JMIR infodemiology最新文献

{"title":"Investigating Online Discussions About Cancer Screening on Twitter (Subsequently Rebranded as X): Corpus Analysis.","authors":"Martin-Pieter Jansen, Hanneke Hendriks, Suzan Verberne, Gert-Jan de Bruijn, Enny Das","doi":"10.2196/90916","DOIUrl":"10.2196/90916","url":null,"abstract":"<p><strong>Background: </strong>While cancer screening is proven to be effective in the early detection of the disease and early detection enables better treatment options, screening uptake has been declining. Research shows that online health information helps people to make health-related decisions. However, not all online health information is credible, and misinformation might play a role in people's choice to take part in screening.</p><p><strong>Objective: </strong>This study aimed to analyze online discussions about cancer screening programs using corpus analysis. Specifically, we aimed to investigate the full dataset through corpus analysis and misinformation in a manually coded subset. This enabled us to study naturalistic discussions about cancer screening over time, what information people share, and how prevalent misinformation is in these discussions. We differentiated tweets on Twitter (subsequently rebranded as X) for cervical, breast, colorectal, and general screening.</p><p><strong>Methods: </strong>We extracted a corpus of 55,403 tweets from 2011 to 2023, tweeted by 22,493 users from a database containing over 5.9 billion tweets. We used specific search strings corresponding to different types of screening to gather our corpus. The corpus consisted of tweets, timestamps, hashtags, and shared URLs. We used a machine learning classifier trained on another dataset of tweets about cancer screening to automatically code whether a tweet fell within the scope of the study. We manually coded a randomly drawn stratified subset of 1200 tweets representative of the full corpus regarding year and screening program for the presence of misinformation.</p><p><strong>Results: </strong>Tweets were not uniformly distributed across different screening programs and over time (<i>χ</i>²<sub>36</sub>=4045.99, n=55,403<i>; P</i><.001). Most tweets discussed population screening in general (n=35,199), and the volume of tweets increased around real-world events. Hashtags in the tweets predominantly focused on the screening programs that were discussed in those tweets. In our corpus, most shared URLs linked to other tweets (n=10,569) or news websites (n=2807). In our coded subset, information was shared in 679 tweets. Overall, 23 tweets contained misinformation. Topics in those tweets showed criticism toward the programs and policies, suspicions about conflicts of interest, and antivaccination beliefs regarding human papillomavirus (HPV). Most users used rhetorical questions, sarcasm, fearmongering, or expressed anger.</p><p><strong>Conclusions: </strong>Our findings reveal that cancer screening programs are actively debated across social media platforms. We observed that conversations tend to spike in response to real-world events, suggesting social media can serve as a valuable lens into public reactions to health policy changes. Link-sharing behavior was common, though we noted a tendency for sources to reference back to the same platform where discus","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e90916"},"PeriodicalIF":4.0,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13536980/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148882840","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":"Multilayered 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":"<p><strong>Unlabelled: </strong>Generative AI 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. This paper introduces the Multilayered 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). Adopting a socioecological and structural epistemic approach, this viewpoint synthesizes empirical findings from communication psychology, medical sociology, and digital infodemiology, and the MEDF is explicitly positioned relative to established health communication frameworks, including the i-frame and s-frame distinction (individual-level vs system-level intervention targets) and socioecological infodemic models, with each construct's novelty defined in relation to adjacent concepts in prior literature. 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 health care institutions (odds ratio 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 Content Provenance and Authenticity standards, automated deepfake detection (showing area under the curve 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. Addressing AI-driven health misinformation requires moving beyond individual-level i-frame interventions toward structural, s-frame policy responses calibrated to each layer of the MEDF cascade. Policymakers an","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e96664"},"PeriodicalIF":4.0,"publicationDate":"2026-08-20","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":"148802569","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":"How Social Media Analysis Offers an Opportunity to Understand the Reality of People Living With Multiple Sclerosis: Descriptive French Study.","authors":"Emmanuelle Leray, Stéphane Schück, Pamela Voillot, Nathalie Texier","doi":"10.2196/88763","DOIUrl":"10.2196/88763","url":null,"abstract":"<p><strong>Background: </strong>Multiple sclerosis (MS) is a chronic neurological disease that starts in young adulthood and can significantly affect quality of life (QoL) due to various symptoms, and the risk of disability. MS directly affects people living with the disease and indirectly affects their relatives and family caregivers.</p><p><strong>Objective: </strong>The objective of this social media analysis was to identify the main topics of discussion among people affected by MS and their perceptions of the impact of MS on their QoL.</p><p><strong>Methods: </strong>Publicly available French messages, posted between January 2017 and October 2022, were retrieved using an extraction query that contained keywords related to MS. The effects on QoL were detected using a machine learning algorithm specifically trained on social media data. Five specific models covered the following health-related QoL dimensions: physical well-being, psychological well-being, daily activities (including professional and academic activities), social or relational well-being, and material well-being. Descriptive statistics were provided and illustrated with quotes from social media.</p><p><strong>Results: </strong>The analysis corpus for the 2017 to 2022 period included 3225 messages corresponding to 2034 different social media users, either people living with MS (654/3225, 20%) messages or family caregivers (2571/3225, 80%) messages, identified from 32 sources. Women represented 42.5% (864/2034) and men represented 28.2% (574/2034) of social media users (gender was unknown for 596/2034, 29.3%), and their mean age was 35 (SD 6.6) years. The 2 main themes of posts were \"Caregivers and family members\" (1032/3225, 32%) and \"Disability\" (774/3225, 24%). Overall, 847 messages described at least one impact of MS on QoL: relational or social (n=431, 50.9%), physical (n=284, 33.5%), psychological (n=76, 9.0%), financial or material well-being (n=32, 3.8%), and daily activities (n=24, 2.8%).</p><p><strong>Conclusions: </strong>Our findings confirm the high impact of MS on everyday life and QoL for both patients and family caregivers. Caregivers were the most numerous to express themselves and post messages on social media. The most affected QoL dimension was relational or social well-being, which is probably linked to the fact that social networks and digital patient communities are places for discussion, sharing experiences, and looking for support. These findings confirm that social media is a way for people affected by MS to express themselves and look for support and understanding. They also show that social media provides an opportunity to discover the fears, questions, needs, and thoughts of those affected by the disease, particularly caregivers, who are rarely considered in research studies.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e88763"},"PeriodicalIF":4.0,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13504247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802554","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":"Comparative Content Analysis and Expert Physician Risk Assessment of Aesthetic Procedures Promoted on TikTok in Taiwan: Quantitative Study.","authors":"Cheng-Jung Wu, Sheng-Yu Wu, Cheng-Yu Tsai, Arnab Majumdar, Jeffrey Yang, Jinn-Moon Yang, Lok-Yee Joyce Li","doi":"10.2196/88227","DOIUrl":"10.2196/88227","url":null,"abstract":"<p><strong>Background: </strong>Algorithm-driven social media platforms such as TikTok are increasingly creating stratified layers in the cosmetic medical market, thereby influencing patient decisions and safety. In Taiwan, TikTok has 2 parallel markets: one is a formal tier promoting \"cosmetic surgery tourism\" to the public and the other is an underground tier targeting Southeast Asian migrant workers, providing informal, high-risk services.</p><p><strong>Objective: </strong>In this study, we quantified the clinical risks embedded within these hierarchical markets and demonstrated how digital platforms exacerbate health inequalities through algorithm-driven social media content delivery.</p><p><strong>Methods: </strong>We conducted a dual-track content analysis of 60 TikTok videos (n=30 per group). A panel of 6 specialist physicians independently evaluated the videos using the newly developed Clinical Legitimacy and Risk Scoring (CLRS) scale. This scale assesses videos across 4 dimensions: depicted environment, operator identity, risk communication, and communication channels. Interrater reliability was evaluated using Fleiss κ.</p><p><strong>Results: </strong>The CLRS was used by 6 specialist physicians and showed a significant safety difference between the two groups. The medical tourism group (model A) had an average video score of 3.2 (SD 0.7), with the primary content type of short-form videos being \"surgery experience vlogs\" (9/30, 30%). Concomitantly, although professionalism was apparent in these videos, underlying risks were regularly obscured. In contrast, the migrant worker group (model B) had an extremely low average score of 1.1 (SD 0.3), indicating a complete deviation from medical standards (P<.001) and thus posing a higher risk to patient safety. The videos in model B were mainly \"home surgery demonstrations\" (15/30, 50%). The interrater reliability among physicians was high (Fleiss κ=0.85; P<.001). The primary coding team compiled the baseline descriptive data, whereas the board-certified physician panel independently evaluated the specific CLRS metrics.</p><p><strong>Conclusions: </strong>The algorithmic architecture of TikTok creates a stratified marketplace, reinforcing socioeconomic stratification and health inequities. Specifically, regarding the informal aesthetic medical tier for migrant workers, the risk of infection has become a serious and urgent public health threat. Results of this study indicate that responsible public health interventions are urgently needed for the informal aesthetic medical tier targeting migrant workers and that there is a need to reassess TikTok's social responsibility in reviewing high-risk medical content in short-form videos.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e88227"},"PeriodicalIF":4.0,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802562","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 Reactions to Sex Toy Criminalization in Ghana: Cross-Sectional Sentiment and Text Network Analysis.","authors":"Anthony Senanu Agbeve, Celestin Niyomugabo, Celestin Hakizimana, Daniel Yaw Fiaveh, Amanda Yad-El Ugboji, Caroline Pukall, Martina Anto-Ocrah","doi":"10.2196/91422","DOIUrl":"10.2196/91422","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Sexuality discourse in Ghana has become more divided, especially around gender and lesbian, gay, bisexual, transgender, and queer rights, leading to new antigay laws criminalizing lesbian, gay, bisexual, transgender, and queer activities, and other forms of sexual practices. One of the most controversial pieces of legislation, previously titled \"The Promotion of Proper Human Sexual Rights and Family Values Bill,\" proposed in 2021, sought to criminalize the use of sex toys, sparking opposition from leading political figures. The then minister of communication, Ursula Owusu-Ekuful, a staunch women's rights advocate, publicly opposed the clause criminalizing sex toys, stating that such initiatives infringed on women's sexual autonomy. Her stance sparked heated debates on several social media platforms, reflecting the broader tensions surrounding conversations about sexuality in Ghana. To better understand the public's reactions to the minister's counterproposal, we analyzed comments from various social media platforms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Specifically, we (1) investigated public perceptions and sentiments regarding the criminalization of sex toys as expressed on social media, and (2) identified whether sentiments differed across social media platforms (eg, are comments on X [X Corp] more negative compared to Instagram or Facebook [Meta Platforms, Inc]?).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Using Python (Python Software Foundation) as a web scraping tool, we extracted public comments from Instagram, Facebook, X, and YouTube (YouTube, LLC). Comments were analyzed using natural language processing to categorize sentiments (positive, negative, and neutral, indicating support for opposition to the bill, support for introducing the bill, and no clear stance in either direction, respectively), and text network analysis was used to identify keywords and their relationships to central themes. We used conditional probability analyses to determine the likelihood that a given comment was classified as negative, positive, or neutral, depending on its source (Instagram, X, Facebook, or YouTube).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 951 comments were retrieved: 62.7% (n=596) from Instagram, 28.2% (n=268) from X, 6.2% (n=59) from Facebook, and 2.9% (n=28) from YouTube. Negative sentiments dominated (n=631, 66.4%), with only 13.3% (n=126) positive and 20.4% (n=196) neutral sentiments. Strongly disapproving language-some directed towards the minister-was common. This included terms such as \"lesbian,\" \"disgrace,\" and \"disgusting.\" X showed the highest proportion of negative sentiment (190/268, 70.9%), followed by Facebook (41/59, 69.5%), YouTube (18/28, 64.3%), and Instagram (382/596, 64.1%). Conditional probability analysis showed that Facebook and X had the highest likelihood of negative comments (0.7), and sentiment distribution differed significantly across platforms (P&lt;.001). Text network analysis revealed recurring","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e91422"},"PeriodicalIF":4.0,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13460675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148714871","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":"Temporal Dynamics of Influenza-Associated Anxiety Symptom Linguistic Markers on Weibo (2023-2024): Observational Study.","authors":"Yifan Ou, Gert-Jan de Bruijn, Peter Johannes Schulz","doi":"10.2196/88849","DOIUrl":"10.2196/88849","url":null,"abstract":"<p><strong>Background: </strong>Influenza seasons may be associated with increased anxiety-related expressions on social media. Social media can reflect population-level emotional expression patterns in real time.</p><p><strong>Objective: </strong>The aim of the study is to characterize diurnal and full-season dynamics of anxiety-related language during the 2023-2024 influenza season in China and its association with influenza activity.</p><p><strong>Methods: </strong>We retrieved Sina Weibo posts in February 2025 covering September 4, 2023, to April 28, 2024. Posts containing Diagnostic and Statistical Manual of Mental Disorders (DSM)-based anxiety terms were cleaned and deduplicated (N=169,728 → 106,440). We first linked weekly influenza incidence with anxiety-related postings. Then, we plotted diurnal patterns by epidemiologic phase, conducted supplementary within-sample hourly normalization analyses, and modeled longitudinal symptom trajectories using ARIMA (autoregressive integrated moving average) and supplementary ARIMAX (autoregressive integrated moving average with exogenous regressors) time-series models.</p><p><strong>Results: </strong>Anxiety-related posts closely followed influenza activity, surging during the outbreak and peak phases and remaining elevated even after influenza declined. Initial Spearman correlation analyses showed significant negative associations for irritability (r=-0.413; P=.02) and restlessness or feeling keyed up or on edge (r=-0.396; P=.02). However, supplementary ARIMAX analyses further revealed that being easily fatigued and difficulty concentrating or mind going blank exhibited more stable positive temporal associations with influenza activity after controlling for autocorrelation and lagged effects. Diurnal patterns shifted across stages, showing mild early-evening variation during the outbreak, clear morning peaks with secondary afternoon and evening rises during prevalence and decline, and morning-afternoon concentration in the end stage. Supplementary within-sample hourly normalization analyses showed that the major diurnal structures remained generally stable after normalization. ARIMA time-series analysis revealed that irritability and being easily fatigued consistently dominated the discussions, whereas others remained at relatively low levels. Out-of-sample forecasting based on a chronological 80% training and 20% testing split suggested generally stable short-term trajectories, with being easily fatigued showing a slight increase.</p><p><strong>Conclusions: </strong>This study demonstrates how social media can capture diurnal and seasonal fluctuations of anxiety symptoms associated with influenza activity, advancing understanding of affective dynamics in population health.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e88849"},"PeriodicalIF":4.0,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13450889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148690088","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":"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":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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&lt;.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&lt;.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&lt;.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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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}