JMIR infodemiology最新文献

{"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}

{"title":"Understanding Vaccine Hesitancy in Louisiana Through Social Media Listening and Community Feedback: Cross-Sectional Study.","authors":"Angie Viviana Sanchez, Sadie Smith, Sahithya Sakhamuri, Julia Ardoin, Henry Chu","doi":"10.2196/76827","DOIUrl":"10.2196/76827","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The rise of social media has significantly impacted public health programs, with platforms such as YouTube, Facebook, X (formerly known as Twitter), Instagram, and, more recently, TikTok being used to promote health information, raise awareness about disease outbreaks, and support disease prevention programs. However, the diverse and often unverified nature of the content on social media can make it challenging to discern accurate information, contributing to user uncertainty, which may in turn contribute to low vaccination rates in some regions. This is especially true in Louisiana as its COVID-19 vaccination rates were among the lowest in the country in 2022. Therefore, understanding public sentiment on social media and developing targeted campaigns to counter unverified information is essential for advancing public health campaigns.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The goal was to gain insights into the underlying factors that contribute to Louisiana's low vaccination rates for routine immunizations by (1) performing social media listening to develop an infodemic management plan and (2) promoting accurate information via a social media campaign.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Social media listening was conducted using Meltwater, a media monitoring and social media listening platform, supplemented by Google Alerts and Google News to identify if vaccine-related stories or sentiments were attracting unusual attention. Additionally, a social media campaign aimed at educating Louisiana residents about disease manifestation, symptoms, vaccines available for disease prevention, and potential side effects was developed. Posts were published 2 to 3 times a week and boosted for 7 days.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;From November 13, 2023, to June 11, 2024, social media listening identified at least 15 unique, noteworthy stories that signified sentiment spikes. These conversations were predominantly related to vaccine hesitancy, with users expressing opposition to vaccines or reluctance to engaging with vaccine-related information. Sentiment spikes included themes related to mistrust of vaccines and concerns about their safety and efficacy. The social media campaign received 69,600 impressions, reached 43,429 users, and received 652 reactions and likes, 62 shares, and 105 comments. Most of the audience was female, with higher engagement from older users on Facebook and younger users on Instagram. Finally, posts related to hepatitis B, rotavirus, and measles, mumps, and rubella vaccines received the most attention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Social media has become a key tool for digital health, helping to implement disease prevention programs and promoting advances in medicine. However, unverified information remains a major reason for the aversion to vaccination despite the dissemination of information from reputable public health organizations, health professionals, hospitals, and medical centers. To address","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e76827"},"PeriodicalIF":4.0,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13148589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846966","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 Discussions of Men's Mental Health on Reddit and YouTube: Cross-Sectional Mixed-Methods Infodemiological Study.","authors":"Anurag Shekhar, Musawenkosi Donia Saurombe","doi":"10.2196/81315","DOIUrl":"10.2196/81315","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Male mental health remains a major global concern, with men underrepresented in mental health care and overrepresented in suicide statistics. Masculine norms that link emotional restraint with strength can discourage help-seeking and vulnerability. Anonymous digital spaces such as Reddit (Reddit Inc) and YouTube (Google LLC) have become informal support environments where men share experiences and emotions outside traditional constraints. Understanding these interactions offers insight into masculine identity and help-seeking behavior.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study examines how men discuss and negotiate mental health within anonymous online communities. It explores whether these spaces support emotional openness, peer validation, and challenges to hegemonic masculinity norms. It triangulates digital discourse with survey and interview data to assess how these patterns align with men's lived experiences and perceived barriers to support.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This cross-sectional, exploratory mixed methods study analyzed publicly available online discourse from Reddit (n=740 posts) and YouTube (n=6287 comments). The qualitative component included 23 adult men (aged 18-55 years, predominantly Asian and employed) recruited via LinkedIn (Microsoft) who completed an anonymous online survey. Of these, 9 volunteered for follow-up semistructured interviews. Data underwent computational text mining using the Natural Language Toolkit and National Research Council Lexicon for word frequency and emotion analysis, followed by Braun and Clarke's 6-phase reflexive thematic analysis. Online discourse patterns were then compared with survey and interview data. Theoretical frameworks included hegemonic masculinity, toxic positivity, and peer-support theory.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Four themes emerged across the datasets: (1) normalizing emotional expression, (2) mutual validation and peer support, (3) coping through humor and irony, and (4) pushback against toxic positivity and societal norms. Emotion analysis showed prominent expressions of sadness, fear, trust, and anger across the Reddit and YouTube corpus. Survey data showed that 20 of 23 (87%) respondents reported having no safe offline space to discuss mental health. Interview participants (n=9) largely confirmed digital discourse themes, though some divergence emerged regarding whether humor functioned as deflection or connection.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study combines large-scale analysis of online discourse with qualitative triangulation across Reddit, YouTube, surveys, and interviews. Theoretically, it extends inclusive masculinity theory into anonymous online contexts, showing how digital platforms enable men to negotiate emotional expression outside traditional masculine constraints. It introduces the concept of \"digitally mediated sanctuaries\" to describe online spaces where men practice vulnerability and mutual support ","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e81315"},"PeriodicalIF":4.0,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13187704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847030","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":"Correction: Quality, Reliability, and Dissemination of In Vitro Fertilization-Related Videos on Chinese Social Media: Cross-Sectional Analysis of 300 Short Videos.","authors":"Xueyan Bai, Feng Guo, Dapeng Chu","doi":"10.2196/95354","DOIUrl":"10.2196/95354","url":null,"abstract":"","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e95354"},"PeriodicalIF":4.0,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13102283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791216","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":"Parents' Multiperspective Views and Misinformation About COVID-19 Mitigation Measures During Tennessee School Board Meetings: Qualitative Content Analysis Using YouTube.","authors":"Olufunto A Olusanya, Brianna M White, Brenda Amuchi, Chad Melton, Arash Shaban-Nejad","doi":"10.2196/75691","DOIUrl":"10.2196/75691","url":null,"abstract":"<p><strong>Background: </strong>In fall 2021, Tennessee school districts faced heightened debates over COVID-19 mitigation amid rising cases, limited vaccination availability, and widespread misinformation. School board meetings (SBMs) served as pivotal decision-making forums influencing district policies. This study investigated perceptions and misinformation regarding the COVID-19 mask mandate at SBMs held within 6 of Tennessee's largest school districts. With widespread debate over pandemic measures, including mask use in schools, understanding community sentiments is crucial for guiding public health policies.</p><p><strong>Objective: </strong>This study aimed to investigate the viewpoints of parents or caregivers and teachers regarding COVID-19 safety protocols, particularly the mask mandate, and to identify the misinformation circulating within SBMs.</p><p><strong>Methods: </strong>Participants' commentaries were extracted from 6 SBM recordings that were publicly uploaded to YouTube from August through September 2021. The data were examined qualitatively to capture themes related to concerns, support, and misinformation. Inductive thematic analysis was conducted using transcripts generated via Microsoft Azure speech-to-text and manually verified.</p><p><strong>Results: </strong>Many parents or caregivers gave personal accounts of how the pandemic had impacted them, their children, and their communities, describing significant comorbidities, adverse psychosocial impacts, mental health disorders, learning difficulties, and worsening socioeconomic and educational disparities. Six thematic domains emerged: (1) perceived effects of the COVID-19 pandemic on children, teachers, and parents or caregivers, including psychosocial distress, learning disruptions, and burnout; (2) perceived effects of mask mandates on children, particularly concerns regarding physical health and psychosocial well-being; (3) perceived government overreach and legal objections to COVID-19 mitigation mandates; (4) tensions between personal liberty, religious beliefs, and collective responsibility in masking decisions; (5) circulation of misinformation and conflicting guidance regarding mask safety and effectiveness; and (6) institutional strain, social tensions, and hostility directed toward school officials alongside educator burnout.</p><p><strong>Conclusions: </strong>Perspectives on COVID-19 mitigation varied widely across meeting participants, highlighting the need for health officials and policymakers to engage in proactive health promotion strategies. Strengthening public health communication, misinformation mitigation, and institutional support for teachers will be essential to ensuring safe and effective learning environments during future public health crises.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e75691"},"PeriodicalIF":4.0,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13094800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147730796","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":"Cross-Sectional Evaluation of Medical Disinformation Safeguards in Consumer-Facing Large Language Model Platforms.","authors":"Natansh D Modi, Cyril A Alex, Abdulhalim A Awaty, Bradley D Menz, Stephen Bacchi, Kacper T Gradon, Jessica M Logan, Andrew Rowland, Lisa Kalisch Ellett, Ross A McKinnon, Michael D Wiese, Michael J Sorich, Ashley M Hopkins","doi":"10.2196/89831","DOIUrl":"10.2196/89831","url":null,"abstract":"<p><strong>Unlabelled: </strong>This cross-sectional evaluation of six consumer-facing large language model platforms found significant heterogeneity in safeguard performance against the generation of health disinformation, with Claude and ChatGPT demonstrating complete resistance across all prompt types, while Copilot, Meta AI, Grok, and Gemini exhibited substantial vulnerabilities.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e89831"},"PeriodicalIF":4.0,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13094790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147730857","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":"Portrayals of Depression on TikTok: Content Analysis of Diagnostic Accuracy, Creator Type, and Stylistic Features.","authors":"Elena Rainer, Amber van der Wal, Ine Beyens","doi":"10.2196/85323","DOIUrl":"10.2196/85323","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Youths are increasingly turning to TikTok for mental health information, making the platform an important space where young people encounter portrayals of mental illness. While such visibility can raise awareness, reduce stigma, and make young people feel more connected and understood in their experiences, concerns have been raised about the diagnostic accuracy of this content, which is often produced by nonprofessionals and presented using emotionally appealing stylistic features. Although prior research has examined mental health content on TikTok broadly, little is known about how depression-related symptoms are portrayed by creators on the platform.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Given depression's rising prevalence among youth and its prominent presence on TikTok, this study examined (1) the diagnostic accuracy of TikTok videos about depression, (2) differences in diagnostic accuracy and stylistic features by creator type (medical professionals vs nonprofessionals), and (3) how diagnostic accuracy, stylistic features (personal experiences, emotional appeals, and background music), and creator type relate to user engagement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A quantitative content analysis was conducted of 210 English-language TikTok videos retrieved using symptom-focused search terms (eg, \"depression symptoms\"). Videos were coded for diagnostic accuracy using a standardized coding scheme based on the International Classification of Diseases, 11th Revision diagnostic criteria for depressive episodes. In addition, videos were coded for creator type, presentation style, and the presence of emotionally appealing stylistic features. Engagement was operationalized as the sum of a video's likes, comments, saves, and shares. Intercoder reliability was assessed using Krippendorff α, percent agreement, and Gwet AC1 (agreement coefficient 1). Analyses included Mann-Whitney U tests, chi-square tests, and hierarchical regression.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Diagnostic accuracy was low overall (mean score 1.21, SD 1.04, on a 0-4 scale) and did not differ significantly between medical professionals and nonprofessionals (median 1.40 [IQR 1-2] vs 1.11 [IQR 0-2]; P=.06). Hierarchical regression analysis showed that diagnostic accuracy did not predict engagement (B=-0.10; P=.19). In contrast, engagement was higher for videos containing personal experiences (B=0.41; P=.02), emotional appeals (B=0.73; P=.001), and background music (B=0.54; P=.01). Across regression models, direct-to-camera formats (Bs -0.49 to -0.69; .003≤P≤.04) and text-centered videos (Bs -0.56 to -0.64; .002≤P&lt;.01) were associated with lower engagement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Depression-related content on TikTok is characterized by limited diagnostic completeness, regardless of creator type. Engagement appears to be driven primarily by stylistic features rather than diagnostic accuracy. These patterns raise concerns about concept creep-the gra","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e85323"},"PeriodicalIF":4.0,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13075627/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147679232","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}