JMIR infodemiology最新文献

{"title":"Hand, Foot, and Mouth Disease Risk Prediction in Southern China: Time Series Study Integrating Web-Based Search and Epidemiological Surveillance Data.","authors":"Yixiong Chen, Xue Zhang, Sheng Zhang, Wenjie Han, Ziqi Wang, Jian Chen, Jinfeng Liu, Jingru Feng, Jiayi Shi, Haoyu Long, Zicheng Cao, Jie Zhang, Yuan Li, Xiangjun Du, Xindong Zhang, Meng Ren","doi":"10.2196/75434","DOIUrl":"10.2196/75434","url":null,"abstract":"<p><strong>Background: </strong>Hand, foot, and mouth disease (HFMD) is a global health concern requiring a risk assessment framework based on systematic factors analysis for prevention and control.</p><p><strong>Objective: </strong>This study aims to construct a comprehensive HFMD risk assessment framework by integrating multisource data, including historical incidence information, environmental parameters, and web-based search behavior data, to improve predictive performance.</p><p><strong>Methods: </strong>We integrated multisource data (HFMD cases, meteorology, air pollution, Baidu Index, and public health measures) from Bao'an District of Shenzhen city in Southern China (2014-2023). Correlation analysis was used to assess the associations between HFMD incidence and systematic factors. The impacts of environmental factors were analyzed using the Distributed Lag Nonlinear Model. Seasonal Autoregressive Integrated Moving Average model and advanced machine learning methods were used to predict HFMD 1-4 weeks ahead. Risk levels for the 1- to 4-week-ahead forecasts were determined by comparing the predicted weekly incidence against predefined thresholds.</p><p><strong>Results: </strong>From 2014 to 2023, Bao'an District reported a total of 118,826 cases of HFMD. Environmental and search behavior factors (excluding sulfur dioxide) were significantly associated with HFMD incidence in nonlinear patterns. For 1-week-ahead prediction, Seasonal Autoregressive Integrated Moving Average using case data alone performed best (R²=0.95, r=0.98, mean absolute error=53.34, and root-mean-square error=99.31). For 2- to 4-week-ahead forecasting, machine learning models incorporating web-based and environmental data showed superior performance (R²=0.83, 0.75, and 0.64; r=0.92, 0.87, and 0.80; mean absolute error=87.84, 112.41, and 132.47; and root-mean-square error=185.08, 229.13, and 276.81). The predicted HFMD risk levels matched the observed levels with accuracies of 96%, 87%, 88%, and 83%, respectively.</p><p><strong>Conclusions: </strong>The epidemic dynamics of HFMD are influenced by multiple factors in a nonlinear manner. Integrating multisource data, particularly web-based search behavior, significantly enhances the accuracy of short- and midterm forecasts and risk assessment. This approach offers practical insights for developing digital surveillance and early warning systems in public health.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e75434"},"PeriodicalIF":2.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12510436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260041","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":"Social Media Discussions About Robotic Total Knee Arthroplasty: Cross-Sectional Analysis.","authors":"Charles Desgagné, Jordan J Levett, Lior M Elkaim, John Antoniou","doi":"10.2196/69883","DOIUrl":"10.2196/69883","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The advent of robotic total knee arthroplasty (TKA) in the field of orthopedics has caused much discussion on social media. As social media grows, its platforms are becoming an increasingly popular medium for health care-related discussions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to better understand the current public discussion about robotic TKA on social media. We aimed to characterize these discussions by analyzing their contributors, the general sentiment, the temporal trends, and the content.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A comprehensive search of the Twitter database for academic research was performed from inception (March 2006) to April 1, 2023, to identify all tweets related to robotic TKA. General data regarding the tweets and the accounts were retrieved. ChatGPT-4o (OpenAI) was used to categorize the post's content and the accounts into different categories developed via iterative testing. The content was categorized using a rule-based classification algorithm developed using Python to assign categories based on keyword presence, phrase matching, and syntactic patterns. Regarding the accounts, an automated keyword-based rule engine was implemented in Python to classify accounts based on the account's name and description. We used a lexicon-based natural language processing Python library, via ChatGPT-4o, to assign a sentiment to the tweets and conducted subgroup sentiment analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 2000 tweets were retrieved for analysis. Account analysis revealed that the most prevalent account categories were \"medical professionals\" (619/2000, 31.0%), \"patients and community\" (274/2000, 13.7%), and \"media and publications\" (268/2000, 13.4%). Content analysis revealed that the most prevalent tweet themes were \"technology and innovation\" (550/2000, 27.5%), \"advertising and promotion\" (176/2000, 8.8%), and \"research and data\" (172/2000, 8.6%). Sentiment analysis showed that 61.6% (1231/2000) of the tweets had a positive sentiment, while 9.2% (183/2000) were neutral, and 29.3% (586/2000) had a negative sentiment. Accounts categorized as \"institutions\" had the highest prevalence of positive sentiment (165/229, 72.1%), while accounts categorized as \"media and publications\" had the highest prevalence of negative sentiment (88/268, 32.8%). The number of tweets relating to robotic TKA has been steadily rising since 2016, with a peak incidence of 402 (20.1%) tweets published in 2022.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The increased number of tweets with a positive sentiment suggests a positive outlook toward robotic TKA. Institutions had the highest prevalence of positive sentiment, suggesting a possible bias toward positive reporting of robotic TKA, likely for commercial reasons. Media and publications had the highest prevalence of negative sentiment, which may represent skepticism and bias toward negative reporting on robotic technologies in health care. Medical profes","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e69883"},"PeriodicalIF":2.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12510438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260087","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":"Physical Activity Misinformation on Social Media: Systematic Review.","authors":"D David Thomas, Linglin Xu, Brian Yu, Octavio Alanis, John Adamek, Imani Canton, Xuan Lin, Yan Luo, Sean P Mullen","doi":"10.2196/62760","DOIUrl":"https://doi.org/10.2196/62760","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Social media is a prominent way in which health information is spread. The accuracy and credibility of such sources range widely, with misleading statements, misreported results of studies, and a lack of references causing health misinformation to become a growing problem. However, previous research on health misinformation related to topics including vaccines, nutrition, and cancer has excluded physical activity despite it being highly searched for and discussed online.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This systematic review was designed to synthesize the existing literature focused on physical activity misinformation on social media in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Keyword searches were conducted in PubMed, the Cochrane Library, Web of Science, and Scopus databases for records published between January 2016 and May 2025. This search strategy yielded 9039 articles. Titles and abstracts were screened by independent reviewers, resulting in 168 (1.86%) articles selected for full-text review. After further review, 33 (19.6%) articles met the inclusion criteria and were used in the final synthesis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;For the 33 studies selected, topics included physical rehabilitation and therapeutic exercise recommendations (n=15, 45%), general physical activity and messaging (n=6, 18%), exercising with a specific condition (n=4, 12%), women's health (n=3, 9%), weight loss (n=2, 6%), exercise testing (n=1, 3%), \"immune boosting exercise\" (n=1, 3%), and workplace sitting versus standing guidelines (n=1, 3%). The social media platforms YouTube (n=13, 39%), TikTok (n=7, 21%), Facebook (n=2, 6%), Instagram (n=1, 3%), and Pinterest (n=1, 3%) were studied, whereas other articles (n=9, 27%) analyzed content that had not explicitly been posted to social media but could be shared widely online. In total, 4 (12%) studies reported research that proactively engaged participants, and the remaining 29 (88%) studies analyzed readily available online content, including social media, news articles, websites, and blogs. Furthermore, 27 (82%) studies reported at least 1 measure of misinformation prevalence, whereas 21 (64%) reported a metric of reach, and 6 (18%) studies reported a measure of misinformation spread.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our findings indicate that research on social media physical activity misinformation spans a diverse array of physical activity topics, with YouTube being the most studied platform due to its widespread use and ease of content evaluation. This review also highlights the prevalence of low-quality information across various platforms and a lack of longitudinal investigations. Our review underscores the need for multifaceted research approaches and suggests several strategies to combat misinformation, including improved messaging, high-quality information dissemination b","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e62760"},"PeriodicalIF":2.3,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global Influence of Cannabis Legalization on Social Media Discourse: Mixed Methods Study.","authors":"Consuelo Castillo-Toledo, Carolina Donat-Vargas, María Montero-Torres, Francisco J Lara-Abelenda, Fernando Mora, Melchor Alvarez-Mon, Javier Quintero, Miguel Ángel Álvarez-Mon","doi":"10.2196/65319","DOIUrl":"10.2196/65319","url":null,"abstract":"<p><strong>Background: </strong>Cannabis is the third most consumed drug worldwide, with its use linked to a high number of substance use disorders, particularly among young men. Associated mortality causes include traffic accidents and cardiovascular diseases. The global expansion of cannabis legalization has sparked debates about its impact on risk perception, with risk perception decreasing in countries with permissive laws. Social media analysis, such as on Twitter (subsequently rebranded as X), is a useful tool for studying these perceptions and their variation by geographic region.</p><p><strong>Objective: </strong>This study aims to analyze Twitter users' perceptions of cannabis use and legalization, taking into account the geographic location of the tweets.</p><p><strong>Methods: </strong>A mixed methods approach was used to analyze cannabis-related tweets on Twitter, using keywords such as \"cannabis,\" \"marijuana,\" and \"hashish.\" Tweets were collected from January 1, 2018, to April 30, 2022, in English and Spanish, and only those with at least 10 retweets were included. The content analysis involved an inductive-deductive approach, resulting in the classification of tweets into thematic categories, including discussions on legalization.</p><p><strong>Results: </strong>The tweet analysis showed that in America, Europe, and Asia, political discussions about cannabis were the most common topic, while personal testimonies dominated in Oceania and Africa. In all continents, personal experiences with cannabis use were mostly positive, with Oceania recording the highest percentage (1642/2695, 60.93%). Regarding legalization, Oceania also led with the highest percentage of tweets in favor (1836/2695, 68.13%), followed by America and Africa, while support in Europe and Asia was slightly lower, with about half of the tweets in favor.</p><p><strong>Conclusions: </strong>The political debate has been the most frequently mentioned topic, reflecting the current situation in which legislative changes are being discussed in many countries. The predominance of opinions in favor of legalization, combined with the prevalence of positive experiences expressed about cannabis, suggests that the health risks associated with cannabis use are being underestimated in the public debate.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e65319"},"PeriodicalIF":2.3,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145194076","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":"Quality Assessment of Videos About Dengue Fever on Douyin: Cross-Sectional Study.","authors":"Youlian Zhou, Liang Yang, Li Luo, Lianghai Cao, Jun Qiu","doi":"10.2196/76474","DOIUrl":"10.2196/76474","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Dengue fever has evolved into a significant public health concern. In recent years, short-video platforms such as Douyin have emerged as prominent media for the dissemination of health education content. Nevertheless, there is a paucity of research investigating the quality of health education content on Douyin.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the quality of dengue videos on Douyin.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A comprehensive collection of short videos pertaining to dengue fever was retrieved from the popular social media platform, Douyin, at a designated point in time. A systematic analysis was then performed to extract the characteristics of these videos. To ensure a comprehensive evaluation, three distinct scoring tools were used: the DISCERN scoring tool, the JAMA benchmarking criteria, and the GQS method. Subsequently, an in-depth investigation was undertaken into the relationship between video features and quality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 156 videos were included in the analysis, 81 of which (51.9%) were posted by physicians, constituting the most active category of contributor. The selected videos pertaining to dengue fever received a total of 718,228 likes and 126,400 comments. The video sources were categorized into four distinct classifications: news agencies, organizations, physicians, and individuals. Individuals obtained the highest number of video likes, comments, and saves. However, the findings of the study demonstrated that physicians, organizations, and news agencies posted videos are of higher quality when compared with individuals. The integrity of the video content was analyzed, and the results showed a higher percentage of videos received a score of zero points for outcomes, management, and assessment, with 69 (45%), 57 (37%), and 41 (26%), respectively. The median Total DISCERN scores, JAMA, and GQS of the 156 dengue-related videos under consideration were 26 (out of a total of 80 points), 2 (out of a total of 4 points), and 3 (out of a total of 5 points), respectively. Spearman correlation analysis was conducted, revealing a positive correlation between video duration and video quality. Conversely, a negative correlation was observed between the following variables: video comments and video quality, and the number of days since posting and video quality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study demonstrates that the quality of short dengue-related health information videos on Douyin is substandard. Videos uploaded by medical professionals were among the highest in terms of quality, yet their videos were not as popular. It is recommended that in future, physicians employ more accessible language incorporating visual elements to enhance the appeal and dissemination of their videos. Future research could explore how to achieve a balance between professionalism and entertainment to promote user acceptance of high-quality content. Moreov","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e76474"},"PeriodicalIF":2.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12466789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145180056","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":"Stillbirth Discourse on Instagram and X (Formerly Twitter): Content Analysis.","authors":"Abigail Paradise Vit, Daniel Fraidin, Yaniv S Ovadia","doi":"10.2196/73980","DOIUrl":"10.2196/73980","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Stillbirth, the loss of a fetus after the 20th week of pregnancy, affects about 1 in 160 deliveries in the United States and nearly 1 in 70 globally. It profoundly affects parents, often resulting in grief, depression, anxiety, and posttraumatic stress disorder, exacerbated by societal stigma and a lack of public awareness. However, no comprehensive analysis has explored social media discussions of stillbirth.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to analyze stillbirth-related content on Instagram and X (formerly Twitter) by (1) identifying dominant themes using topic modeling, evaluated using latent Dirichlet allocation, non-negative matrix factorization (NMF), and BERTopic; (2) detecting influential hashtags via co-occurrence network analysis; (3) examining sentiments and emotions using transformer-based models; (4) categorizing visual representations of stillbirth on Instagram (Meta) through manual image analysis with a predefined codebook; and (5) screening for misinformation relating to stillbirth on X.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Stillbirth-related posts were collected via RapidAPI (N=27,395), with Instagram posts (#stillbirth: n=7415; #stillbirthawareness: n=8312; 2023-2024) and X posts (#stillbirth: n=11,668; 2020-2024) analyzed using Python 3.12.7 (Python Software Foundation), with NetworkX for hashtag co-occurrence networks and the PageRank algorithm; comparative analyses were restricted to 2023-2024 due to Instagram application programming interface constraints. Topic modeling was evaluated using latent Dirichlet allocation, NMF, and BERTopic, with coherence scores guiding our model selection. Sentiment and emotion were analyzed using transformer-based RoBERTa and DistilRoBERTa. Misinformation screening was applied to X posts. On Instagram, 2 representative image samples (n=366) were manually categorized using a predefined codebook, with the interrater reliability being assessed using Cohen Kappa.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Health-related hashtags (eg, #COVID19) appeared more frequently on X. Topic modeling showed that NMF achieved the highest coherence scores (#stillbirthawareness=0.624 and #stillbirth=0.846 on Instagram, #stillbirth=0.816 on X). Medical misinformation appeared in 27.8% (149/536) of tweets linking COVID-19 vaccines to stillbirth. In the image analysis, \"Image of text\" was most common, followed by remembrance visuals (eg, gravesites and stillborn infants). The interrater reliability was strong, κ=0.837 (95% CI 0.773-0.891) and κ=0.821 (95% CI 0.755-0.879), with high Pearson correlation (r=0.999; P&lt;.001) and no significant difference (χ²7=12.4; P=.09). The sentiment analysis found that positive sentiments exceeded negative sentiments. The emotion analysis showed that fear and sadness were dominant, with fear being more prevalent on X.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Instagram emphasizes emotional expression while X focuses on public health and informational conte","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e73980"},"PeriodicalIF":2.3,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12466797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145180010","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 Pain on Social Media: Observational Study on Perceptions and Discussions of Chronic Pain Conditions.","authors":"Teresa Valades, Cesar I Fernandez-Lazaro, Francisco Lara-Abelenda, Maria Montero-Torres, Ines Cuberta Gonzalez, Miguel A Ortega, Melchor Alvarez-Mon Soto, Miguel Angel Alvarez-Mon","doi":"10.2196/67473","DOIUrl":"10.2196/67473","url":null,"abstract":"<p><strong>Background: </strong>Chronic pain, affecting 30.3% of the global population, constitutes a major public health and social challenge. It is associated with disability, emotional distress, and diminished quality of life. Conditions, such as fibromyalgia, headache, paraplegia, neuropathy, and multiple sclerosis are characterized by persistent pain and limited social and medical understanding. This contributes to patient isolation and increases mental health burden. In recent years, social media, particularly X (formerly Twitter), has emerged as a key space for analyzing health-related perceptions and experiences. Its massive use, spontaneity, and broad reach have made these platforms a valuable source for infodemiological research.</p><p><strong>Objective: </strong>This study aims to analyze posts on X concerning fibromyalgia, headache, paraplegia, neuropathy, and multiple sclerosis, as well as characterize the profile of users involved in these conversations, identify prevalent topics, measure public perception, evaluate treatment efficacy, and detect discussions related to the most frequent nonmedical issues.</p><p><strong>Methods: </strong>A total of 72,874 tweets in English and Spanish containing the selected keywords were collected between 2018 and 2022. A manual review of 2500 tweets was conducted, and the larger subset was automatically classified using natural language processing methods based on the BERTweet model, previously fine-tuned for content analysis on social media platforms. Subsequently, tweets related to chronic pain conditions were analyzed to examine user types, disease origin, and both medical and nonmedical content.</p><p><strong>Results: </strong>Of the total tweets collected, 55,451 (76.1%) were classifiable. The most active users were health care professionals and institutions. The primary perceived etiology was pharmacological, and higher treatment efficacy was noted in neuropathy, paraplegia, and multiple sclerosis. Regarding nonmedical content, there were more tweets related to the definition and understanding of the disease.</p><p><strong>Conclusions: </strong>Social media platforms, such as X, are playing a crucial role in the dissemination of information on chronic pain. Discussions largely focus on the available treatments and the need to enhance public education, using these platforms to correct misconceptions and provide better support to patients.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e67473"},"PeriodicalIF":2.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12440231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076629","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":"Viewpoint on the Intersection Among Health Information, Misinformation, and Generative AI Technologies.","authors":"António Bandeira, Luis Henrique Gonçalves, Felix Holl, Juliet Ugbedeojo Shaibu, Mariana Laranjo Gonçalves, Ronan Payinda, Sagun Paudel, Alessandro Berionni, Tina D Purnat, Tim Mackey","doi":"10.2196/69474","DOIUrl":"10.2196/69474","url":null,"abstract":"<p><p>In recent years, artificial intelligence (AI) has seen rapid advancements, with innovations such as large language models and generative AI evolving at a rapid pace. While this progress offers tremendous opportunities, it also presents risks, particularly in the creation, consumption, and amplification of information and its impact on population health and health program delivery. Thoughtful approaches are necessary to navigate the consequences of advances in AI for different health care professionals and patient populations and from a policy and governance perspective. Through a collaboration between the World Federation of Public Health Associations working groups, this Viewpoint article brings together perspectives, concerns, and aspirations from young adult professionals across 5 continents and from diverse backgrounds to explore the future of public health and AI in the context of the changing health information environment. Our discussion is divided into 2 parts, specifically examining aspects of disinformation and AI, and also the role of public health and medical professionals in a growing AI-driven health information ecosystem. This Viewpoint concludes with 5 key recommendations on how to potentially address issues such as information and disinformation overload; misinformation propagation; and resultant changes in health practices, research, ethics, and the need for robust policies that can dynamically address current and future challenges.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e69474"},"PeriodicalIF":2.3,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145071356","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":"Health-Related Concerns of Anti-LGBTQ+ Legislation: Thematic Analysis Using Social Media Data.","authors":"Ari Z Klein, Kaelen Spiegel, José A Bauermeister, Graciela Gonzalez-Hernandez","doi":"10.2196/68956","DOIUrl":"10.2196/68956","url":null,"abstract":"<p><strong>Background: </strong>There has been a recent proliferation of anti-LGBTQ+ (lesbian, gay, bisexual, transgender, queer/questioning) legislation being proposed in the United States, including more than 500 bills across 42 states in 2024. Many of the studies examining the impact of anti-LGBTQ+ legislation have focused specifically on the association with mental health outcomes.</p><p><strong>Objective: </strong>The objective of this study was to use social media data to more broadly explore health-related concerns of anti-LGBTQ+ legislation among sexual minority men in the United States.</p><p><strong>Methods: </strong>We leveraged a dataset containing 70 million tweets that were posted by 23,276 users in the United States who self-reported on Twitter that they are sexual minority men. First, we searched these tweets for keywords related to LGBTQ+ legislation. Next, we developed a codebook for identifying those that expressed health-related concerns of anti-LGBTQ+ legislation. Then, we developed a coding scheme to categorize these concerns into one or more themes by using an inductive approach. Finally, we automatically identified the users' geographic location and age for subgroup analyses.</p><p><strong>Results: </strong>Among 8486 keyword-matched tweets, 493 (5.8%) tweets expressed health-related concerns due to anti-LGBTQ+ legislation and were posted by 288 sexual minority men in the United States: 112 (38.9%) who posted about health care, 84 (29.2%) about safety, 64 (22.2%) about mental health, 62 (21.5%) about general harm, 49 (17%) about human rights, and 40 (13.9%) about support. Health care was the top concern overall and across the United States and age groups. In contrast, the higher prevalence of mental health was driven by the larger number of users in the South, as it was less of a concern in other regions. Similarly, mental health was less of a concern among older age groups. Safety was as much of a concern as mental health overall and across the United States and most age groups.</p><p><strong>Conclusions: </strong>Our findings may inform a broader range of health interventions and approaches for targeting them at specific populations of sexual minority men. By demonstrating that these concerns are expressed on social media, our findings can be leveraged by advocacy groups to amplify voices and rally public support for countering anti-LGBTQ+ bills.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e68956"},"PeriodicalIF":2.3,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12464498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042461","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":"Data Mining Trauma: AI-Assisted Qualitative Study of Cyber Victimization on Reddit.","authors":"J'Andra Antisdel, Wendy R Miller, Doyle Groves","doi":"10.2196/75493","DOIUrl":"10.2196/75493","url":null,"abstract":"<p><strong>Background: </strong>Cyber victimization exposes individuals to numerous risks. Developmental and psychological factors may leave some users unaware of the potential dangers, increasing their susceptibility to psychological distress. Despite this vulnerability, methods for identifying those at risk of cyber victimization within health care settings are limited, as is research that explores their experiences of cyber victimization. The purpose of this study was to analyze how users describe experiences of cyber victimization on the social media platform Reddit (Reddit, Inc) using data mining.</p><p><strong>Objective: </strong>This study aimed to analyze and describe how users on Reddit describe and discuss their experience of cyber victimization using data mining and computational analysis of unsolicited data.</p><p><strong>Methods: </strong>This computational qualitative study used data mining, Word Adjacency Graph (WAG) modeling, and thematic analysis to analyze discussions of Reddit users surrounding cyber victimization. Inclusion criteria included posts from 2012 to 2023 from subreddits r/cyberbullying and r/bullying. GPT-4 (OpenAI), an advanced artificial intelligence language model, summarized posts and assisted in cluster labeling. Posts were reviewed to remove irrelevant content and duplicates. User anonymity was maintained throughout the study.</p><p><strong>Results: </strong>A total of 13,381 posts from 3283 Reddit were analyzed, with approximately 5.1% (n=678) originating between 2012 and 2018 and 94.9% (n=12,703) from 2019 to 2023. The WAG modeling approach identified 38 clusters, with 35 deemed to be relevant to cyber victimization experiences. Two clusters containing irrelevant material were excluded. Six overarching themes emerged: (1) psychological impact, (2) coping and healing, (3) protecting yourself online, (4) protecting yourself offline, (5) victimization across various settings, and (6) seeking meaning and understanding.</p><p><strong>Conclusions: </strong>The study highlights the effectiveness of data mining and AI in analyzing large public datasets for qualitative research. These methods can inform future studies on risky internet behavior, victimization, and assessment strategies in health care settings.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e75493"},"PeriodicalIF":2.3,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12407219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994507","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}