Explore the vast range of titles available.

The widespread dissemination of misinformation through social media poses significant challenges. With the increasing prevalence of social media, vast amounts of information - both accurate and inaccurate - can be rapidly shared among a large audience. This information often plays a critical role in shaping public opinion and influencing significant events, such as elections. This paper addresses the urgent need for more effective models to combat misinformation. We propose a novel approach based on the Susceptible-Infective-Removed (SIR) model, traditionally used in studying information diffusion. Our modified model, termed SIRMIS (SIR-based Misinformation Spreading Model), integrates stochastic differential equations (SDEs) to account for the inherent randomness and uncertainty in information diffusion processes on social networks. SIRMIS offers insights into the dynamics of misinformation propagation, the role of accurate information in counteracting falsehoods, and the estimation of the peak number of misinformed users. The stability of the stochastic equations within the SIRMIS model is rigorously proven using Lyapunov stability theory, ensuring that the model reliably predicts the conditions under which misinformation can be controlled or eradicated. Our results indicate that systematically increasing the exposure of accurate, verified information to key segments of the population can slow down or even completely combat the spread of misinformation. This effect is particularly pronounced when the true information is disseminated by trusted sources, highlighting the importance of credibility in combating falsehoods. Furthermore, we discuss the linear and global stability of the proposed model, emphasizing its potential in effectively mitigating the impact of misinformation on social networks.

Background Health-related conspiracy theories undermine trust in healthcare, exacerbate health inequities, and contribute to harmful health behaviors such as vaccine hesitancy and reliance on unproven treatments. These theories disproportionately impact marginalized populations, further widening health disparities. Their rapid spread, amplified by social media algorithms and digital misinformation networks, exacerbates public health challenges, highlighting the urgency of understanding their prevalence, key drivers, and mitigation strategies. Methods This scoping review synthesizes research on health-related conspiracy theories, focusing on their prevalence, impacts on health behaviors and outcomes, contributing factors, and counter-measures. Using Arksey and O’Malley’s framework and the Joanna Briggs Institute guidelines, a systematic search of six databases (PubMed, Embase, Web of Science, CINAHL, PsycINFO, and Scopus) was conducted. Studies were screened using predefined inclusion and exclusion criteria, with thematic synthesis categorizing findings across diverse health contexts. Results The review revealed pervasive conspiracy beliefs surrounding HIV/AIDS, vaccines, pharmaceutical companies, and COVID-19, linked to reduced vaccine uptake, increased mistrust in health authorities, and negative mental health outcomes such as anxiety and depression. Key drivers included sociopolitical distrust, cognitive biases, low scientific literacy, and the unchecked proliferation of misinformation on digital platforms. Promising countermeasures included inoculation messaging, media literacy interventions, and two-sided refutational techniques. However, their long-term effectiveness remains uncertain, as few studies assess their sustained impact across diverse sociopolitical contexts. Conclusion Health-related conspiracy theories present a growing public health challenge that undermines global health equity. While several interventions show potential, further research is needed to evaluate their effectiveness across diverse populations and contexts. Targeted efforts to rebuild trust in healthcare systems and strengthen critical health literacy are essential to mitigate the harmful effects of these conspiracy beliefs.

The problem of fake news detection (FND) is becoming increasingly important in the field of natural language processing (NLP) because of the rapid dissemination of misleading information on the web. Large language models (LLMs) such as GPT-4. Zero excels in natural language understanding tasks but can still struggle to distinguish between fact and fiction, particularly when applied in the wild. However, a key challenge of existing FND methods is that they only consider unimodal data (e.g., images), while more detailed multimodal data (e.g., user behaviour, temporal dynamics) is neglected, and the latter is crucial for full-context understanding. To overcome these limitations, we introduce M3-FND (Multimodal Misinformation Mitigation for False News Detection), a novel methodological framework that integrates LLMs with multimodal data sources to perform context-aware veracity assessments. Our method proposes a hybrid system that combines image-text alignment, user credibility profiling, and temporal pattern recognition, which is also strengthened through a natural feedback loop that provides real-time feedback for correcting downstream errors. We use contextual reinforcement learning to schedule prompt updating and update the classifier threshold based on the latest multimodal input, which enables the model to better adapt to changing misinformation attack strategies. M3-FND is tested on three diverse datasets, FakeNewsNet, Twitter15, and Weibo, which contain both text and visual social media content. Experiments show that M3-FND significantly outperforms conventional and LLM-based baselines in terms of accuracy, F1-score, and AUC on all benchmarks. Our results indicate the importance of employing multimodal cues and adaptive learning for effective and timely detection of fake news.

The rapid evolution of Generative AI and large language models has drastically increased the risk of information disorder across digital communication platforms. The synthetically created messages with a deepfake aiming to mislead, manipulate the opinion of the masses, or disinformation is a serious danger to the honesty and credibility of online communication. Addressing this growing challenge requires an effective and transparent detection framework that can operate effectively at scale. This study introduces DEFT-Net (Deepfake Explainable FastText Tri-Convolutional Network), a practical and explainable deep learning architecture specifically designed for deepfake text detection on social media platforms. DEFT-Net leverages a multi-scale convolutional neural network (CNN) architecture combined with FastText embeddings to capture both syntactic and semantic patterns characteristic of synthetic content. The model was trained and evaluated using the TweepFake dataset, a rich collection of real and deepfake tweets, enabling the development of an efficient classifier capable of distinguishing authentic from fabricated text. To ensure transparency and foster trust in AI-based decision-making, this work integrates Explainable AI (XAI) techniques to provide interpretability of the model’s predictions. Experimental results demonstrate that DEFT-Net achieves high detection performance attaining 93% accuracy, 92% precision, 95% recall, and a 93% F1-score, outperforming several evaluated baseline models on the TweepFake dataset using FastText, subword embeddings, BERT embeddings, and GloVe. The proposed framework can further aid in the overall combating of information disorder by providing a practical, explainable, and computationally efficient solution for deepfake text detection, which may have potential uses in digital forensics, online safety, and safeguarding information integrity.

This research introduces a systematic and multidisciplinary agent-based model to interpret and simplify the dynamic actions of the users and communities in an evolutionary online (offline) social network. The organizational cybernetics approach is used to control/monitor the malicious information spread between communities. The stochastic one-median problem minimizes the agent response time and eliminates the information spread across the online (offline) environment. The performance of these methods was measured against a Twitter network related to an armed protest demonstration against the COVID-19 lockdown in Michigan state in May 2020. The proposed model demonstrated the dynamicity of the network, enhanced the agent level performance, minimized the malicious information spread, and measured the response to the second stochastic information spread in the network.

Misinformation and disinformation spread rapidly on social media, threatening public discourse, democratic processes, and social cohesion. One promising strategy to address these challenges is to evaluate the trustworthiness of entire domains (source websites) as a proxy for content credibility. This approach demands methods that are both scalable and data-driven. However, current solutions such as NewsGuard and Media Bias/Fact Check (MBFC) rely on expert assessments, cover only a limited number of domains, and some (e.g., NewsGuard) require paid subscriptions. These constraints limit their usefulness for large-scale research. This study introduces a machine-learning-based system designed to assess the quality and trustworthiness of websites. We propose a data-driven approach that leverages a large dataset of expert-rated domains to predict credibility scores for previously unseen domains using domain-level features. Our supervised regression model achieves moderate performance on test data and high performance on independent datasets, highlighting its ability to generalize to unseen domains. Using feature importance analysis, we found that PageRank-based features provided the greatest reduction in prediction error, suggesting that link-based indicators play a central role in domain trustworthiness. The solution’s scalable design accommodates the continuously evolving nature of online content, ensuring that evaluations remain timely and relevant. The framework enables continuous assessment of thousands of domains with minimal manual effort. This capability allows stakeholders (social media platforms, media monitoring organizations, content moderators, and researchers) to allocate resources more efficiently, prioritize verification efforts, and reduce exposure to questionable sources.

The global spread of the novel coronavirus is affected by the spread of related misinformation—the so-called COVID-19 Infodemic—that makes populations more vulnerable to the disease through resistance to mitigation efforts. Here, we analyze the prevalence and diffusion of links to low-credibility content about the pandemic across two major social media platforms, Twitter and Facebook. We characterize cross-platform similarities and differences in popular sources, diffusion patterns, influencers, coordination, and automation. Comparing the two platforms, we find divergence among the prevalence of popular low-credibility sources and suspicious videos. A minority of accounts and pages exert a strong influence on each platform. These misinformation “superspreaders” are often associated with the low-credibility sources and tend to be verified by the platforms. On both platforms, there is evidence of coordinated sharing of Infodemic content. The overt nature of this manipulation points to the need for societal-level solutions in addition to mitigation strategies within the platforms. However, we highlight limits imposed by inconsistent data-access policies on our capability to study harmful manipulations of information ecosystems.

During COVID-19, governments and the public are fighting not only a pandemic but also a co-evolving infodemic—the rapid and far-reaching spread of information of questionable quality. We analysed more than 100 million Twitter messages posted worldwide during the early stages of epidemic spread across countries (from 22 January to 10 March 2020) and classified the reliability of the news being circulated. We developed an Infodemic Risk Index to capture the magnitude of exposure to unreliable news across countries. We found that measurable waves of potentially unreliable information preceded the rise of COVID-19 infections, exposing entire countries to falsehoods that pose a serious threat to public health. As infections started to rise, reliable information quickly became more dominant, and Twitter content shifted towards more credible informational sources. Infodemic early-warning signals provide important cues for misinformation mitigation by means of adequate communication strategies. An analysis of news shared on Twitter estimates the level of infodemic risk associated with COVID-19 across countries. Epidemic spread and infodemic risk co-evolve, with reliable information becoming more dominant as infection rates rise locally.

The article explores the concept of infodemics during the COVID-19 pandemic, focusing on the propagation of false or inaccurate information proliferating worldwide throughout the SARS-CoV-2 health crisis. We provide an overview of disinformation, misinformation and malinformation and discuss the notion of “fake news”, and highlight the threats these phenomena bear for health policies and national and international security. We discuss the mis-/disinformation as a significant challenge to the public health, intelligence, and policymaking communities and highlight the necessity to design measures enabling the prevention, interdiction, and mitigation of such threats. We then present an overview of selected opportunities for applying technology to study and combat disinformation, outlining several approaches currently being used to understand, describe, and model the phenomena of misinformation and disinformation. We focus specifically on complex networks, machine learning, data- and text-mining methods in misinformation detection, sentiment analysis, and agent-based models of misinformation spreading and the detection of misinformation sources in the network. We conclude with the set of recommendations supporting the World Health Organization’s initiative on infodemiology. We support the implementation of integrated preventive procedures and internationalization of infodemic management. We also endorse the application of the cross-disciplinary methodology of Crime Science discipline, supplemented by Big Data analysis and related information technologies to prevent, disrupt, and detect mis- and disinformation efficiently.

Policy Points Nudges steer people toward certain options but also allow them to go their own way. “Dark nudges” aim to change consumer behavior against their best interests. “Sludge” uses cognitive biases to make behavior change more difficult. We have identified dark nudges and sludge in alcohol industry corporate social responsibility (CSR) materials. These undermine the information on alcohol harms that they disseminate, and may normalize or encourage alcohol consumption. Policymakers and practitioners should be aware of how dark nudges and sludge are used by the alcohol industry to promote misinformation about alcohol harms to the public. Context “Nudges” and other behavioral economic approaches exploit common cognitive biases (systematic errors in thought processes) in order to influence behavior and decision‐making. Nudges that encourage the consumption of harmful products (for example, by exploiting gamblers’ cognitive biases) have been termed “dark nudges.” The term “sludge” has also been used to describe strategies that utilize cognitive biases to make behavior change harder. This study aimed to identify whether dark nudges and sludge are used by alcohol industry (AI)–funded corporate social responsibility (CSR) organizations, and, if so, to determine how they align with existing nudge conceptual frameworks. This information would aid their identification and mitigation by policymakers, researchers, and civil society. Methods We systematically searched websites and materials of AI CSR organizations (e.g., IARD, Drinkaware, Drinkwise, Éduc'alcool); examples were coded by independent raters and categorized for further analysis. Findings Dark nudges appear to be used in AI communications about “responsible drinking.” The approaches include social norming (telling consumers that “most people” are drinking) and priming drinkers by offering verbal and pictorial cues to drink, while simultaneously appearing to warn about alcohol harms. Sludge, such as the use of particular fonts, colors, and design layouts, appears to use cognitive biases to make health‐related information about the harms of alcohol difficult to access, and enhances exposure to misinformation. Nudge‐type mechanisms also underlie AI mixed messages, in particular alternative causation arguments, which propose nonalcohol causes of alcohol harms. Conclusions Alcohol industry CSR bodies use dark nudges and sludge, which utilize consumers’ cognitive biases to promote mixed messages about alcohol harms and to undermine scientific evidence. Policymakers, practitioners, and the public need to be aware of how such techniques are used to nudge consumers toward industry misinformation. The revised typology presented in this article may help with the identification and further analysis of dark nudges and sludge.