Search Results (127)

Along with the coronavirus pandemic (COVID-19), some in the medical publication industry have observed an “infodemic”, which is more pandemic than the virus. Given the lack of sufficient pandemic preparedness measures in many countries, people started posting millions of posts on social media without questioning their veracity or accuracy, particularly within Arabic-speaking communities. This study investigates an unsupervised model for detecting fake news in Arabic to fight the infodemic. While there has been much research on fake news detection (FND) in English, this subject in Arabic has yet to be investigated enough in the literature. We examine the use of distribution-based clustering techniques for Arabic FND and show their performance compared to each other. Moreover, we conduct a comprehensive linguistic analysis, identifying significant differences in textual features between real and fake posts, which can improve fake news detection. Our research shows the potential of online learning techniques to enhance model performance, leading to high accuracy, reaching up to 92%. By addressing the unique challenges posed by Arabic-language posts, our research offers practical implications for developing effective strategies for reducing infodemics and their social consequences and for strategic planning to control the current and future infodemics. Full article

During elections, information manipulation on social media has accelerated the use of artificial intelligence, yet the evidence is difficult to interpret without an integrated view of methods, data, and evaluation. We mapped 557 English-language journal articles from Scopus and Web of Science, combining performance indicators, science mapping, and a focused full-text synthesis of highly cited papers. The literature grows sharply after 2019, peaks in 2025, and shows geographically uneven production, with collaboration structured around a small set of hubs. The thematic structure suggests that, during the pandemic era, infodemic-related research served as a catalyst, intensifying scientific attention to fake news and disinformation and expanding the associated detection and monitoring agendas. In addition, socio-political harm constructs such as hate speech, extremism, and polarization appear as recurrent and structurally central targets, highlighting that election-relevant work often extends beyond veracity assessment toward monitoring discourse risks. Blockchain also emerges as a novel and adjacent integrity theme, aligned with authenticity and provenance-oriented mitigation rather than mainstream detection pipelines. AI for electoral disinformation is not reducible to veracity classification, as influential studies also target automation and coordinated behavior, verification support, diffusion analysis, and estimation frameworks that focus on exposure and impact. Evaluation remains heterogeneous and is often shaped by benchmark settings, making high accuracy values hard to compare and potentially misleading when labeling quality, topic leakage, or context shift are not characterized. Overall, the findings motivate evaluation protocols that align operational objectives with modeling roles and explicitly address robustness to temporal and platform changes, asymmetric error costs during election windows, and representativeness across electoral contexts and languages, while also guiding future work on emerging integrity challenges and governance-relevant deployment settings. Full article

Travel-associated Legionnaires’ disease (TALD) events can generate public concern when environmental surveillance findings are communicated without an adequate explanation of the results. This study examined how surveillance data on Legionella spp. were framed and amplified during a TALD-related investigation in Crete, Greece, from June to July 2025. A mixed infodemiology and environmental surveillance approach was applied, including the analysis of 95 online media items across nine languages, Google Trends search-interest data, and hotel water-system surveillance data from epidemiologically linked facilities. Sampling conducted in a limited number of hotels associated with TALD cases indicated that approximately 50% of the water samples exceeded the laboratory reporting limit of ≥50 CFU/L for Legionella spp., a numerically correct but context-specific finding. Numerical misframing occurred in 83.7%, 41.7%, and 18.2% of Greek, German, and English language items, respectively, with significant differences across language markets (χ² (8) = 43.75, p < 0.0001; Cramér’s V = 0.679). Public search-interest signals were transient and geographically limited. Environmental surveillance showed no increase in Legionella pneumophila risk, with similar proportions of samples ≥50 CFU/L in the pre-/peri-infodemic (January–July 2025) and post-infodemic (August–November 2025) periods (23.11% [95% CI: 18.21–28.87] vs. 24.45% [19.34–30.41]) and similar exceedance of ≥1000 CFU/L (13.45% [9.69–18.36] vs. 14.41% [10.45–19.55]). Overall, the loss of contextual interpretation of surveillance results and conflation of laboratory reporting limits with regulatory thresholds were associated with inconsistent public risk perception, without evidence of increased environmental hazard. Full article

Global health faces unprecedented challenges driven by communicable diseases, which are increasingly amplified by persistent health inequities, the impact of climate change, and the speed of emerging crises. Prevention is not merely a component but the foundational strategy for an effective, sustainable, and fiscally responsible public health response. This paper delves into the pivotal role of core prevention levers: robust vaccination programs, stringent hygiene standards, advanced epidemiological surveillance, and targeted health education. We detail how contemporary technological advancements, including Artificial Intelligence (AI), big data analytics, and genomics, are fundamentally reshaping infectious disease management, enabling superior predictive capabilities, faster early warning systems, and personalized prevention models. Furthermore, we thoroughly examine the imperative of integrating the One Health approach, which formally recognizes the close, interdependent links between human, animal, and environmental health as critical for combating complex threats like zoonoses and Antimicrobial Resistance (AMR). Despite significant scientific progress, persistent socio-economic disparities, the pervasive influence of health-related misinformation (infodemics), and structural weaknesses in global preparedness underscore the urgent need for decisive international cooperation and equitable financing models. We conclude that only through integrated, multidisciplinary, and resource-equitable strategies can the global community ensure effective prevention, mitigate severe socio-economic disruption, and successfully build resilient healthcare systems capable of withstanding future global health threats. Full article

Background/Objectives: The COVID-19 pandemic highlighted how infodemics—an excessive amount of both accurate and misleading information—undermine health responses. Artificial intelligence (AI) and digital tools have been increasingly applied to monitor, detect, and counter health misinformation online. This scoping review aims to systematically map digital and AI-based interventions, describing their applications, outcomes, ethical and equity implications, and policy frameworks. Methods: This review followed the Joanna Briggs Institute methodology and was reported according to PRISMA-ScR. The protocol was preregistered on the Open Science Framework . Searches were conducted in PubMed/MEDLINE, Scopus, Web of Science, and CINAHL (January 2017–March 2025). Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved by a third reviewer. Data extraction included study characteristics, populations, technologies, outcomes, thematic areas, and domains. Quantitative synthesis used descriptive statistics with 95% confidence intervals. Results: A total of 63 studies were included, most published between 2020 and 2024. The majority originated from the Americas (41.3%), followed by Europe (15.9%), the Western Pacific (9.5%), and other regions; 22.2% had a global scope. The most frequent thematic areas were monitoring/surveillance (54.0%) and health communication (42.9%), followed by education/training, AI/ML model development, and digital engagement tools. The domains most often addressed were applications (63.5%), responsiveness, policies/strategies, ethical concerns, and equity/accessibility. Conclusions: AI and digital tools provide significant contributions in detecting misinformation, strengthening surveillance, and promoting health literacy. However, evidence remains heterogeneous, with geographic imbalances, reliance on proxy outcomes, and limited focus on vulnerable groups. Scaling these interventions requires transparent governance, multilingual datasets, ethical safeguards, and integration into public health infrastructures. Full article

We consider the problem of identifying the source of a rumor in a network, given only a snapshot observation of infected nodes after the rumor has spread. Classical approaches, such as the maximum likelihood (ML) and joint maximum likelihood (JML) estimators based on the conventional Susceptible–Infectious (SI) model, exhibit degeneracy, failing to uniquely identify the source even in simple network structures. To address these limitations, we propose a generalized estimator that incorporates independent random observation times. To capture the structure of information flow beyond graphs, our formulations consider rate constraints on the rumor and the multicast capacities for cyclic polylinking networks. Furthermore, we develop forward elimination and backward search algorithms for rate-constrained source detection and validate their effectiveness and scalability through comprehensive simulations. Our study establishes a rigorous and scalable foundation on the infodemic source detection. Full article

Background/Objectives: The COVID-19 pandemic was characterized by the rapid transmission of the virus and a global race for vaccines, with vaccines such as AstraZeneca, CoronaVac, Pfizer, and Janssen arriving in Brazil in 2020. Concurrently, an infodemic of information, driven by the media and social media, highlighted the importance of health communication. This study examines how online newspapers in a Brazilian state disseminated information about vaccination and its relationship with vaccine adherence among the population. Methods: Quantitative research, in which a total of 5308 journalistic articles were verified, using two databases, one for the publication of journalistic articles and the other for vaccinations in the state, which applied 9,577,567 doses in the period. Results: The analyses demonstrated a positive correlation between the number of publications of articles and the number of applications of vaccines (rho = 0.407, p-value < 0.0005), revealing a relationship of both increase and decrease in the publication of newspaper articles and the application of vaccines in specific weeks during the analysis period. Vaccination data revealed low adherence to the booster dose by the population, with unequal values among the cities of the state. Conclusions: The study highlighted the potential importance of newspapers in disseminating information about vaccines during the pandemic, underscoring the need for regional health strategies to increase vaccination coverage. Full article

The problem of geolocating Reddit users without access to the author information API is tackled in this study. Using subreddit data, we analyzed and identified user location based on their interactions within location-specific subreddits. Using unsupervised learning methods such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) algorithms, we examined conversations about COVID-19 and immunization across the U.S., focusing on COVID-19 vaccination. Our topic modeling identifies four themes: humor and sarcasm (e.g., jokes about microchips), conspiracy theories (e.g., tracking devices and microchips in the COVID-19 vaccine), public skepticism (e.g., debates over vaccine safety and freedom), and vaccine brand concerns (e.g., Pfizer, Moderna, and booster shots). Our geolocation analysis shows that regions with lower vaccination rates often exhibit a higher prevalence of misinformation-labeled comments. For example, counties such as Ada County (Idaho), Newton County (Missouri), and Flathead County (Montana) showed both a low vaccine uptake and a high rate of false information. This study provides useful information on the many different examples of misinformation that are disseminated online. It gives us a better understanding of how people in different parts of the U.S. think about getting a COVID-19 vaccine. Full article

Background and Objectives: Rumors, disinformation, and fake news are problems of contemporary society. We live in a world where the truth no longer holds much importance, and the line that divides the truth from lies, between real news and disinformation, becomes increasingly blurred and difficult to identify. The purpose of this study is to describe this concept, to draw attention to one of the “pandemics” of the 21st-century world, and to find methods by which we can defend ourselves against them. Materials and methods. A cross-sectional study was conducted based on a sample of 442 respondents. Results. For 77.8% of the people surveyed, the concept of “fake news” is important in Romania. Regarding trust in the mass media, a clear dominance (72.4%) was observed among participants who have little trust in the mass media. Although 98.2% of participants detect false information found on the internet, 78.5% are occasionally deceived by the information provided. Of the participants, 47.3% acknowledged their vulnerability to disinformation. The main source of disinformation is the internet, as 59% of the interviewed subjects believed. As the best measure against disinformation, the study group was divided almost equally according to the three possible answers, all of which were considered to be equally important: imposing legal restrictions and blocking the posting of certain news (35.4%), imposing stricter measures for authors (33.9%), and increasing vigilance among people (30.5%). Conclusions. According to the statistics based on the participants’ responses, the main purposes of disinformation are propaganda, manipulation, distracting attention from the truth, making money, and misleading the population. It can be observed that the main intention of disinformation, in the perception of the study participants, is manipulation. Full article

The management of public health projects in a BANI (brittle, anxious, non-linear, incomprehensible) environment, exemplified by the ongoing war in Ukraine, presents unprecedented challenges due to fragile systems, heightened uncertainty, and complex socio-political dynamics. This study proposes an AI-driven framework to enhance the resilience and effectiveness of public health interventions under such conditions. By integrating a coupled SEIR–Infodemic–Panicdemic Model with war-specific factors, we simulate the interplay of infectious disease spread, misinformation dissemination, and panic dynamics over 1500 days in a Ukrainian city (Kharkiv). The model incorporates time-varying parameters to account for population displacement, healthcare disruptions, and periodic war events, reflecting the evolving conflict context. Sensitivity and risk–opportunity analyses reveal that disease transmission, misinformation, and infrastructure damage significantly exacerbate epidemic peaks, while AI-enabled interventions, such as fact-checking, mental health support, and infrastructure recovery, offer substantial mitigation potential. Qualitative assessments identify technical, organisational, ethical, regulatory, and military risks, alongside opportunities for predictive analytics, automation, and equitable healthcare access. Quantitative simulations demonstrate that risks, like increased displacement, can amplify infectious peaks by up to 28.3%, whereas opportunities, like enhanced fact-checking, can reduce misinformation by 18.2%. These findings provide a roadmap for leveraging AI to navigate BANI environments, offering actionable insights for public health practitioners in Ukraine and other crisis settings. The study underscores AI’s transformative role in fostering adaptive, data-driven strategies to achieve sustainable health outcomes amidst volatility and uncertainty. Full article

Scientific disinformation has emerged as a critical challenge at the interface of science and society. This paper examines how false or misleading scientific content proliferates across both social media and traditional media and evaluates strategies to counteract its spread. We conducted a comprehensive literature review of research on scientific misinformation across disciplines and regions, with particular focus on climate change and public health as exemplars. Our findings indicate that social media algorithms and user dynamics can amplify false scientific claims, as seen in case studies of viral misinformation campaigns on vaccines and climate change. Traditional media, meanwhile, are not immune to spreading inaccuracies—journalistic practices such as sensationalism or “false balance” in reporting have at times distorted scientific facts, impacting public understanding. We review efforts to fight disinformation, including technological tools for detection, the application of inoculation theory and prebunking techniques, and collaborative approaches that bridge scientists and journalists. To empower individuals, we propose practical guidelines for critically evaluating scientific information sources and emphasize the importance of digital and scientific literacy. Finally, we discuss methods to quantify the prevalence and impact of scientific disinformation—ranging from social network analysis to surveys of public belief—and compare trends across regions and scientific domains. Our results underscore that combating scientific disinformation requires an interdisciplinary, multi-pronged approach, combining improvements in science communication, education, and policy. We conducted a scoping review of 85 open-access studies focused on climate-related misinformation and disinformation, selected through a systematic screening process based on PRISMA criteria. This approach was chosen to address the lack of comprehensive mappings that synthesize key themes and identify research gaps in this fast-growing field. The analysis classified the literature into 17 thematic clusters, highlighting key trends, gaps, and emerging challenges in the field. Our results reveal a strong dominance of studies centered on social media amplification, political denialism, and cognitive inoculation strategies, while underlining a lack of research on fact-checking mechanisms and non-Western contexts. We conclude with recommendations for strengthening the resilience of both the public and information ecosystems against the spread of false scientific claims. Full article

The COVID-19 pandemic intensified concerns about misinformation, sparking interest in the field of infodemiology, which examines the spread and impact of information on public health perceptions. This research examines how geographic location influenced COVID-19 discourse across 10 Italian cities by analyzing geographically tagged Twitter data. Our network analysis of 4792 high-degree nodes identifies key information spreaders and community structures, while spatiotemporal mapping reveals regional variations in information patterns and influential narratives. Results demonstrate significant geographic and cultural influences on public discourse. In Milan and Rome, economic and political narratives dominated, suggesting targeted messaging about economic recovery and government transparency. Southern regions like Naples require trust-building through community-led initiatives addressing cultural health beliefs. The study identified a clear dichotomy among influencers: established public figures provided evidence-based information, while another group cultivated followings through conspiracy theories, creating echo chambers for skeptical views. This research informs strategies for location-specific information campaigns, helping public health agencies combat misinformation more effectively. Findings emphasize the need for context-specific interventions that consider geographic, cultural, and socioeconomic factors to enhance community resilience during health emergencies. Full article

Background and Objectives: Schizophrenia is a significant public health issue, and YouTube has become an increasingly popular source of health information. This study aims to assess the quality and validity of YouTube videos about schizophrenia, focusing on the presence of scientific evidence and the role of healthcare professionals in content quality. Methods: A retrospective, cross-sectional observational study was conducted. One hundred videos in Spanish were selected using NodeXL Pro software, based on specific keywords and hashtags. The videos were categorized by content type and assessed using the DISCERN and Global Quality Scale [GQS] tools to evaluate quality and reliability. Results: Only 39% of the videos referenced scientific articles or technical documents. The videos created by healthcare professionals exhibited a higher quality and reliability. Significant differences were found in the DISCERN and GQS scores between the videos presenting personal opinions and those providing scientific information, favoring the latter. Conclusion: There is a prevalence of misinformation about schizophrenia on YouTube. To enhance the educational value of the platform and reduce misinformation risks, involving healthcare professionals in content creation and implementing control mechanisms is essential. Full article

The spread of health-related misinformation has become a significant global challenge, particularly during the COVID-19 pandemic. This study introduces a comprehensive framework for detecting and analyzing misinformation using advanced natural language processing techniques. The proposed classification model combines BERT embeddings with Bi-LSTM architecture and attention mechanisms, achieving high performance, including 99.47% accuracy and an F1-score of 0.9947. In addition to classification, topic modeling is employed to identify thematic clusters, providing valuable insights into misinformation narratives. The findings demonstrate the effectiveness and reliability of the proposed methodology in detecting misinformation while offering tools for understanding its underlying themes. The adaptable and scalable approach makes it applicable to various domains and datasets. This research improves public health communication and combating misinformation in digital environments. Full article

The aim of this study was to assess the association between the use of and trust in sources of information, knowledge about human papillomavirus (HPV) and vaccines against it, perceived lack of information, and the decision to receive the HPV vaccine in undergraduate students in Belgrade. The sample of this cross-sectional study included students aged 18 to 27 who received the second dose of the HPV vaccine or used other services of the general medicine department at the Institute for Students’ Health of Belgrade during the period June–July 2024. The research instrument was a questionnaire consisting of socio-demographic data, information environment (sources of information, trust in sources of information, as well as questions related to perceived lack of information), knowledge about HPV and HPV vaccines, and vaccination status. Participants filled out an online questionnaire created on the RedCap platform of the Faculty of Medicine, University of Belgrade, which they accessed via a QR code. Hierarchical logistic regression was used to assess the association between vaccine status and socio-demographic characteristics, use and trust in information sources, knowledge, and perceived lack of information. Of the 603 participants who filled out the questionnaire completely, 78.6% were vaccinated against HPV. Key factors associated with vaccine uptake were female gender (OR = 2.33, p < 0.05), use of scientific literature (OR = 1.40, p < 0.05) and family as a source of information (OR = 1.37, p < 0.01), less frequent use of regional TV channels (OR = 0.76, p < 0.05), higher level of knowledge (OR = 1.43, p < 0.01), and lower perceived lack of information (OR = 0.50, p < 0.01). These variables explained 41% of variability in vaccine uptake in the multivariate hierarchical logistic regression model. Exposure to and trust in sources of information were significantly associated with knowledge about HPV and HPV vaccination, as well as with the perceived lack of information regarding HPV vaccination, and were the most significant determinants of the decision to accept HPV vaccine in the student population. Full article