>GPT-4 Turbo with chain-of-thought prompting achieved the highest performance of all LLMs for detecting misinformation, with an accuracy of 67.2% and an F₁-score of 78.3%, but was outperformed by human annotators, who achieved 70.1% accuracy and an F₁-score of 81%. LLMs performed well in tasks involving logical reasoning and straightforward misinformation detection, but struggled with complex judgments, including detecting sarcasm, understanding misinformation, and analyzing user intent. LLM confidence scores positively correlated with accuracy in simpler tasks (r=0.72, P<.01) but were less reliable in subjective and complex contextual evaluations.</p><p><strong>Conclusions: </strong>LLMs show significant potential for automating misinformation detection. Their limitations in understanding and interpreting these posts highlight the current necessity of human oversight. A hybrid framework combining LLMs for preliminary screening with human moderators for more complex evaluation presents a promising future direction. Future research could prioritize the fine-tuning of LLMs using datasets that emphasize cognitive and emotional linguistic features, alongside the development of advanced prompting techniques.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e72524"},"PeriodicalIF":4.0,"publicationDate":"2026-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13227085/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147965481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Weight Loss Medication Discourse: Mixed Methods Analysis of US-Based Facebook Posts.","authors":"Elizabeth Dennard, Katrina Makres, Amrutha Alibilli, Vidur Jain, Anne Wang, Roger Abim-Karmon, Shaniece Criss, Yulin Hswen, Quynh C Nguyen, Alexandria Ratzki-Leewing, Rozalina G McCoy, Thu T Nguyen","doi":"10.2196/89732","DOIUrl":"10.2196/89732","url":null,"abstract":"<p><strong>Background: </strong>Despite the documented clinical efficacy of weight loss medications, few large-scale mixed methods studies have captured the experiences of individuals taking these medications.</p><p><strong>Objective: </strong>This study aims to examine dominant themes and public narratives about the perceived benefits, challenges, identity-based experiences, and the broader sociocultural framing of glucagon-like peptide-1 receptor agonist use on Facebook.</p><p><strong>Methods: </strong>We used CrowdTangle to collect 2500 US-based Facebook posts from January 1, 2022, to May 31, 2024. Bidirectional encoder representations from transformers topic modeling was used to identify latent topics, followed by a qualitative thematic analysis.</p><p><strong>Results: </strong>An analysis of 2500 Facebook posts revealed distinct thematic patterns across five unique subcategories: (1) healthy weight management, (2) weight loss medications, (3) discussion specific to semaglutide, (4) discussions specific to Ozempic, and (5) public figures and media. Thirteen overarching themes emerged through this thematic analysis, with the most common themes including weight management programs (n=702), neutral content related to weight loss medication use (n=466), harms of weight loss medication use (n=373), benefits of weight loss medication use (n=329), and access to these medications (n=188).</p><p><strong>Conclusions: </strong>Social media provides critical insights into individual experiences with pharmacotherapy and weight management. Enhanced public education may optimize the safe use of glucagon-like peptide-1 receptor agonists for weight loss.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e89732"},"PeriodicalIF":4.0,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13153451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation and User Evaluation of the SANGYAN Digital Health Platform to Enhance Knowledge About COVID-19 and Other Health Conditions: Quasi-Experimental Study.","authors":"Ashish Joshi, Ashruti Bhatt, Surapaneni Krishna Mohan, Gunjan Malhotra, Doilyn Oliveira, Saravanavel Kalpana Revathi, Ashoo Grover","doi":"10.2196/67504","DOIUrl":"10.2196/67504","url":null,"abstract":"<p><strong>Background: </strong>The spread of misinformation during the COVID-19 pandemic highlighted the importance of evidence-based information. The SANGYAN podcast promotes evidence-based knowledge on health-related issues in multiple languages in a simple, cost-effective, and concise manner. This provides individuals access to the appropriate information in an accessible manner.</p><p><strong>Objective: </strong>The study's goal is to assess user preferences for health information on a digital health platform designed to address COVID-19 misinformation.</p><p><strong>Methods: </strong>SANGYAN was developed by integrating the principles of social cognitive theory and information processing theory. The SANGYAN podcast was created to promote the importance of evidence-based information in order to address the spread of misinformation. The study design was a quasi-experimental study; prior to introducing the SANGYAN podcast, participants' sociodemographic information was collected, and health literacy was assessed using the Rapid Estimate of Adult Literacy in Medicine, Revised scale. After listening to the podcast, participants were interviewed about its usability, and they completed the System Usability Scale and the Client Satisfaction Questionnaire - 8. Data were collected from a total of 500 participants, 250 each from the Rural Health Training Center and Panimalar Medical College Hospital & Research Institute. The participants were older than 18 years when they were included. Descriptive and bivariate analyses were performed.</p><p><strong>Results: </strong>A total of 500 participants were enrolled in the study, 50% (250/500) from rural areas and 50% (250/500) from urban areas. The majority of the participants were 45 years to 64 years old (155/500, 31%), were women (289/500, 57.8%), had poor health literacy (384/500, 76.8%), and had a high school education or less than a high school certification (241/500, 48.2%). The mean overall System Usability System score was 70.9 (SD 17.73), with those aged 18 years to 24 years having the highest mean score (81.2, SD 15.48). High user satisfaction was present, with 97.6% (487/499) obtaining the desired information from the platform.</p><p><strong>Conclusions: </strong>The study revealed that the SANGYAN podcast provides information to diverse individuals, as it is multilingual, and was found useful by the participants.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e67504"},"PeriodicalIF":4.0,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13152227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}