#Infodemic hashtag - Bluesky

@jmirpub.bsky.social

2 hours ago

Understanding Social Support and Opinion Leaders in a Tuberculosis-Related Online Community in China: Content and Network Analyses Background: Tuberculosis (TB) remains one of the world’s deadliest infectious diseases. Yet, despite the growing role of online health communities (OHCs) as key sources of social support, research on TB-related online communities remains scarce. Network analysis has been increasingly used to study OHCs and identify opinion leaders (OLs), offering a valuable approach to advancing knowledge about TB-related online communities. Objective: This study examined the types of social support and the influence of OLs in a prominent TB-related online forum in China, with a particular focus on its curated subforum that served as a centralized space for user interaction. The subforum consisted of posts recommended by the forum’s administrator and the corresponding user replies they generated. Methods: The data consisted of all 438 administrator-recommended posts and the 150,570 associated user replies over 18 years, from the forum’s launch in 2004 to 2021. The study used content analysis to examine the types of social support present in administrator-recommended posts, which are commonly considered high-quality. It then applied social network analysis to these posts and their associated user replies to identify OLs by using a Borda ranking method based on centrality measures and user tenure. Finally, semantic network analysis was used to explore topic clusters within each OL’s posts and their associated user replies. Results: The content analysis showed a high prevalence of informational and emotional support in the administrator-recommended posts. Of the 438 posts, 296 (67.5%) contained social support, with 150 containing informational support and 136 containing emotional support. Social support varied by post theme and whether the intent was to provide or seek it. Among disease knowledge posts, 74 out of 75 provided informational support. Emotional support was most frequently provided in nontreatment sharing posts (28/113) and most frequently sought in treatment experience posts (47/129). The social network analysis identified 10 OLs. The first was a former patient with TB, and the second was a pulmonary TB doctor. Together, they contributed 30.4% (133/438) of all the posts. Across the semantic network analyses of each OL’s posts and their associated user replies, informational support was more prominent than emotional support. Conclusions: The findings suggest that the examined TB-related online forum served as an important source of social support for people affected by TB in China, fostering an environment for both informational and emotional support. OLs played an important role by contributing posts and establishing a central position through reply interactions with users.

JMIR Infodemiology: Understanding Social Support and Opinion Leaders in a Tuberculosis-Related Online Community in China: Content and Network Analyses #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

1 day ago

JMIR Infodemiology: Analyzing Misinformation and Disinformation: Understanding Swiss COVID-19 Narratives Through Natural Language Processing Analysis #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

1 week ago

Social Media Perspectives on a Future HIV Vaccine: Mixed Methods Analysis Background: As the prospect of an HIV vaccine nears reality, understanding public discourse around the vaccine is essential for informing communication strategies and addressing misinformation. Social media platforms are influential spaces where public narratives form, yet little research has examined discourse around an HIV vaccine, especially on TikTok. Objective: This study aims to compare and characterize public discourse about a future HIV vaccine across Twitter (subsequently rebranded X) and TikTok, identifying prevailing themes, sentiments, and rhetorical strategies to inform public health communication. Methods: From over 400,000 tweets and 65,000 TikTok comments, we analyzed the 1000 most-liked posts on each platform using natural language processing and coded the top 500 most-liked posts for rhetorical strategies, sentiment, and themes. Results: Our findings reveal expressions of hope and trust in science on both platforms, as well as concerns about institutional corruption and conspiracy theories, such as the belief that the HIV vaccine responds to harm caused by the COVID-19 vaccine. Tweets tended to be more linguistically complex and yielded richer insights, while TikTok comments were shorter and more difficult to interpret without video context. Key rhetorical strategies included conspiracy theories, post hoc reasoning, and emotional appeals. Conclusions: This study underscores the need for platform-specific communication strategies to address misinformation and build public trust. The findings offer timely insights into emerging HIV vaccine discourse and highlight actionable opportunities for public health stakeholders to build trust and combat misinformation in advance of the vaccine rollout.

JMIR Infodemiology: Social Media Perspectives on a Future HIV Vaccine: Mixed Methods Analysis #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

3 weeks ago

Audience-Specific Health Communication: Mixed Methods Evaluation of the Maria Ciência AI-Assisted Knowledge Translation Tool Background: Scientific misinformation remains a major barrier to effective health communication. Bridging the gap between academic research and public understanding requires tools that simplify scientific language and adapt content to diverse audiences. Objective: This study presents Maria Ciência (LPCT-IGM), a specialized GPT-based assistant for science communication. The tool supports researchers in translating peer-reviewed scientific findings through simple prompts into accessible, ethically appropriate materials tailored for children, the general public, health professionals, and policymakers. Methods: The tool was configured using prompt engineering techniques and guided by curated reference materials on inclusive and nonstigmatizing scientific language. Materials derived from 47 public health papers resulted in 188 outputs, which were assessed by 121 evaluators using 4 criteria: clarity, level of detail, language suitability, and content quality. In addition, outputs generated by Maria Ciência were compared with those produced by a base large language model and with human-written science communication materials. Readability and linguistic accessibility were assessed using multiple established metrics. Results: Worldwide, mean scores were high: clarity (4.90), language suitability (4.78), content quality (4.72), and level of detail (4.56), on a 5-point scale. Materials for children and the general public consistently achieved the highest ratings across all criteria. A targeted comparison with the base large language model demonstrated superior performance of Maria Ciência in contextual stability. Readability analyses indicated that Maria Ciência’s outputs were significantly more accessible than human-written texts, while maintaining high legibility classifications. Conclusions: Maria Ciência demonstrates the potential of artificial intelligence–assisted tools to enhance knowledge translation and counter scientific misinformation by producing scalable, audience-specific content that balances accessibility and informational integrity.

JMIR Infodemiology: Audience-Specific Health Communication: Mixed Methods Evaluation of the Maria Ciência AI-Assisted Knowledge Translation Tool #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

1 month ago

Nonnegotiable Symbolic Value and Sugar-Driven Food Habits in Indonesia: Mixed Methods Study Using a Digital Sociological Approach Background: The sugar market in Indonesia reflects the distinct consumer behavior shaped by economic and deeply rooted cultural factors. This study explores how symbolic values attached to sugar sustain persistent, often irrational or uncontrollable consumption, highlighting the need for a demand-side perspective in the economic sociology of sugar markets. Objective: This study analyzes the nonnegotiable symbolic value of sugar and its implication to uncontrollable consumption in Indonesia. Referring to the framework of product valuation in the social order of markets by Beckert, it offers insights into both the symbolic and material values of sugar. Methods: The applied method complements digital mixed method approaches used in prior research. Digital data from online news and YouTube were visualized through textual network analysis and social network analysis to describe the symbolic and material values of sugar. In-depth interviews with key actors and limited field observations on food and beverage labels were also conducted. Results: Findings reveal that the symbolic value of sugar increases significantly when processed into food or beverages, shaping food habits and habitus across diverse ethnic groups in Indonesia and reinforcing early dependence on sugar. Weak enforcement of labeling regulations on food and beverage packages further impedes shifts in consumer perceptions of the risks of excessive sugar consumption. Conclusions: This study contributes a demand-side perspective to the economic sociology of the sugar market, proposing strategies to address the sugar-driven food habits and habitus from the perspective of consumer behavior. Simultaneously, it assesses producer compliance with regulations on the sweetness level to reduce sugar consumption and the prevalence of noncommunicable diseases.

JMIR Infodemiology: Nonnegotiable Symbolic Value and Sugar-Driven Food Habits in Indonesia: Mixed Methods Study Using a Digital Sociological Approach #infodemic #infodemiology

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@owlyspirit.bsky.social

1 month ago

To challenge the vaccine infodemic, World Health Organization targets social media science Carlos Mendez (São Paulo, Brazil) watched his father die from COVID-19 in April 2020, gasping for breath in their cramped apartment, refusing to go to the

Misinformation kills as surely as any virus. The WHO is fighting back against the vaccine #infodemic by using social media science and AI to track rising narratives and fill "information voids" with credible facts.
https://ow.ly/PK9p50YkQBn
#ScienceCommunication #HealthTech #WHO

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JMIR Publications

@jmirpub.bsky.social

1 month ago

The Impact of Social Media Videos on Quantitative Health Outcomes: Systematic Review Background: Social media has transformed the landscape of health communication. Video content can optimally activate our cognitive systems, enhance learning, and deliver accessible information. Evidence has suggested the positive impact of videos on health knowledge and health-related behaviors, yet the impact of social media videos on quantitative health outcomes is underresearched. Evaluating such outcomes poses unique challenges in measuring exposure and outcomes within internet-based populations. Objective: We aimed to evaluate the impact of social media videos on quantitative health outcomes, examine methodologies used to measure these effects, and describe the characteristics of video interventions and their delivery. Methods: In accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, MEDLINE, Embase, Web of Science, CINAHL, and Google Scholar were searched. Studies were eligible if they were original research evaluating long-form social media video interventions addressing any health-related condition, delivered via social media platforms, and reported quantitative health outcomes. The primary outcome was the effect of social media videos on quantitative health outcomes. Additional outcomes included participant characteristics, video features, delivery methods, and the use of theoretical frameworks. A narrative synthesis was conducted. A subgroup meta-analysis was performed to synthesize health outcomes mentioned in 2 or more studies with sufficient homogeneity. Risk of bias assessment was conducted using Cochrane Risk of Bias 2, ROBINS-I, or National Institutes of Health Quality Assessment Tool, depending on the study design. One reviewer screened titles and abstracts. Two reviewers independently conducted full-text screening, data extraction, and risk of bias assessment. Results: A systematic search was conducted on October 25, 2023, and was updated on June 12, 2025, yielding a total of 41,172 records after duplicate removal. Sixteen studies were included, involving 4158 participants. Mental health–related conditions were the most studied (10 studies). Most video interventions were delivered via YouTube (12 studies). Studies have reported that video interventions were associated with significant improvements in peri-procedural anxiety, mood, and physical activity levels, although most findings were limited to individual studies with variable methodological quality. Three studies that developed videos with user input and theoretical frameworks significantly impacted study-specific primary outcomes. A subgroup meta-analysis demonstrated a significant moderate impact of online video interventions in improving peri-procedural anxiety (standard mean difference=0.57, 95% CI 0.09‐1.05). All but one study showed some concern or high risk of bias. Conclusions: We demonstrated a potential positive impact of social media videos on quantitative health outcomes, notably in improving peri-procedural anxiety. Videos developed with user input and theoretical frameworks significantly impacted study-specific primary outcomes. Nevertheless, there is the need to shift focus toward measuring physical health–related outcomes and to develop better designed, innovative methodologies to measure the impact that can better simulate the social media environment. Trial Registration: PROSPERO CRD42023474648; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023474648

JMIR Infodemiology: The Impact of Social Media Videos on Quantitative Health Outcomes: Systematic Review #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

1 month ago

Automated Risk Assessment of Opioid Use: Analysis Using Pre-Trained Transformers on Social Media Data Background: The illegal use of opioids has emerged as a major global public health concern, contributing to widespread addiction and a growing number of overdose-related deaths. In response, the US federal government has invested billions of dollars in combating the opioid epidemic through treatment, prevention, and law enforcement initiatives. Despite these efforts, there remains an urgent need for automated tools capable of detecting overdose cases and assessing the risk levels of substances—tools that can enable faster, more effective responses with less reliance on human intervention. Social media, particularly Reddit, has become a valuable source of self-reported data on opioid misuse, offering rich insights into user experiences and symptoms. Objective: This research aimed to develop an advanced automated tool for detecting opioid overdose risks and classifying substances into high-risk and low-risk categories by analyzing social media posts. Methods: A multistage methodology was used to achieve the objectives of this work. First, a new dataset was constructed from Reddit posts and manually annotated. Each post was labeled according to the risk level of the mentioned substance, using contextual indicators and user-reported experiences as the basis for classification. To ensure reliability and annotator consistency, detailed annotation guidelines were developed and applied throughout the labeling process. Second, a bidirectional encoder representation from transformers for biomedical text mining (BioBERT)–based classification framework was implemented and enhanced with a custom attention mechanism to capture relevant semantic information for more accurate predictions. Third, the model’s performance was evaluated using 5-fold cross-validation and compared against several baseline approaches, including traditional supervised learning, deep learning, and transfer learning methods. In total, 14 experiments were conducted to evaluate comparative effectiveness. To further assess the contribution of the attention layer, the best-performing model was also evaluated against a version incorporating the standard self-attention mechanism, using a train-test split. Finally, a paired t test was conducted to statistically assess the performance difference between the BioBERT-based model and the strongest baseline, extreme gradient boosting (XGBoost), providing validation of the observed improvements. Results: The proposed BioBERT model with custom attention achieved an F1-score of 0.99 in cross-validation, outperforming the best baseline, XGBoost (F1-score=0.97), with a relative improvement of 2.06%. A paired t test conducted across the 5 folds (n=5) confirmed that the performance gain was statistically significant (P=.003), providing strong evidence that the improvement reflects genuine advances in overdose risk detection. Conclusions: This paper demonstrates the potential of leveraging social media data and advanced natural language processing models to build reliable systems for opioid overdose risk detection. The BioBERT model with custom attention shows state-of-the-art performance and robustness, offering a powerful tool to support timely intervention and harm reduction strategies in the ongoing opioid crisis. Trial Registration:

JMIR Infodemiology: Automated Risk Assessment of Opioid Use: Analysis Using Pre-Trained Transformers on Social Media Data #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

1 month ago

Leveraging AI for Analysis of Digital Health Information on Cancer Prevention Among Arab Youth and Adults: Content Analysis Background: As TikTok (ByteDance) grows as a major platform for health information, the quality and accuracy of Arabic-language cancer prevention content remain unknown. Limited access to culturally relevant and evidence-based information may exacerbate disparities in cancer knowledge and prevention behaviors. Although large language models offer scalable approaches for analyzing online health content, their utility for short-form video data, especially in underrepresented languages, has not been well established. Objective: We aimed to characterize and evaluate the quality of Arabic-language TikTok videos on cancer prevention and explore the use of large language models for scalable content analysis. Methods: We used the TikTok research application programming interface and a GPT-assisted keyword strategy to collect Arabic-language TikTok videos (2021-2024). From an initial collection of 1800 TikTok videos, 320 were eligible after preprocessing. Of these, the top 25% (N=30) most-viewed were analyzed and manually coded for content type, cancer type, uploader identity, tone and register, scientific citation, and disclaimers. Video quality was assessed using the Patient Education Materials Assessment Tool for Audiovisual Materials for understandability and actionability, and the Global Quality Scale (GQS). GPT-4 was used to generate artificial intelligence annotations, which were compared to human coding for select variables. Results: The top 25% (N=30) most-viewed videos amassed a total of 21.6 million views. Diet and alternative therapies were most common (15/30, 50%), which included recommendations to reduce hydrogenated oils, increase fruit and vegetable intake, and the use of traditional remedies such as garlic and black seed. Only 6.6% (2/30) of videos cited scientific literature. General cancer (15/30, 53%), breast (5/30, 17%), and cervical (4/30, 13%) cancers were most frequently mentioned. Doctors led 30% (9/30) of videos and were more likely to produce higher quality content, including significantly higher global quality scores (GQS=4, median 4, IQR 4-4 vs 3, median 3, IQR 2-3, P=.06). Over half of the videos had low understandability (16/30, 53%) and actionability (18/30, 60%). Emotionally framed content had the highest engagement across likes and shares, although this did not reach statistical significance (P=.08 and P=.05, respectively). However, emotional tone was significantly associated with lower GQS scores (P=.01). GPT-4 showed high agreement with human coders for cancer type (Cohen κ=1.0), strong agreement for GQS (κ=0.94), but low agreement for tone classification (κ=0.15), due to misclassification of emotional delivery from text-only input. Conclusions: Arabic-language TikTok cancer prevention content is highly engaging but variable in quality, with emotionally framed videos attracting substantial attention despite lower informational value. Artificial intelligence-assisted tools show strong potential for scalable, multilingual health content analysis, but multimodal approaches are needed to accurately interpret tonal and audiovisual features.

JMIR Infodemiology: Leveraging AI for Analysis of Digital Health Information on Cancer Prevention Among Arab Youth and Adults: Content Analysis #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

1 month ago

Health Data for Linguistic Minority Group Research in Canada: Proof-of-Concept Centralized Health Care Metadata Repository Development and Usability Study Background: Language barriers between Canadian patients and health care providers are associated with poorer health outcomes, including decreased patient safety and quality of care, misdiagnosis and longer treatment initiation times, and increased mortality. However, research exploring language as a social determinant of health is limited, as Canadian health data are scattered across many jurisdictions, each with its own policies and procedures. This fragmentation makes it difficult for researchers to identify, locate, and use existing data. This paper presents the results of a pilot study that attempts to address this gap by creating a metadata repository (MDR) to act as a central source of information about what data are available at which data holdings across Canada. Objective: This project aimed to (1) create a proof-of-concept MDR for Canadian health data at the variable level; (2) identify and label language-related variables existing within the MDR data; and (3) develop an interactive, public-facing web application to let users browse and search the MDR. Methods: Metadata were collected from 5 Canadian health data sources, including 4 provincial data holdings and 1 national survey, and pooled to create a data repository. Then, we performed bottom-up labeling of language-related variables within the pooled metadata by first using a search string algorithm across all variable labels, names, and definitions and then consensus screening these variables using a derived, standardized definition of language or linguistic variables. Using the Shiny web framework in R, we then developed an openly accessible web application to allow users to search the proof-of-concept MDR. Results: A total of 850,343 variables were collected and included in the repository, with most coming from Ontario (n=712,037, 83.7%) and Manitoba (n=97,051, 11.4%) provincial data holdings. Among all variables in the repository, 213,696 (25.1%) were confirmed to be language related. Conclusions: Developing a national MDR would be a transformative opportunity for Canadian researchers to leverage the full scope of Canadian health administrative data. Although a top-down approach with consistent engagement of and collaboration between provincial data holdings and federal data agencies is ideal to develop a national MDR, this study demonstrates the feasibility of a bottom-up approach in contributing to this overarching goal.

JMIR Infodemiology: Health Data for Linguistic Minority Group Research in Canada: Proof-of-Concept Centralized Health Care Metadata Repository Development and Usability Study #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

2 months ago

Quality, Reliability, and Dissemination of In Vitro Fertilization–Related Videos on Chinese Social Media: Cross-Sectional Analysis of 300 Short Videos Background: In recent years, short-video platforms like Douyin, Bilibili, and Xiaohongshu have become dominant channels for health information dissemination in China. While they offer opportunities for medical education, the content quality is highly variable and lacks rigorous oversight. For patients seeking In Vitro Fertilization (IVF), who are navigating a complex and emotionally challenging journey, access to accurate and reliable information is critical. However, the factors driving the dissemination of IVF-related content on these platforms—whether it is the quality of the information or the influence of the creator—remain under-studied, representing a significant gap in understanding the modern health information ecosystem. Objective: To assess the quality, reliability, and key drivers of dissemination for In Vitro Fertilization (IVF)-related short videos on major Chinese social media platforms. Methods: This cross-sectional study analyzed the top 300 popular IVF-related videos from Douyin, Bilibili, and Xiaohongshu, retrieved between January 10 and January 15, 2025. Video quality and reliability were assessed using the Global Quality Score (GQS) and a modified DISCERN (mDISCERN) instrument. An XGBoost machine learning model was used to identify predictors of video dissemination, with "likes" as the primary outcome measure. Results: Content from medical professionals was of significantly higher quality (P < 0.001). However, the XGBoost analysis revealed that video dissemination was primarily driven by creator influence, not content quality. The uploader's follower count was identified as the most powerful predictor of video "likes," while GQS and mDISCERN scores had a negligible impact on engagement. Conclusions: In the current Chinese social media landscape, the reach of IVF-related videos is determined by creator influence rather than scientific merit. This "engagement-over-quality" dynamic poses a potential risk of misinformation for patients seeking reliable health information. Clinical Trial: Not applicable.

JMIR Infodemiology: Quality, Reliability, and Dissemination of In Vitro Fertilization–Related Videos on Chinese Social Media: Cross-Sectional Analysis of 300 Short Videos #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

2 months ago

Using Artificial Intelligence Methods to Evaluate the Effect of the National Cytomegalovirus Awareness Month on the Content and Sentiment of Social Media Posts: Infodemiology Study Background: The month of June has been recognized as the National Cytomegalovirus (CMV) Awareness Month since 2011 in the United States. Established by government resolution, the goal is to increase awareness and reduce the incidence of congenital CMV infection, a leading cause of preventable birth defects and developmental disabilities. Social media is a powerful tool to support public health by making health information easily accessible. With an estimated 246 million users in the United States and more than half of adults seeking health information through such platforms, social media offers an unparalleled opportunity to promote CMV awareness and prevention. Objective: This study aimed to evaluate social media messaging before, during, and after the National CMV Awareness Month to assess how the campaign influenced messaging patterns and sentiment related to specific CMV health topics. Methods: Publicly available posts on Twitter/X from May to August 2023 that contained at least one of the five most used CMV-related hashtags were collected using a media monitoring platform. The dataset was preprocessed using a customized Bidirectional Encoder Representations from Transformers tokenizer and a language detection package to remove irrelevant and non-English posts. Validated and artificial intelligence (AI) methods (Cohen κ=0.69) were used to determine the thematic content of posts (N=14,900), such as awareness and prevention messaging, and to characterize the sentiment. Changes in post characteristics were measured in relation to the National CMV Awareness Month. Results: CMV-relevant post volume increased by 55% during the campaign month and returned to precampaign levels in July. Overall, academic/university researchers were the most frequent authors, pediatrics was the most frequent population discussed, and vaccines were the most frequently mentioned prevention. Significant associations were observed between the month of post publication and the target audience (χ22=144.3, P

JMIR Infodemiology: Using Artificial Intelligence Methods to Evaluate the Effect of the National Cytomegalovirus Awareness Month on the Content and Sentiment of Social Media Posts: Infodemiology Study #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

2 months ago

Review of the Quality and Reliability of Online Arabic Content on Diabetic Retinopathy: Infodemiological Study Background: Diabetic retinopathy (DR) is a leading cause of vision loss, particularly in the Middle East. With the rise of online health information, many patients turn to the internet for knowledge about health conditions. However, the accuracy and quality of this information can be questionable, particularly in languages other than English. Objective: We sought to evaluate the quality and reliability of Arabic websites on DR to address this knowledge gap and improve patient care. Methods: The first 100 Arabic search results for DR were examined on Google.com, focusing on patient education websites in Arabic. Content was assessed using a 20-question model, quality was evaluated with the Discern instrument, and reliability was measured by the JAMA benchmark. Two independent raters conducted evaluations, and data were analyzed with SPSS. Descriptive statistics were used for website characteristics, and the first ten Google webpages were compared to others using bivariate analysis with a significance level of p

JMIR Infodemiology: Review of the Quality and Reliability of Online Arabic Content on Diabetic Retinopathy: Infodemiological Study #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

2 months ago

Marketing Strategies and Factors Influencing the Popularity of Alcohol Videos from Official Brand Accounts on Douyin: Content Analysis Study Background: Alcohol consumption in China poses significant public health challenges. Alcohol marketing has been shown to increase public alcohol consumption, with social media platforms like Douyin (Tiktok in Mainland China) becoming one of the main channels for alcohol marketing. Objective: This study aimed to analyze the thematic content of alcohol advertising on Douyin platform and to explore the factors influencing the popularity of these alcohol-related videos. Methods: Using data from JINGDONG platform and alcohol industry reports, we identified 40 popular alcohol brands. For each brand, we located their official Douyin accounts and selected the top 20 most-liked videos posted between November 1, 2020, and December 1, 2021. In total, 659 videos from 37 brands were collected for analysis. Two trained researchers independently coded each video using a predefined codebook, which consisted of 7 sections and 20 items. Results: Of the 659 videos analyzed, 320 (48.6%) garnered over 1,000 likes. A significant portion of videos were direct advertisements (42.6%) and short skits (38.7%), with 56.0% featuring characters engaging in drinking-related behaviors or directly consuming alcohol. Additionally, many videos highlighted brand elements (77.4%) and extended features (24.4%). Cultural themes were also common, with 23.2% of videos promoting the enjoyment of life and 6.8% emphasizing balance in life. However, age restrictions were missing in 26.9%of videos, and only 1.2% included health warnings stating “Drinking is harmful to health.” Certain marketing strategies were significantly associated with higher video popularity, including the use of short skits (OR = 2.77, 95% CI: 1.42–5.41), highlighting brand elements (OR = 2.96, 95% CI: 1.59–5.51), and emphasizing life balance (OR = 3.44, 95% CI: 1.11–10.66). In contrast, the presence of age restrictions (OR = 0.32, 95% CI: 0.15–0.67) and explicit health warnings (OR = 0.06, 95% CI: 0.01–0.84) were associated with lower popularity. Conclusions: Alcohol marketing strategies on Douyin leverage experiential, brand-driven, collaborative, and cultural marketing techniques to enhance the video attractiveness and create alcogenic environments. Meanwhile, the effective age restriction and health warning are largely absent. It’s essential to legislate and enforce stricter alcohol marketing regulations to reduce the health risks associated with alcohol marketing.

JMIR Infodemiology: Marketing Strategies and Factors Influencing the Popularity of Alcohol Videos from Official Brand Accounts on Douyin: Content Analysis Study #infodemic #infodemiology

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TracingVRL by A.J. Fish

@tracingvrl.bsky.social

2 months ago

The Year 2+2=5, ... rather, the Year 2025 is in the Rearview Mirror! Looking back shows A.I. industrialists took up residence in the Health and Human Services department.

TracingVRL is my software-journalism project tracing the vaccine infodemic going ViRaL. My latest installment is the Year 2025 in reverse chronology, as well as some media reform history: tracingvrl.substack.com/p/the-year-2... #vaccines #publichealth #healthcare #infodemic

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JMIR Publications

@jmirpub.bsky.social

3 months ago

Impacts of Sexual and Reproductive Health and Rights Misinformation in Digital Spaces on Human Rights Protection and Promotion: Scoping Review Background: Sexual and reproductive health and rights (SRHR) are foundational to both individual autonomy and global well-being. Misinformation in this domain poses serious risks by undermining evidence-based decision-making, weakening systems of accountability, and perpetuating social injustices. Objective: This scoping review aimed to map and synthesize evidence on the forms, spread, and impacts of misinformation related to SRHR in digital spaces, with a particular focus on implications for the protection and promotion of human rights. Methods: We conducted a scoping review of scientific papers and gray literature. It was guided by the JBI (Joanna Briggs Institute) population, exposure, and outcomes framework. The extracted information was documented following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Thematic analysis was carried out and mapped against human rights standards: (1) equality and nondiscrimination; (2) Availability, Accessibility, Acceptability, and Quality; (3) informed decision-making; (4) privacy and confidentiality; (5) participation and inclusion; and (6) accountability. Results: Of the 254 eligible studies and documents, 133 focused on the information ecosystem, 37 on the individual, 32 on service delivery and health system, 31 on law and policy, and 21 on community levels. SRHR misinformation impacts individuals’ informed SRHR decisions by shaping their beliefs, attitudes, and health-seeking behaviors. It reinforces harmful and discriminatory social norms at community levels and the exclusion of marginalized voices. SRHR misinformation impacts health systems by shaping provider knowledge and practice, disrupting service delivery, and creating barriers to equitable care. It may function as a legal and policy tool to erode SRHR protections. The design of online platforms, digital marketing strategies, and content moderation policies enables misinformation to spread widely while restricting credible SRHR content. Conclusions: SRHR misinformation in digital spaces is a systemic issue that undermines human rights across multiple levels, highlighting the urgent need for integrated, rights-based approaches to research, policy, and intervention. Trial Registration:

JMIR Infodemiology: Impacts of Sexual and Reproductive Health and Rights Misinformation in Digital Spaces on Human Rights Protection and Promotion: Scoping Review #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

3 months ago

Internet Memes as Drivers of Health Narratives and Infodemics: Integrative Review Background: Digital media memes have emerged as influential tools in health communication, particularly during the COVID-19 pandemic. While they offer opportunities for emotional engagement and community resilience, they also act as vectors for health misinformation, contributing to the global infodemic. Despite growing interest in their communicative power, the role of memes in shaping public perception and misinformation diffusion remains underexplored in infodemiology. Objective: This integrative review aims to analyze how memes influence emotional, behavioral, and ideological responses to health crises, and to examine their dual role as both contributors to and potential mitigators of infodemics. The paper also explores strategies for integrating memes into public health campaigns and infodemic management. Methods: A comprehensive literature search was conducted across three major databases (MedLine, Scopus, and Web of Science), identifying a total of 386 records. Following duplicate removal and eligibility screening, 14 peer-reviewed studies published between 2020 and 2025 were included. An integrative narrative approach was used to synthesize evidence on social media behavior, misinformation dynamics, and digital health campaigns. The analysis was grounded in infodemiological and infoveillance frameworks as established by Eysenbach, incorporating insights from psychology, media studies, and public health. Results: Memes function as emotionally salient and visually potent carriers of health-related narratives. While they can simplify complex messages and foster adaptive humor during crises, they are also susceptible to distortion, particularly in echo chambers and conspiracy communities. Findings reveal that misinformation-laden memes often leverage humor and disgust to bypass critical thinking, and their viral potential is linked to emotional intensity. However, memes have also been successfully integrated into prebunking strategies, increasing engagement and reducing susceptibility to false claims when culturally tailored. The review identifies key mechanisms that enhance or hinder the infodemiological value of memes, including political orientation, digital literacy, and narrative framing. Conclusions: Memes are a double-edged sword in the context of infodemics. Their integration into infodemic surveillance and digital health campaigns requires a nuanced understanding of their emotional, cultural, and epistemic effects. Public health institutions should incorporate meme analysis into real-time infoveillance systems, apply evidence-based meme formats in prebunking efforts, and foster digital literacy that enables critical meme consumption. Future infodemiology research should further explore the long-term behavioral impacts of memetic misinformation and the scalability of meme-based interventions.

JMIR Infodemiology: Internet Memes as Drivers of Health Narratives and Infodemics: Integrative Review #infodemic #infodemiology

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André Picard @picardonhealth

@picardonhealth.bsky.social

3 months ago

Opinion | Anti-Vaccine Influencers Are Only Getting Stronger

Anti-Vaccine Influencers Are Only Getting Stronger, And That's Depressing, by Jessica Grose www.nytimes.com/2025/12/17/o... via @nytopinion.nytimes.com #infodemic

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JMIR Publications

@jmirpub.bsky.social

3 months ago

Mapping the Quality of German-Language Health Information on the Treatment of Knee Osteoarthritis: Cross-Sectional Analysis Background: Patients with knee osteoarthritis have a considerable need for information about their condition, its progression, and available treatments. Decision-making is often complex and requires evidence-based health information material (HIM). When medical consultations do not sufficiently address patients’ needs, many seek additional information independently. Objective: This study aimed to examine the quality of German-language HIM on knee osteoarthritis treatment and its suitability for supporting informed choice. In particular, the study analyzed the content of the HIM and assessed the balance in the presentation of treatment options. Methods: A descriptive cross-sectional study was conducted. HIM was identified through a combination of search strategies, including a systematic internet search using commonly used German terms related to the treatment of knee osteoarthritis. Identified HIMs were independently assessed by 2 raters using the validated Mapping the Quality of Health Information (MAPPinfo) checklist, which operationalizes the criteria of the Guideline Evidence-Based Health Information. Information quality was calculated on a scale from 0% to 100%, representing compliance with the quality standard. A descriptive content analysis was also carried out to examine the range and balance of treatment options presented, as well as the reporting of benefits and complications associated with total knee arthroplasty (TKA). The presence of certification was recorded. Results: A total of 94 HIMs were included. On average, the material met 14.6% (SD 9.4%) of the quality criteria. HIM from public and nonprofit providers performed better (mean 40.1%, SD 3.6% and mean 37.2%, SD 23.1%, respectively) than those from other providers. Overall, 14 HIMs presented treatment options in a balanced manner. Among the 78 HIMs that covered TKA, 38.5% (n=30) did not report any benefits, and 35.9% (n=28) omitted potential complications. Certified HIMs showed only moderately higher information quality than uncertified material (mean 26.8%, SD 16% vs mean 12.7%, SD 5.9%). Conclusions: Our results highlight the urgent need to improve the quality of German-language HIM on knee osteoarthritis. The deficits identified are fundamental and affect all dimensions of information quality. Although HIM from public or nonprofit organizations has better information quality, this does not facilitate informed choice. The frequent omission of complications and benefits of TKA and the unbalanced presentation of treatment options can influence decisions. Until structural improvements are made, patients seeking quality information should favor material from public or nonprofit providers. Additionally, the MAPPinfo checklist could form the basis of a differentiated certification system to make information quality more transparent for patients.

JMIR Infodemiology: Mapping the Quality of German-Language Health Information on the Treatment of Knee Osteoarthritis: Cross-Sectional Analysis #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

4 months ago

Public Interest in Dry Eye Disease and Its Association With Environmental Parameters in Taiwan: Google Trends Infodemiology Study Background: In Taiwan, high prevalence of dry eye disease (DED) intensifies public health concerns. With the growing reliance on online resources for health information, platforms like Google Trends (GT) offer a valuable method to capture public interest. This approach also allows for the exploration of potential associations between DED public interest and environmental parameters, which may further illuminate underlying factors contributing to the disease's rising prevalence. Objective: To analyze public interest in DED in Taiwan using GT data, investigate correlations between search interest and environmental parameters, and identify shifts in the focus of search over time. Methods: We analyzed GT data from December 2018 to July 2024, focusing on relative search volume (RSV) for dry eye syndrome across Taiwan and its six special municipalities. Temporal trends in RSV were assessed using spline regression models, and monthly variations were assessed using the Kruskal-Wallis test. Spearman’s correlation was utilized to evaluate the relationship between RSV and environmental parameters, while dynamic time warping (DTW) analysis clarified the temporal alignment of RSV with these parameters. Rising related search queries were analyzed to identify shifts in public interest over time. Furthermore, top Google search results for DED-related keywords were assessed for topic coverage, quality, and readability. Results: A significant increasing trend in RSV for dry eye syndrome was observed over the study period in Taiwan (mean instantaneous derivative = 0.445; p < 0.001) and across all six special municipalities. Environmental parameters such as methane (CH₄), total hydrocarbons (THC), and non-methane hydrocarbons (NMHC) were identified as novel pollutants strongly correlated with RSV (p < 0.001), along with known pollutants such as nitric oxide (NO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), nitrogen oxides (NOx), and carbon monoxide (CO). DTW analysis revealed the strongest temporal alignment between RSV and hydrocarbons including CH4 and THC, further emphasizing their potential role in influencing public interest. Assessment of online DED information of 80 websites revealed generally low quality (mean DISCERN score: 2.14 ± 0.40), and the mean readability corresponded to a college reading level (mean grade: 21.1 ± 4.5). Rising search queries shifted from diagnostic and treatment methods before COVID-19 to natural remedies during COVID-19, and self-diagnosis and treatment options after COVID-19; while gaps were identified between public interest and the availability of online information. Conclusions: Public interest in DED has increased significantly in Taiwan from 2018 to 2024, with hydrocarbons identified as strongly associated environmental parameters. The shifts in related queries reflect changing public interest, accentuating the need for healthcare information that aligns with public interests and addresses gaps in available resources.

JMIR Infodemiology: Public Interest in Dry Eye Disease and Its Association With Environmental Parameters in Taiwan: Google Trends Infodemiology Study #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

4 months ago

Quality and Perception of Attention-Deficit/Hyperactivity Disorder Content on TikTok: Cross-Sectional Study Background: Social media platforms are increasingly used for both sharing and seeking of health-related information online. Especially TikTok has become one of the most widely used social networking platforms. One health-related topic trending on TikTok recently is Attention Deficit / Hyperactivity Disorder (ADHD). However, the accuracy of health-related information on TikTok remains a significant concern. Misleading information on ADHD on TikTok can increase stigmatization and lead to false “self-diagnosis”, pathologizing of normal behavior and overuse of care. Objective: This study aims at investigating the occurrence of misleading information in TikTok videos about ADHD and exploring potential self-diagnosis among viewers based on an in-depth analysis of the video comments. Methods: We scraped data from the 125 most liked ADHD-related TikTok videos uploaded between July 2021 and November 2023 using a commercial scraping software. We categorized videos based on the usefulness of their content as "misleading", "personal experience", or "useful" and used the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V) to evaluate the video quality regarding understandability and actionability. By purposive sampling we selected six videos and analyzed the content of 100 randomly selected user comments per video to understand the extent of self-identification with ADHD-behavior among the viewers. All qualitative analyses were carried out independently by at least two authors; disagreement was resolved by discussion. Using SPSS 27, we calculated the interrater reliability between the raters and the descriptive statistics for video and creator characteristics. We used one-way ANOVA to compare the usefulness of the videos. Results: We assessed 50.4% of the videos as misleading, 30.4% as personal experience, and 19.2% as useful. The PEMAT-A/V scores for all videos for understandability and actionability are 79.5% and 5.1%, respectively. With a score of 92.3%, useful videos scored significantly higher for understandability than misleading and personal experience videos (P

JMIR Infodemiology: Quality and Perception of Attention-Deficit/Hyperactivity Disorder Content on TikTok: Cross-Sectional Study #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

4 months ago

JMIR Infodemiology: Current Approaches To and Implementation of Information Environment Assessments in the Context of Public Health: Rapid Review #infodemic #infodemiology

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JMIR Publications

@jmirpub.bsky.social

4 months ago

Monitoring Opioid-Related Social Media Chatter Using Natural Language Processing and Large Language Models: Temporal Analysis Background: Opioid overdose has become a global public health emergency, with the United States experiencing particularly high rates of morbidity and mortality due to both prescription and illicit opioid use. Traditional public health monitoring systems often fail to provide real-time insights, limiting their capacity for early detection and intervention. Social media platforms, especially Reddit, offer a promising alternative for timely toxicovigilance due to the abundance of user-generated, real-time content. Objective: This study aims to explore the use of Reddit as a real-time, high-volume source for toxicovigilance and to develop an automated system that can classify and analyze opioid-related social media posts to detect behavioral patterns and monitor the evolution of public discourse on opioid use. Methods: To investigate the evolving social media chatter discourse around opioid use, we collected a large-scale dataset from Reddit spanning six years, from January 1, 2018, to December 30, 2023. Using a comprehensive opioid lexicon—including formal drug names, street slang, common misspellings, and abbreviations—we filtered relevant chatters post for further analysis. A subset of this data was manually annotated according to well-defined annotation guidelines into four distinct categories: Self-abuse (chatter describing his/her own experience with opioid use or overdose), External-abuse (use by someone close, such as a friend or family member), Information (general or factual knowledge about opioids), and Unrelated (content not contextually relevant to opioid use). The distribution across categories was as follows: 37.21% Self-abuse, 27.25% External-abuse, 27.57% Information, and 7.97% Unrelated. To automate the classification of opioid-related chatter, we developed a robust NLP pipeline leveraging classical machine learning algorithms, deep learning models, transformer-based architectures, and fine-tuned a state-of-the-art large language model (OpenAI GPT-3.5 Turbo). In the final stage, the trained LLM was deployed on an unlabeled dataset comprising 74,975 additional Reddit chatter entries. This enabled a detailed temporal analysis of opioid-related discussions over the six-year period, uncovering trends and shifts in public perception, self-reported use, external reported use, and information sharing around opioid drugs. This methodology demonstrates the power of combining manual annotation with cutting-edge language models for real-time toxicovigilance and public health monitoring. Results: The fine-tuned GPT-3.5 Turbo model achieved the highest classification accuracy of 0.86, outperforming the mBERT model (0.81) by representing a performance improvement of 6.17% over the Transformer model. The temporal analysis of the unlabeled data revealed evolving trends in opioid-related discussions, indicating shifts in user behavior and overdose-related chatter over time. Conclusions: This study demonstrates the potential of integrating advanced NLP techniques and LLMs with social media data to support real-time public health surveillance. Reddit provides a valuable platform for identifying emerging trends in opioid use and overdose risk. The proposed system offers a proactive tool for researchers, clinicians, and policymakers to better understand and respond to the opioid crisis.

JMIR Infodemiology: Monitoring Opioid-Related Social Media Chatter Using Natural Language Processing and Large Language Models: Temporal Analysis #infodemic #infodemiology

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@byteandpieces.bsky.social

4 months ago

The PERFECT Storm: Why This Winter Could TRIGGER the Next PANDEMIC You remember the empty shelves, the lockdowns, the constant uncertainty. What if I told you that wasn't a once-in-a-century event? The warning signs for the next global pandemic are here, and "Day One" could be this winter. This isn't fear-mongering; this is what the experts are screaming. This podcast unpacks the terrifying reality of why we are dangerously unprepared for what's coming. We'll show you how increased human-animal interaction and deforestation are creating a perfect breeding ground for new zoonotic diseases to leap from animals to humans. We'll break down the science of why winter creates a "perfect storm" for respiratory viruses to explode, and why our own weakened immunity might be the trigger. Then, we expose the critical failures that could make the next pandemic even worse: chronic underfunding of public health, terrifyingly fragile supply chains, and the rampant "infodemic" of misinformation that will cripple our response before it even begins. This is the ultimate briefing on the threat of Disease X. We'll tell you exactly what health authorities are looking for to declare "Day One" and start the clock on a new global crisis. This isn't about fear; it's about awareness. To protect yourself and your family, you need to understand the threat. Subscribe now and share this crucial information. Being prepared starts with being informed.

📣 New Podcast! "The PERFECT Storm: Why This Winter Could TRIGGER the Next PANDEMIC" on @Spreaker #beprepared #dayone #diseasex #globalhealth #globalpandemic #health #healthtok #healthwarning #infectiousdisease #infodemic #misinformation #nextpandemic #pandemicpreparedness #publichealth #science

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JMIR Publications

@jmirpub.bsky.social

5 months ago

Quality Assessment of Health Information on Social Media During a Public Health Crisis: Infodemiology Study Background: The quality of health information on social media is a major concern, especially during the early stages of public health crises. While the quality of the results of the popular search engines related to particular diseases have been analyzed in the literature, the quality of health-related information on social media such as X/Twitter during the early stages of a public health crisis has not been addressed. Objective: This study aims to evaluate the quality of health-related information on social media during the early stages of a public health crisis. Methods: A cross-sectional analysis was conducted on tweets related to health in the early stages of the most recent public health crisis (COVID-19 pandemic). The study analyzed the top 100 websites that were most frequently retweeted in the early stages of the crisis, categorizing them by content type, website affiliation, and exclusivity. Quality and reliability were assessed using the DISCERN and JAMA benchmarks. Results: Our analyses showed that 95% of the websites met only 2 of the 4 JAMA quality criteria. DISCERN scores revealed that 81% of the websites were evaluated as low scores, and only 11% of the websites were evaluated as high scores. The analysis revealed significant disparities in the quality and reliability of health information across different website affiliations, content types, and exclusivity. Conclusions: This study highlights a significant issue with the quality, reliability, and transparency of online health-related information during a public health challenge. The extensive shortcomings observed across frequently shared websites on Twitter highlight the critical need for continuous evaluation and improvement of online health content during the early stages of future health crises. Without consistent oversight and improvement, we risk repeating the same shortcomings in future, potentially more challenging situations.

JMIR Infodemiology: Quality Assessment of Health Information on Social Media During a Public Health Crisis: Infodemiology Study #infodemic #infodemiology

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