JMIR infodemiology最新文献

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 3 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.

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.

Background: Opioid overdose is a global public health emergency, with the United States experiencing high rates of morbidity and mortality due to 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 aimed to explore the use of Reddit as a real-time, high-volume source for toxicovigilance and 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 evolving social media discourse around opioid use, we collected a large-scale dataset from Reddit spanning 6 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 posts for further analysis. A subset of these data was manually annotated according to well-defined annotation guidelines into 4 categories: self-misuse, external misuse, information, and unrelated, with distributions of 37.21%, 27.25%, 27.57%, and 7.97%, respectively. To automate the classification of opioid-related chatter, we developed a robust natural language processing pipeline leveraging classical machine learning algorithms, deep learning models, and transformer-based architecture, and fine-tuned a state-of-the-art large language model (LLM; OpenAI GPT-3.5 Turbo). In the final stage, the trained LLM was deployed on an unlabeled dataset comprising 74,975 additional Reddit chatter posts. This enabled a detailed temporal analysis of opioid-related discussions, aligned with 6 years of opioid-related death records from the Centers for Disease Control and Prevention (CDC). For this study, self-misuse and external misuse were merged into a misuse category for direct comparison with the CDC's mortality data, examining whether trends in social media discourse on opioid misuse reflect patterns in real-world mortality statistics.

Results: The fine-tuned GPT-3.5 Turbo model achieved the highest classification accuracy of 0.93, outperforming the baseline (random forest 0.85) by representing a performance improvement of 9.14% over the machine learning 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. To quantify this relationship, we calculated the Pearson correlation coefficient between misuse-related posts and CDC death records (r=0.854). This correlation was statistically significant (P<.001), indicating a strong positive relationship betwe

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 has been analyzed in the literature, the quality of health-related information on social media, such as X (formerly 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 health-related tweets in the early stages of the most recent public health crisis (the 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 (Journal of the American Medical Association) benchmarks.

Results: Our analyses showed that 95% (95/100) of the websites met only 2 of the 4 JAMA quality criteria. DISCERN scores revealed that 81% (81/100) of the websites were evaluated as low scores, and only 11% (11/100) 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.

Background: The analysis of social networks should be considered by institutions and governments alongside surveys and other conventional methods for assessing public attitudes toward vaccines. X (formerly known as Twitter) has emerged as a significant source for studying vaccine hesitancy.

Objective: The aim of the study is to examine the main arguments and narratives in favor and against vaccination expressed in Spanish- and Catalan-language posts, comments, and opinions on the social media platform X.

Methods: Spanish and Catalan posts were collected from X using NodeXL Pro between March and December 2021, resulting in 479,734 posts. For qualitative analysis, a random subsample of 384 tweets was selected using Cochran's formula (95% confidence and ±5% margin of error). A bespoke code frame was developed in collaboration with medical and social media experts, and posts were translated into English. Intercoder reliability, assessed on 20% of the sample, yielded 93.4% agreement and a Cohen κ of 0.92.

Results: A total of 479,734 posts were retrieved from 29,706 users. After an inductive review of the data, six themes were identified, which formed the basis of our code frame: (theme 1) vaccine acquisition and distribution, (theme 2) vaccine skepticism and criticism, (theme 3) provaccination stance, (theme 4) global COVID-19 situation, (theme 5) vaccine politics and international relations, and (theme 6) miscellaneous news and posts. Vaccine skepticism and criticism was the most frequent theme (93/384, 24.2%), whereas vaccine politics and international relations was the least (25/384, 6.5%). We observed that while some posts supported vaccination, others expressed concerns about vaccine safety and efficacy, promoted conspiracy theories, disseminated misinformation, or opposed scientific consensus. Challenges related to vaccine acquisition and distribution within specific countries were also identified, along with political and economic factors, such as the politicization of vaccines, which hindered equitable distribution between vaccine-producing and vaccine-needing countries. Additionally, the pandemic's social impact fostered community support initiatives and solidarity.

Conclusions: Our findings can inform measures to promote vaccine acceptance and reinforce trust in health care systems, professionals, and scientific perspectives, thereby improving vaccination coverage. These insights may serve as a foundation for developing sociopolitical strategies to enhance vaccination management and address future pandemics or new vaccination campaigns.

Background: Hand, foot, and mouth disease (HFMD) is a global health concern requiring a risk assessment framework based on systematic factors analysis for prevention and control.

Objective: This study aims to construct a comprehensive HFMD risk assessment framework by integrating multisource data, including historical incidence information, environmental parameters, and web-based search behavior data, to improve predictive performance.

Methods: We integrated multisource data (HFMD cases, meteorology, air pollution, Baidu Index, and public health measures) from Bao'an District of Shenzhen city in Southern China (2014-2023). Correlation analysis was used to assess the associations between HFMD incidence and systematic factors. The impacts of environmental factors were analyzed using the Distributed Lag Nonlinear Model. Seasonal Autoregressive Integrated Moving Average model and advanced machine learning methods were used to predict HFMD 1-4 weeks ahead. Risk levels for the 1- to 4-week-ahead forecasts were determined by comparing the predicted weekly incidence against predefined thresholds.

Results: From 2014 to 2023, Bao'an District reported a total of 118,826 cases of HFMD. Environmental and search behavior factors (excluding sulfur dioxide) were significantly associated with HFMD incidence in nonlinear patterns. For 1-week-ahead prediction, Seasonal Autoregressive Integrated Moving Average using case data alone performed best (R²=0.95, r=0.98, mean absolute error=53.34, and root-mean-square error=99.31). For 2- to 4-week-ahead forecasting, machine learning models incorporating web-based and environmental data showed superior performance (R²=0.83, 0.75, and 0.64; r=0.92, 0.87, and 0.80; mean absolute error=87.84, 112.41, and 132.47; and root-mean-square error=185.08, 229.13, and 276.81). The predicted HFMD risk levels matched the observed levels with accuracies of 96%, 87%, 88%, and 83%, respectively.

Conclusions: The epidemic dynamics of HFMD are influenced by multiple factors in a nonlinear manner. Integrating multisource data, particularly web-based search behavior, significantly enhances the accuracy of short- and midterm forecasts and risk assessment. This approach offers practical insights for developing digital surveillance and early warning systems in public health.