JMIR infodemiology最新文献

{"title":"Impact of Repeated Exposure to Polarized Health-Related News on Explicit and Implicit Attitudes Toward Dietary Supplements: Online Experimental Study.","authors":"Eugen-Călin Secară, Nicolae-Adrian Opre","doi":"10.2196/88632","DOIUrl":"10.2196/88632","url":null,"abstract":"<p><strong>Background: </strong>Repetition is a central feature of digital news consumption, where engagement-driven algorithms often expose users to similar health-related content. Prior research suggests that repeated exposure can influence perceived truth and evaluation, but most studies use brief or decontextualized stimuli and have rarely distinguished between explicit and implicit attitudes. Little is known about how repeated exposure to full-length, polarized health news shapes explicit and implicit attitudes, particularly toward familiar products such as dietary supplements.</p><p><strong>Objective: </strong>This study aims to examine whether 2 weeks of exposure to positively, negatively, or mixed-valence health news articles would alter explicit and implicit attitudes toward dietary supplements, and whether engagement mediated these effects or initial attitudes moderated them.</p><p><strong>Methods: </strong>In a preregistered 4 (group: PRO, CON, MIX, and control) × 3 (time: T0, T1, and T2) mixed experimental study, 228 participants (174 women, PRO: n=68, CON: n=51, MIX: n=52, control: n=57; mean age 2.81, SD 4.88 years) were randomly assigned to receive one full-length article per day for 2 weeks. Articles presented positive (PRO), negative (CON), mixed (MIX), or neutral space-related information (control). Explicit attitudes toward dietary supplements (perceived efficiency, harmfulness, and willingness to recommend) were assessed with visual analog scales, and implicit attitudes with an Implicit Association Test at baseline, 1 week, and 2 weeks. Mixed analysis of covariances (ANCOVAs) controlled for self-reported exposure to health-related news. Mediation, moderation, and moderated mediation analyses examined whether time spent reading and baseline attitudes influenced change.</p><p><strong>Results: </strong>The change in implicit attitudes was not significant (F6, 428=1.90; P=.08). In contrast, explicit attitudes showed a significant group × time interaction (F5.04, 428=14.34; P<.001). Explicit attitudes became more favorable in the PRO group (MΔ=14.86, SE=5.93, d=0.30; P=.02) and less in the CON (MΔ=-53.42, SE=7.35, d=1.02; P<.001) and MIX groups (MΔ=-24.56, SE=6.77, d=0.50; P=.001). At T2, the CON group reported lower explicit attitudes than the control group (MΔ=-32.84, SE=1.75, d=0.32; P=.02), and the PRO group scored higher than the CON and MIX groups (MΔ=52.08, d=0.52; P<.001 and MΔ=27.07, d=0.29; P=.03). Exploratory item analyses showed large effects of negative exposure on perceived harmfulness and recommendability. No mediation by reading time and no moderation by baseline attitudes were supported.</p><p><strong>Conclusions: </strong>Repeated exposure to polarized health-related news shifted explicit but not implicit attitudes toward dietary supplements. Negative content exerted the strongest influence. Engagement and prior attitudes did not meaningfully shape these outcomes. These findings suggest that even brief, routine ex","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e88632"},"PeriodicalIF":4.0,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13404937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148610952","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 the Discourse Around Zyn Nicotine Pouches on Instagram and TikTok: Content Analysis.","authors":"Arpita Tripathi, Beth Hoffman, Christine Larkin, Piper Narendorf, Chaim Kittredge, Coltin Kunz, Jaime E Sidani","doi":"10.2196/88825","DOIUrl":"10.2196/88825","url":null,"abstract":"<p><strong>Background: </strong>Oral nicotine pouches, such as Zyn, have rapidly grown in popularity in the United States, with sales increasing from 83.2 million cans in 2020 to 385 million cans in 2023. This growth has occurred alongside concerns about youth use. At the same time, Zyn's visibility on social media has also expanded, where youth-targeted content may shape perceptions and influence product uptake.</p><p><strong>Objective: </strong>This content analysis of Instagram and TikTok Zyn-related posts aimed to (1) examine their sentiment and content, (2) assess youth appeal, (3) identify potential misinformation, and (4) report the most frequently used hashtags in the selected posts as indicators of platform-specific framing and audience targeting.</p><p><strong>Methods: </strong>In March 2024, we collected 10,502 Instagram posts and 609 TikTok posts with #Zyn. We used a systematically developed codebook to guide the analysis of a random Instagram subsample (n=1200) and all TikTok posts (n=609). Interrater reliability was assessed using a Cohen κ of more than 0.80 and percent agreement.</p><p><strong>Results: </strong>Many coded posts expressed positive sentiment (n=789/887, 88.9%) and normalized Zyn use through comedic content (n=308/887, 35.2%). Posts revealed youth-targeted themes, including appealing flavors (n=417/887, 87.6%); Zyn usage methods (n=159/887, 18.1%); and associations with sports, athletic, and gym settings (n=70/887, 8%). Both platforms contained posts with potential misinformation. While TikTok featured more influencer-generated content, Instagram showcased more business and commercial content.</p><p><strong>Conclusions: </strong>Findings suggest that Zyn-related social media content may appeal to youth. Zyn and other oral nicotine pouches are often presented favorably with engaging content, underscoring the need for updated regulatory strategies to address potential misinformation and their appeal to youth.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e88825"},"PeriodicalIF":4.0,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13392921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148564103","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":"Investigation of Differences in Tobacco Use Language Between Groups: Corpus-Assisted Analysis.","authors":"Iona Fitzpatrick, Xinmei Sun","doi":"10.2196/86593","DOIUrl":"10.2196/86593","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The variation of language concerning tobacco products and tobacco use is known to impact the understanding of related risks and influence behaviors including use uptake and product cessation. Transnational tobacco companies can use such complexities to change the acceptability of tobacco use and influence public understanding of related risks. These changes, in turn, impact tobacco use behaviors. Looking at variations in the language used by different groups can therefore offer helpful insights into tobacco use cultures and make imbalances of information between groups plain. This paper examines the language of tobacco use, specifically smoking, across a sample of health organizations (the National Health Service, the World Health Organization, the National Institute for Health and Care Excellence, and the Centers for Disease Control and Prevention), the tobacco industry (British American Tobacco and Philip Morris International), and in \"general English.\"&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Through corpus-assisted analysis, the study aims to illustrate differences in tobacco use and tobacco user characterizations by 2 transnational tobacco companies, health organizations, and users of general English. The study assesses the possible implications of these differences for public health.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We queried 4 bodies of text (corpora) from 3 different groups; 2 of these corpora were preexisting corpora of general spoken and written English in the United Kingdom and the United States. The remaining 2 sampled tobacco-related documents from 2 transnational tobacco companies and from the United Kingdom and international organizations with a focus on health between 2003 and 2023. The 2 sampled corpora contained 1355 documents and 10,023,538 words. We used the corpus analysis software LancsBox (Lancaster University) to identify variations in characterizations of tobacco users and tobacco use behaviors between these groups, using the stems \"smoker*\" and \"smok*.\"&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Frequency and collocation analysis showed clear differences in how the 3 groups described smokers and smoking, with only limited overlap in the terms they used. Only 7 of 23 unique categorizations of \"smoker*\" were shared. There was a significant association (P&lt;.001) between individual corpora and singular or plural noun forms. Health organization texts more often used clinical and population-focused classifications, whereas tobacco industry texts more often framed smokers as consumers or people involved in legal disputes. General English corpora showed the widest range of labels for tobacco users.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;While there was some overlap in terminology used between corpora, the most common categorizations in each corpus were highly varied, showing very little shared language between groups in their descriptions of either tobacco users or use behaviors. This variance indicates that these grou","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e86593"},"PeriodicalIF":4.0,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13372072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458050","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":"Multi-layered Epistemic Disruption in AI-Driven Health Misinformation: Conceptual Framework and Viewpoint.","authors":"Muzaffer Malkoç","doi":"10.2196/96664","DOIUrl":"10.2196/96664","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Generative AI (GenAI) has transformed the health information ecosystem by enabling scalable, sophisticated health misinformation production at near-zero marginal cost. Current literature addresses AI's role in health misinformation predominantly through a binary threat-detection framework, systematically overlooking the structural, multilayered mechanisms through which AI simultaneously embeds false claims across intersecting human trust systems.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This paper introduces the Multi-layered Epistemic Disruption Framework (MEDF), which conceptualizes how AI-driven health misinformation structurally undermines public trust through four interdependent layers of cognitive and institutional disruption: discursive (clinical language shielding: fluent medical terminology and fabricated citations deployed as credibility signals), biometric (embodied authority transfer: deepfake appropriation of real clinicians' faces and voices), temporal (the synthetic chorus effect: near-simultaneous fabrication of apparently independent corroborating sources), and systemic (structural epistemic erosion: cumulative macro-level collapse of trust in medical institutions).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Adopting a socioecological and structural epistemic approach, this Viewpoint synthesizes empirical findings from communication psychology, medical sociology, and digital infodemiology. The MEDF is explicitly positioned relative to established health communication frameworks, including the i-frame/s-frame distinction (individual-level vs system-level intervention targets) and socioecological infodemic models, and each construct's novelty is defined in relation to adjacent concepts in prior literature.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The MEDF proposes that AI-driven health misinformation is distinctively dangerous due to its capacity to exploit variable individual receptivity to medical authority claims and to simultaneously lower epistemic thresholds across multiple trust layers. Population-level data indicate that individuals who frequently encounter health misinformation on social media are 1.66 times more likely to report systemic distrust of healthcare institutions (OR 1.66; 95% CI 1.11-2.48). Perceptual studies document that listeners correctly identify AI-generated voice clones only about 60% of the time and perceive a cloned voice as identical to its real counterpart in approximately 80% of trials. Existing defenses - including C2PA provenance standards, automated deepfake detection (showing AUC drops of up to 50% under real-world conditions), and prebunking interventions - are shown to address only subsets of the proposed cascade, leaving temporal and systemic layers substantially unmitigated. Four testable hypotheses are advanced for empirical validation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Addressing AI-driven health misinformation requires moving beyond individual-level i-frame interventions toward str","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13494861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148427019","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":"Measuring Safety Risks in Online Drug Sales: Empirical Study Based on Complex Network Theory.","authors":"Rong Jiang, Bingke Xing, Lixin Shu","doi":"10.2196/86876","DOIUrl":"10.2196/86876","url":null,"abstract":"<p><strong>Background: </strong>With the rapid development of pharmaceutical e-commerce, online drug sales have led to additional safety risks, while providing convenient services to the public. These risks pose challenges to public health security and regulatory systems.</p><p><strong>Objective: </strong>This study aimed to quantitatively measure the safety risks of online drug sales.</p><p><strong>Methods: </strong>We collected data on cases of violation in online drug sales from government websites and established a complex network model consisting of 116 nodes and 152 edges for the safety risks in online drug sales based on complex network theory. We explored network topological properties through complex network characteristic indicators (eg, average path length, diameter, degree, and degree distribution). In addition, we followed the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to identify key risk factors in online drug sales safety, calculate the relative nearness degree value of each risk node, and rank them accordingly.</p><p><strong>Results: </strong>Data on 458 cases of violation were collected. The key risk nodes in the complex network model for safety risks in online drug sales were identified as the failure to (1) strictly perform prescription review and dispensing duties, (2) issue prescriptions through internet hospitals, (3) establish drug procurement and acceptance records, and (4) establish drug sales records, in addition to (5) consumers failing to provide prescriptions.</p><p><strong>Conclusions: </strong>Based on the risk measurement results, regulatory authorities can formulate targeted risk prevention and control measures for key risks, such as failure to strictly perform prescription review and dispensing duties and consumers failing to provide prescriptions. By building a tighter and stronger regulatory network for online drug sales and improving regulatory efficiency, this approach could maintain order in the online drug sales market and enhance the overall level of public health security.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e86876"},"PeriodicalIF":4.0,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13389469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148400270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

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

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

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