JMIR infodemiology最新文献

{"title":"Misinformation Through the Lens of Dutch News Media and Public Health Authorities: Content Analysis of Topics and Narratives Present in Pandemic-Related Misinformation During COVID-19.","authors":"David Blanco-Herrero, Bas van den Putte, Toni G L A van der Meer","doi":"10.2196/91743","DOIUrl":"https://doi.org/10.2196/91743","url":null,"abstract":"<p><strong>Background: </strong>One of the multiple challenges associated with misinformation is its risk of hindering public health efforts during a health crisis. Misinformation can cover a diversity of thematic topics and is usually spread using a series of nontopic-specific recurring narrative structures. Identifying these 2 misinformation features helps to understand underlying structures and patterns through which misleading information can take shape or be circulated.</p><p><strong>Objective: </strong>The goal of the study is to obtain a complete overview of the topics and narratives of pandemic-related misinformation cases addressed by 2 key information providers during the COVID-19 pandemic in the Netherlands: news media and public health authorities.</p><p><strong>Methods: </strong>A quantitative content analysis was conducted on 698 news articles and 1333 tweets published between January 2020 and May 2023.</p><p><strong>Results: </strong>The study revealed that vaccine-related misinformation, both in terms of topics and narratives, was the most frequently addressed type of misinformation across both sources. The topics and narratives evolved along the pandemic, with the virus origin and danger being more predominant in the misinformation addressed in the early phases and the vaccines gaining presence around 2021.</p><p><strong>Conclusions: </strong>In total, the study revealed the dominance of vaccine-related narratives as well as the change of misinformation features over time. The deeper understanding of misinformation topics and narratives can inform communication strategies to counter its effects, focusing on the appropriate topics and narrative structures in different moments.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e91743"},"PeriodicalIF":4.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying the Vertically Integrated Project Model to Youth-Inclusive Social Media Coding of Zyn-Related YouTube Shorts: Methodological Case Study.","authors":"Julia Pengyue Dou, Beth L Hoffman, Piper Narendorf, Tatiana Grinberg Limoncic, Grace Carver, Natalia Connor, Christine Larkin, Jaime E Sidani","doi":"10.2196/95363","DOIUrl":"https://doi.org/10.2196/95363","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The rapid evolution of the nicotine and tobacco product (NTP) marketplace continues to outpace existing research. Although adolescents are active participants on social media who both consume and generate content, this age group has rarely been included in analyses of NTP content, raising a methodological challenge for accurate interpretation and coding of youth-oriented NTP content. Most research examining oral nicotine product content online has focused on TikTok, despite YouTube's widespread adolescent use. One promising framework is the vertically integrated project (VIP) model. Situating content analysis within the VIP model suggests the methodological potential of age-diverse teams to capture nuances in social media data coding, reveal areas of interpretive divergence, and ultimately advance research methodologies in public health.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to understand how perspectives across different age groups, academic levels, and lived experiences shape the interpretation of Zyn-related YouTube shorts by engaging high school (HS) students, undergraduate public health students, and faculty researchers in parallel coding.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We manually collected 300 publicly available YouTube shorts containing #Zyn on September 18, 2024, as a methodological case example. Three coder pairs, including 2 HS students (GC and NC), 2 undergraduate students (PN and TGL), and 2 public health faculty members (BLH and JES), independently coded the same 50 videos per round across 6 iterative rounds (N=300). For each round, interrater reliability was assessed using Cohen κ and percent agreement as complementary indicators. After all coding rounds were completed, the full coding team participated in a debriefing session. The transcript was analyzed using a combined deductive-inductive thematic approach to contextualize reliability patterns and identify challenges and practical insights related to age-diverse collaborative coding within the VIP-informed model.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Interrater reliability varied across 6 iterative rounds, with the undergraduate team showing the highest κ values in the final coding round (κ range: 0.40-0.63). Several constructs showed consistently high percent agreement but low and variable κ values, particularly within the HS team. For individual codes, mean κ values for male stereotype were higher for the undergraduate and faculty teams than for the HS team (0.56, 0.47, and 0.10, respectively). Debriefing findings indicated that disagreement reflected factors such as evolving code definitions, interpretive drift across rounds, and differences in cultural and experiential lenses. Coders noted that the codebook struggled to capture ambiguity in short-form content and that teams developed distinct heuristics for resolving uncertainty. Suggested improvements included rotating coding pairs and expanding codebook examples.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclu","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"6 ","pages":"e95363"},"PeriodicalIF":4.0,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148882812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing Online Opinion on Vaping and E-Cigarettes in X (Formerly Twitter) Discourse in Singapore: Infodemiology Study.","authors":"Charles Alba","doi":"10.2196/103561","DOIUrl":"https://doi.org/10.2196/103561","url":null,"abstract":"<p><strong>Unstructured: </strong>As Singapore enforces a comprehensive e-cigarette prohibition, this infodemiology analysis of vaping-related X (formerly Twitter) discourse reveals predominantly neutral, event-driven discussions across 18 topics covering legislation and policy, illicit consumption, illicit sales and smuggling, and health risks, offering insights to inform vaping-control strategies.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emotional Expression and Mental Health Support in BTS Fandom Communities Using Natural Language Processing on YouTube Comments: Cross-Sectional Study.","authors":"Nari Yoo, Aaron H Rodwin, Michael Park, Sangpil Youm, Sou Hyun Jang","doi":"10.2196/74397","DOIUrl":"10.2196/74397","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The global rise of K-pop has shaped youth culture and online communities, particularly through BTS, a South Korean boy band with an international fanbase known as ARMY (Adorable Representative MC for Youth). Music fandoms are increasingly engaging with digital platforms such as YouTube not only for entertainment but also as spaces for emotional expression and mutual support. Despite growing interest in the mental health potential of music-based coping strategies, limited research has examined how fandom cultures differentially express emotional needs and supportive interactions online.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study investigates specific mental health language patterns and coping mechanisms expressed by BTS fans in online spaces, examining how different linguistic features (including self-referential language and emotional expression patterns) may reflect psychological states and mental health needs. We used YouTube comments from fan-curated \"sad\" or \"depression\" playlists of BTS. We further included YouTube comments from equivalent Taylor Swift playlists as a reference group.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Using natural language processing and Linguistic Inquiry and Word Count 2022 software, we analyzed a total of 13,224 YouTube comments: 11,772 comments on BTS \"sad playlist\" videos and 1452 comments on Taylor Swift equivalents. Statistical comparisons were conducted to evaluate differences in comment length, word count, pronoun use, and emotional valence. Representative comments were examined to contextualize the emotion classification results.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;BTS original comments were significantly longer (mean 253.38, SD 703.65 characters) and had higher word counts (mean 38.93, SD 88.54 words) than Taylor Swift original comments (length: mean 89.84, SD 330.96 characters; word count: mean 16.08, SD 64.78 words; P&lt;.001). BTS fans used more first-person singular pronouns (mean 10.24%, SD 9.57% vs mean 7.43%, SD 9.41%) and expressed greater sadness (1691/5341, 31.7% vs 75/452, 16.6%). In contrast, Taylor Swift fans exhibited higher admiration (86/452, 19% vs 429/5341, 8%). Among reply comments, BTS fans demonstrated more caring (242/1729, 14% vs 7/128, 5.5%), gratitude (294/1729, 17% vs 15/128, 11.7%), and optimism (162/1729, 9.4% vs 6/128, 4.7%). Linguistic analysis also revealed a broader international user base for BTS, including higher proportions of Spanish (719/11,772, 6.11%) and Portuguese (222/11,772, 1.89%) comments. Examination of comment content showed that fans used these spaces to disclose personal struggles, express gratitude for the community, and offer peer support, with many describing the fandom as a safe space for emotional expression they could not access elsewhere.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The findings show that comments on BTS fan playlists included more emotional disclosure and more supportive replies than those on the Taylor Swift comparison, consi","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":" ","pages":"e74397"},"PeriodicalIF":4.0,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147446240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"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":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Tweets were not uniformly distributed across different screening programs and over time (&lt;i&gt;χ&lt;/i&gt;²&lt;sub&gt;36&lt;/sub&gt;=4045.99, n=55,403&lt;i&gt;; P&lt;/i&gt;&lt;.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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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":"&lt;p&gt;&lt;strong&gt;Unlabelled: &lt;/strong&gt;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}