JMIR infodemiology最新文献

{"title":"Evaluating Public Sentiment on Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Compared With Other Mental Health Disorders From Posts on X (Formerly Known as Twitter): Longitudinal Analysis.","authors":"Vitor Tiosso Batistetti, Facundo G Sanchez, Andrea Varaona, Francisco Lara-Abelenda, Mariana Pinto da Costa, Juan Pablo Chart-Pascual, Alberto Rodriguez-Quiroga, Javier Quintero, Miguel Angel Alvarez-Mon","doi":"10.2196/74440","DOIUrl":"10.2196/74440","url":null,"abstract":"<p><strong>Background: </strong>Neurodevelopmental disorders, especially attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD), have seen a marked rise in public attention, yet research on public opinion remains limited. Social media analysis offers real-time, unfiltered insights into public perceptions, enabling empirical examination of public attitudes and opinions.</p><p><strong>Objective: </strong>This study aimed to assess the evolution of public opinion on ADHD and ASD between 2009 and 2023 by analyzing posts from X (formerly known as Twitter; X Corp), comparing perceptions across English and Spanish languages and against other mental health conditions.</p><p><strong>Methods: </strong>Posts mentioning keywords related to ADHD and ASD and control conditions (eg, depression, anxiety, insomnia, bipolar disorder, schizophrenia, suicide, and substance use disorders) were collected from X between 2009 and 2023. The dataset included posts in both English and Spanish. Machine learning algorithms were then applied to classify post content into predefined categories, including volume of posts, engagement, personal experiences, trivialization, perceived causes, and perceived treatability. Parametric and nonparametric tests were used to assess for differences by language. Descriptive statistics were presented using tables and graphical representations.</p><p><strong>Results: </strong>A total of 852,990 posts were analyzed, including 511,510 (59.97%) in English and 341,480 (40.03%) in Spanish. Overall, post volume on mental health conditions increased across the study period. In English, posts about ADHD (97,084/511,510, 18.98%) and ASD (74,619/511,510, 14.59%) were among the most frequent, while of the 341,480 Spanish posts, there were 49,475 (14.49%) ASD posts, significantly outnumbering ADHD posts (n=18,223, 5.34%; chi-square test P<.001). Engagement analysis indicated a notable increase in likes and reposts per post over time, particularly after 2019, with ADHD-related posts in English experiencing peak engagement during the COVID-19 pandemic. However, ASD posts had comparatively lower engagement across languages. Posts sharing personal experiences were more polarized in Spanish, with higher proportions of negative and positive experiences compared with English posts. Trivialization of mental illnesses was less common in Spanish posts than in English posts, particularly for ADHD (17,053/18,223, 93.59%; chi-square test P<.001) and ASD (41,933/49,475, 84.73%; chi-square test P<.001). User-perceived causes included multifactorial factors, biological or genetic factors, substance use, psychological susceptibility, acute psychosocial stressors, and COVID-19. Perceived treatability varied by language but consistently included high perceived incurability, limited improvement despite professional help, and low perceived self-manageability except for anxiety.</p><p><strong>Conclusions: </strong>Analysis of social media discourse ","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e74440"},"PeriodicalIF":4.0,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13107104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147647690","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":"The Information Void in Asymptomatic Chronic Disease: A Digital Health Framework for Understanding Social Media Health Information Seeking in Young Adults.","authors":"Victoria Sze Min Ekstrom","doi":"10.2196/86489","DOIUrl":"10.2196/86489","url":null,"abstract":"<p><strong>Unlabelled: </strong>Nearly 1 in 4 young adults has a chronic condition, yet many feel well despite their diagnosis. Asymptomatic conditions such as prediabetes and hypertension create a unique vulnerability to digital health misinformation, particularly on platforms where inaccurate content is prevalent. Conventional clinical responses, which often just warn patients about online misinformation, fail to address the underlying drivers of this behavior. This viewpoint proposes a novel disease characteristic-based vulnerability framework to understand this challenge, grounded in established behavioral science theories such as the capability, opportunity, and motivation-behavior model; temporal discounting; and the concept of information voids in infodemiology. We identify a critical \"information void\" for asymptomatic conditions managed primarily through lifestyle modification. This void, created by the absence of symptomatic feedback combined with delayed clinical biomarker feedback, compels patients to seek information online. Instead of viewing this information seeking as a problematic deviation, we reframe it as a \"digital phenotype\" indicating a patient's readiness for behavior change. Through case studies illustrating how this framework applies to specific conditions (prediabetes, nonalcoholic fatty liver disease, and untreated hypertension), we demonstrate its practical utility for clinicians, health systems, and policymakers. Evidence supports a multipronged approach: integrating digital health literacy into clinical encounters, providing curated evidence-based resources, and pursuing strategic institutional engagement in digital spaces. While acknowledging the framework's deliberate simplification and the need for culturally sensitive adaptation across diverse health care settings, this viewpoint offers a generalizable strategy for engaging with patients' information needs, helping transform a public health challenge into an opportunity for empowerment.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e86489"},"PeriodicalIF":4.0,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13055946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147635221","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 Social Support and Opinion Leaders in a Tuberculosis-Related Online Community in China: Content and Network Analyses.","authors":"Xiaojun Fan, Jueman Zhang, Xiuli Wang","doi":"10.2196/79140","DOIUrl":"10.2196/79140","url":null,"abstract":"<p><strong>Background: </strong>Tuberculosis (TB) remains one of the world's deadliest infectious diseases. Yet, despite the growing role of online health communities (OHCs) as key sources of social support, research on TB-related online communities remains scarce. Network analysis has been increasingly used to study OHCs and identify opinion leaders (OLs), offering a valuable approach to advancing knowledge about TB-related online communities.</p><p><strong>Objective: </strong>This study examined the types of social support and the influence of OLs in a prominent TB-related online forum in China, with a particular focus on its curated subforum that served as a centralized space for user interaction. The subforum consisted of posts recommended by the forum's administrator and the corresponding user replies they generated.</p><p><strong>Methods: </strong>The data consisted of all 438 administrator-recommended posts and the 150,570 associated user replies over 18 years, from the forum's launch in 2004 to 2021. The study used content analysis to examine the types of social support present in administrator-recommended posts, which are commonly considered high-quality. It then applied social network analysis to these posts and their associated user replies to identify OLs by using a Borda ranking method based on centrality measures and user tenure. Finally, semantic network analysis was used to explore topic clusters within each OL's posts and their associated user replies.</p><p><strong>Results: </strong>The content analysis showed a high prevalence of informational and emotional support in the administrator-recommended posts. Of the 438 posts, 296 (67.5%) contained social support, with 150 containing informational support and 136 containing emotional support. Social support varied by post theme and whether the intent was to provide or seek it. Among disease knowledge posts, 74 out of 75 provided informational support. Emotional support was most frequently provided in nontreatment sharing posts (28/113) and most frequently sought in treatment experience posts (47/129). The social network analysis identified 10 OLs. The first was a former patient with TB, and the second was a pulmonary TB doctor. Together, they contributed 30.4% (133/438) of all the posts. Across the semantic network analyses of each OL's posts and their associated user replies, informational support was more prominent than emotional support.</p><p><strong>Conclusions: </strong>The findings suggest that the examined TB-related online forum served as an important source of social support for people affected by TB in China, fostering an environment for both informational and emotional support. OLs played an important role by contributing posts and establishing a central position through reply interactions with users.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e79140"},"PeriodicalIF":4.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13080297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147582897","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":"Analyzing Misinformation and Disinformation: Understanding Swiss COVID-19 Narratives Through Natural Language Processing Analysis.","authors":"Federico Germani, Giovanni Spitale, Franc Fritschi, Sonja Merten, Nikola Biller-Andorno","doi":"10.2196/76441","DOIUrl":"10.2196/76441","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic has highlighted the challenges posed by the rapid spread of misinformation and disinformation, exacerbating societal polarization and institutional distrust. Understanding how misinformation and disinformation is understood and framed in public discourse is essential to developing strategies for building societal resilience and promoting informed decision-making during crises.</p><p><strong>Objective: </strong>This study explores the use of the terms misinformation and disinformation across Swiss public discourse during the COVID-19 pandemic, examining their framing within newspaper articles and social media interactions. The findings aim to inform policymakers and journalists or communicators on mitigating the societal impact of misinformation and disinformation through the promotion of a common understanding of the terms misinformation and disinformation.</p><p><strong>Methods: </strong>We analyzed 2 datasets using a natural language processing pipeline, including lemmatization, co-occurrence analysis, and semantic network mapping: media articles retrieved via Factiva and social media posts collected via CrowdTangle.</p><p><strong>Results: </strong>The framing of misinformation and disinformation varied significantly across the datasets. News media highlighted its role in shaping public sentiment, often discussing the tension between journalistic integrity and the amplification of falsehoods. Social media exhibited polarized narratives, with discussions centered on conspiracy theories, distrust in institutions, and grassroots mobilization.</p><p><strong>Conclusions: </strong>Diverging narratives on the very concepts of misinformation and disinformation across public discourse reflect broader societal tensions. Robust journalistic integrity in the media and resilience strategies against misinformation and disinformation involving empowering publics through information literacy approaches are critical to bridging divides and reducing polarization.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e76441"},"PeriodicalIF":4.0,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13035030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147582801","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 Perspectives on a Future HIV Vaccine: Mixed Methods Analysis.","authors":"Megan A Rabin, Sarah Penuela-Wermers, Neil K R Sehgal, Teniola I Egbe, Criswell L M Lavery, Sharath Chandra Guntuku, Alison M Buttenheim","doi":"10.2196/82917","DOIUrl":"10.2196/82917","url":null,"abstract":"<p><strong>Background: </strong>As the prospect of an HIV vaccine nears reality, understanding public discourse around the vaccine is essential for informing communication strategies and addressing misinformation. Social media platforms are influential spaces where public narratives form, yet little research has examined discourse around an HIV vaccine, especially on TikTok.</p><p><strong>Objective: </strong>This study aims to compare and characterize public discourse about a future HIV vaccine across Twitter (subsequently rebranded X) and TikTok, identifying prevailing themes, sentiments, and rhetorical strategies to inform public health communication.</p><p><strong>Methods: </strong>From over 400,000 tweets and 65,000 TikTok comments, we analyzed the 1000 most-liked posts on each platform using natural language processing and coded the top 500 most-liked posts for rhetorical strategies, sentiment, and themes.</p><p><strong>Results: </strong>Our findings reveal expressions of hope and trust in science on both platforms, as well as concerns about institutional corruption and conspiracy theories, such as the belief that the HIV vaccine responds to harm caused by the COVID-19 vaccine. Tweets tended to be more linguistically complex and yielded richer insights, while TikTok comments were shorter and more difficult to interpret without video context. Key rhetorical strategies included conspiracy theories, post hoc reasoning, and emotional appeals.</p><p><strong>Conclusions: </strong>This study underscores the need for platform-specific communication strategies to address misinformation and build public trust. The findings offer timely insights into emerging HIV vaccine discourse and highlight actionable opportunities for public health stakeholders to build trust and combat misinformation in advance of the vaccine rollout.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e82917"},"PeriodicalIF":4.0,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13001999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147488941","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":"Audience-Specific Health Communication: Mixed Methods Evaluation of the Maria Ciência AI-Assisted Knowledge Translation Tool.","authors":"Mariana Araújo-Pereira, Klauss Villalva-Serra, Gustavo Pires-Ramos, Beatriz Sousa-Peres, Joanã Nascimento Conceição-Oliveira, Sarah Dourado Maiche, Rebeca Rebouças da Cunha Silva, Bruno de Bezerril Andrade","doi":"10.2196/78843","DOIUrl":"10.2196/78843","url":null,"abstract":"<p><strong>Background: </strong>Scientific misinformation remains a major barrier to effective health communication. Bridging the gap between academic research and public understanding requires tools that simplify scientific language and adapt content to diverse audiences.</p><p><strong>Objective: </strong>This study presents Maria Ciência (LPCT-IGM), a specialized GPT-based assistant for science communication. The tool supports researchers in translating peer-reviewed scientific findings through simple prompts into accessible, ethically appropriate materials tailored for children, the general public, health professionals, and policymakers.</p><p><strong>Methods: </strong>The tool was configured using prompt engineering techniques and guided by curated reference materials on inclusive and nonstigmatizing scientific language. Materials derived from 47 public health papers resulted in 188 outputs, which were assessed by 121 evaluators using 4 criteria: clarity, level of detail, language suitability, and content quality. In addition, outputs generated by Maria Ciência were compared with those produced by a base large language model and with human-written science communication materials. Readability and linguistic accessibility were assessed using multiple established metrics.</p><p><strong>Results: </strong>Worldwide, mean scores were high: clarity (4.90), language suitability (4.78), content quality (4.72), and level of detail (4.56), on a 5-point scale. Materials for children and the general public consistently achieved the highest ratings across all criteria. A targeted comparison with the base large language model demonstrated superior performance of Maria Ciência in contextual stability. Readability analyses indicated that Maria Ciência's outputs were significantly more accessible than human-written texts, while maintaining high legibility classifications.</p><p><strong>Conclusions: </strong>Maria Ciência demonstrates the potential of artificial intelligence-assisted tools to enhance knowledge translation and counter scientific misinformation by producing scalable, audience-specific content that balances accessibility and informational integrity.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e78843"},"PeriodicalIF":4.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12978924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147438096","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":"Nonnegotiable Symbolic Value and Sugar-Driven Food Habits in Indonesia: Mixed Methods Study Using a Digital Sociological Approach.","authors":"Ewina Efriani Manik, Sudarsono Hardjosoekarto, Ricardi Adnan, Radhiatmoko Radhiatmoko, One Herwantoko, Hakiki Nurmajesty, Darwan Darwan, Farrah Eriska Putri, Astuti Sri Pawening","doi":"10.2196/77261","DOIUrl":"10.2196/77261","url":null,"abstract":"<p><strong>Background: </strong>The sugar market in Indonesia reflects the distinct consumer behavior shaped by economic and deeply rooted cultural factors. This study explores how symbolic values attached to sugar sustain persistent, often irrational or uncontrollable consumption, highlighting the need for a demand-side perspective in the economic sociology of sugar markets.</p><p><strong>Objective: </strong>This study analyzes the nonnegotiable symbolic value of sugar and its implication to uncontrollable consumption in Indonesia. Referring to the framework of product valuation in the social order of markets by Beckert, it offers insights into both the symbolic and material values of sugar.</p><p><strong>Methods: </strong>The applied method complements digital mixed method approaches used in prior research. Digital data from online news and YouTube were visualized through textual network analysis and social network analysis to describe the symbolic and material values of sugar. In-depth interviews with key actors and limited field observations on food and beverage labels were also conducted.</p><p><strong>Results: </strong>Findings reveal that the symbolic value of sugar increases significantly when processed into food or beverages, shaping food habits and habitus across diverse ethnic groups in Indonesia and reinforcing early dependence on sugar. Weak enforcement of labeling regulations on food and beverage packages further impedes shifts in consumer perceptions of the risks of excessive sugar consumption.</p><p><strong>Conclusions: </strong>This study contributes a demand-side perspective to the economic sociology of the sugar market, proposing strategies to address the sugar-driven food habits and habitus from the perspective of consumer behavior. Simultaneously, it assesses producer compliance with regulations on the sweetness level to reduce sugar consumption and the prevalence of noncommunicable diseases.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e77261"},"PeriodicalIF":4.0,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12954692/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147346004","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":"The Impact of Social Media Videos on Quantitative Health Outcomes: Systematic Review.","authors":"Fangyue Chen, Tricia Tay, Hannah Thould, Chatchaya Nangsue, Simon Dryden, Amish Acharya, Ara Darzi, Kate Grailey","doi":"10.2196/77578","DOIUrl":"10.2196/77578","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Social media has transformed the landscape of health communication. Video content can optimally activate our cognitive systems, enhance learning, and deliver accessible information. Evidence has suggested the positive impact of videos on health knowledge and health-related behaviors, yet the impact of social media videos on quantitative health outcomes is underresearched. Evaluating such outcomes poses unique challenges in measuring exposure and outcomes within internet-based populations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We aimed to evaluate the impact of social media videos on quantitative health outcomes, examine methodologies used to measure these effects, and describe the characteristics of video interventions and their delivery.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, MEDLINE, Embase, Web of Science, CINAHL, and Google Scholar were searched. Studies were eligible if they were original research evaluating long-form social media video interventions addressing any health-related condition, delivered via social media platforms, and reported quantitative health outcomes. The primary outcome was the effect of social media videos on quantitative health outcomes. Additional outcomes included participant characteristics, video features, delivery methods, and the use of theoretical frameworks. A narrative synthesis was conducted. A subgroup meta-analysis was performed to synthesize health outcomes mentioned in 2 or more studies with sufficient homogeneity. Risk of bias assessment was conducted using Cochrane Risk of Bias 2, ROBINS-I, or National Institutes of Health Quality Assessment Tool, depending on the study design. One reviewer screened titles and abstracts. Two reviewers independently conducted full-text screening, data extraction, and risk of bias assessment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A systematic search was conducted on October 25, 2023, and was updated on June 12, 2025, yielding a total of 41,172 records after duplicate removal. Sixteen studies were included, involving 4158 participants. Mental health-related conditions were the most studied (10 studies). Most video interventions were delivered via YouTube (12 studies). Studies have reported that video interventions were associated with significant improvements in peri-procedural anxiety, mood, and physical activity levels, although most findings were limited to individual studies with variable methodological quality. Three studies that developed videos with user input and theoretical frameworks significantly impacted study-specific primary outcomes. A subgroup meta-analysis demonstrated a significant moderate impact of online video interventions in improving peri-procedural anxiety (standard mean difference=0.57, 95% CI 0.09-1.05). All but one study showed some concern or high risk of bias.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;We demonstrated a potential p","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e77578"},"PeriodicalIF":4.0,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146230037","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":"Automated Risk Assessment of Opioid Use: Analysis Using Pre-Trained Transformers on Social Media Data.","authors":"Muhammad Ahmad, Rita Orji, Maaz Amjad, Abubakar Siddique, Nailya Kubysheva, Ildar Batyrshin, Grigori Sidorov","doi":"10.2196/77783","DOIUrl":"10.2196/77783","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The illegal use of opioids has emerged as a major global public health concern, contributing to widespread addiction and a growing number of overdose-related deaths. In response, the US federal government has invested billions of dollars in combating the opioid epidemic through treatment, prevention, and law enforcement initiatives. Despite these efforts, there remains an urgent need for automated tools capable of detecting overdose cases and assessing the risk levels of substances-tools that can enable faster, more effective responses with less reliance on human intervention. Social media, particularly Reddit, has become a valuable source of self-reported data on opioid misuse, offering rich insights into user experiences and symptoms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This research aimed to develop an advanced automated tool for detecting opioid overdose risks and classifying substances into high-risk and low-risk categories by analyzing social media posts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A multistage methodology was used to achieve the objectives of this work. First, a new dataset was constructed from Reddit posts and manually annotated. Each post was labeled according to the risk level of the mentioned substance, using contextual indicators and user-reported experiences as the basis for classification. To ensure reliability and annotator consistency, detailed annotation guidelines were developed and applied throughout the labeling process. Second, a bidirectional encoder representation from transformers for biomedical text mining (BioBERT)-based classification framework was implemented and enhanced with a custom attention mechanism to capture relevant semantic information for more accurate predictions. Third, the model's performance was evaluated using 5-fold cross-validation and compared against several baseline approaches, including traditional supervised learning, deep learning, and transfer learning methods. In total, 14 experiments were conducted to evaluate comparative effectiveness. To further assess the contribution of the attention layer, the best-performing model was also evaluated against a version incorporating the standard self-attention mechanism, using a train-test split. Finally, a paired t test was conducted to statistically assess the performance difference between the BioBERT-based model and the strongest baseline, extreme gradient boosting (XGBoost), providing validation of the observed improvements.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The proposed BioBERT model with custom attention achieved an F&lt;sub&gt;1&lt;/sub&gt;-score of 0.99 in cross-validation, outperforming the best baseline, XGBoost (F&lt;sub&gt;1&lt;/sub&gt;-score=0.97), with a relative improvement of 2.06%. A paired t test conducted across the 5 folds (n=5) confirmed that the performance gain was statistically significant (P=.003), providing strong evidence that the improvement reflects genuine advances in overdose risk detection.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusio","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e77783"},"PeriodicalIF":4.0,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13147923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146230042","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 Data for Linguistic Minority Group Research in Canada: Proof-of-Concept Centralized Health Care Metadata Repository Development and Usability Study.","authors":"Vincent Martin-Schreiber, Cayden Peixoto, Ricardo Batista, Christopher Belanger, Peter Tanuseputro, Amy T Hsu, Lise M Bjerre","doi":"10.2196/77242","DOIUrl":"10.2196/77242","url":null,"abstract":"<p><strong>Background: </strong>Language barriers between Canadian patients and health care providers are associated with poorer health outcomes, including decreased patient safety and quality of care, misdiagnosis and longer treatment initiation times, and increased mortality. However, research exploring language as a social determinant of health is limited, as Canadian health data are scattered across many jurisdictions, each with its own policies and procedures. This fragmentation makes it difficult for researchers to identify, locate, and use existing data. This paper presents the results of a pilot study that attempts to address this gap by creating a metadata repository (MDR) to act as a central source of information about what data are available at which data holdings across Canada.</p><p><strong>Objective: </strong>This project aimed to (1) create a proof-of-concept MDR for Canadian health data at the variable level; (2) identify and label language-related variables existing within the MDR data; and (3) develop an interactive, public-facing web application to let users browse and search the MDR.</p><p><strong>Methods: </strong>Metadata were collected from 5 Canadian health data sources, including 4 provincial data holdings and 1 national survey, and pooled to create a data repository. Then, we performed bottom-up labeling of language-related variables within the pooled metadata by first using a search string algorithm across all variable labels, names, and definitions and then consensus screening these variables using a derived, standardized definition of language or linguistic variables. Using the Shiny web framework in R, we then developed an openly accessible web application to allow users to search the proof-of-concept MDR.</p><p><strong>Results: </strong>A total of 850,343 variables were collected and included in the repository, with most coming from Ontario (n=712,037, 83.7%) and Manitoba (n=97,051, 11.4%) provincial data holdings. Among all variables in the repository, 213,696 (25.1%) were confirmed to be language related.</p><p><strong>Conclusions: </strong>Developing a national MDR would be a transformative opportunity for Canadian researchers to leverage the full scope of Canadian health administrative data. Although a top-down approach with consistent engagement of and collaboration between provincial data holdings and federal data agencies is ideal to develop a national MDR, this study demonstrates the feasibility of a bottom-up approach in contributing to this overarching goal.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":" ","pages":"e77242"},"PeriodicalIF":4.0,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12930145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145992290","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}