Health misinformation: protocol for a hybrid concept analysis and development.

Background: Misinformation represents a serious and growing concern for public health and healthcare health; and has attracted much interest from researchers, media, and the public over recent years. Despite increased concern about the impacts of misinformation on health and wellbeing, however, the concept of health misinformation remains underdeveloped. In particular, there is a need to clarify how certain types of health information come to be designated as "misinformation," what characteristics are associated with this classification, and how the concept of misinformation is applied in health contexts.

Evaluating the Influence of Role-Playing Prompts on ChatGPT's Misinformation Detection Accuracy: Quantitative Study.

Background: During the COVID-19 pandemic, the rapid spread of misinformation on social media created significant public health challenges. Large language models (LLMs), pretrained on extensive textual data, have shown potential in detecting misinformation, but their performance can be influenced by factors such as prompt engineering (ie, modifying LLM requests to assess changes in output). One form of prompt engineering is role-playing, where, upon request, OpenAI's ChatGPT imitates specific social roles or identities.

Management of infodemics in outbreaks or health crises: a systematic review.

Introduction: The World Health Organization (WHO) defined an infodemic as an overabundance of information, accurate or not, in the digital and physical space, accompanying an acute health event such as an outbreak or epidemic. It can impact people's risk perceptions, trust, and confidence in the health system, and health workers. As an immediate response, the WHO developed the infodemic management (IM) frameworks, research agenda, intervention frameworks, competencies, and processes for reference by health authorities.

Chatbots and COVID-19: Taking Stock of the Lessons Learned.

While digital innovation in health was already rapidly evolving, the COVID-19 pandemic has accelerated the generation of digital technology tools, such as chatbots, to help increase access to crucial health information and services to those who were cut off or had limited contact with health services. This theme issue titled "Chatbots and COVID-19" presents articles from researchers and practitioners across the globe, describing the development, implementation, and evaluation of chatbots designed to address a wide range of health concerns and services. In this editorial, we present some of the key challenges and lessons learned arising from the content of this theme issue.

Expressions of pandemic fatigue on digital platforms: a thematic analysis of sentiment and narratives for infodemic insights.

Background: The infodemic accompanying the COVID-19 pandemic has led to an overwhelming amount of information, including questions, concerns and misinformation. Pandemic fatigue has been identified as a concern from early in the pandemic. With new and ongoing health emergencies in 2022, it is important to understand how pandemic fatigue is being discussed and expressed by users on digital channels.

Informing social media analysis for public health: a cross-sectional survey of professionals.

Background: During the COVID-19 pandemic, the field of infodemic management has grown in response to urgent global need. Social listening is the first step in managing the infodemic, and many organizations and health systems have implemented processes. Social media analysis tools have traditionally been developed for commercial purposes, rather than public health, and little is known of the experiences and needs of those professionals using them for infodemic management.

Using Machine Learning Technology (Early Artificial Intelligence-Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study.

Background: Amid the COVID-19 pandemic, there has been a need for rapid social understanding to inform infodemic management and response. Although social media analysis platforms have traditionally been designed for commercial brands for marketing and sales purposes, they have been underused and adapted for a comprehensive understanding of social dynamics in areas such as public health. Traditional systems have challenges for public health use, and new tools and innovative methods are required.

Infodemic Insights on Trust in a Health Emergency: A Narrative Deep-Dive.

Trust in authorities is important during health emergencies, and there are many factors that influence this. The infodemic has resulted in overwhelming amounts of information being shared on digital media during the COVID-19 pandemic, and this research looked at trust-related narratives during a one-year period. We identified three key findings related to trust and distrust narratives, and a country-level comparison showed less mistrust narratives in a country with a higher level of trust in government.

Measuring the burden of infodemics with a research toolkit for connecting information exposure, trust, and health behaviours.

Background: During a public health emergency, accurate and useful information can be drowned out by questions, concerns, information voids, conflicting information, and misinformation. Very few studies connect information exposure and trust to health behaviours, which limits available evidence to inform when and where to act to mitigate the burden of infodemics, especially in low resource settings. This research describes the features of a toolkit that can support studies linking information exposure to health behaviours at the individual level.

Establishing Infodemic Management in Germany: A Framework for Social Listening and Integrated Analysis to Report Infodemic Insights at the National Public Health Institute.

Background: To respond to the need to establish infodemic management functions at the national public health institute in Germany (Robert Koch Institute, RKI), we explored and assessed available data sources, developed a social listening and integrated analysis framework, and defined when infodemic management functions should be activated during emergencies.

Objective: We aimed to establish a framework for social listening and integrated analysis for public health in the German context using international examples and technical guidance documents for infodemic management.

Methods: This study completed the following objectives: identified (potentially) available data sources for social listening and integrated analysis; assessed these data sources for their suitability and usefulness for integrated analysis in addition to an assessment of their risk using the RKI's standardized data protection requirements; developed a framework and workflow to combine social listening and integrated analysis to report back actionable infodemic insights for public health communications by the RKI and stakeholders; and defined criteria for activating integrated analysis structures in the context of a specific health event or health emergency.

Narrative Trends over the COVID-19 Pandemic: Digital Social Listening to Inform WHO Infodemic Management.

The COVID-19 infodemic is an overwhelming amount of information that has challenged pandemic communication and epidemic response. WHO has produced weekly infodemic insights reports to identify questions, concerns, information voids expressed and experienced by people online. Publicly available data was collected and categorized to a public health taxonomy to enable thematic analysis.

An Adaptive Digital Intelligence System to Support Infodemic Management: The WHO EARS Platform.

The WHO Early AI-Supported Response with Social Listening (EARS) platform was developed to help inform infodemic response during the COVID-19 pandemic. There was continual monitoring and evaluation of the platform and feedback from end-users was sought on a continual basis. Iterations were made to the platform in response to user needs, including the introduction of new languages and countries, and additional features to better enable more fine-grained and rapid analysis and reporting.

Measuring the Burden of Infodemics: Summary of the Methods and Results of the Fifth WHO Infodemic Management Conference.

Background: An infodemic is excess information, including false or misleading information, that spreads in digital and physical environments during a public health emergency. The COVID-19 pandemic has been accompanied by an unprecedented global infodemic that has led to confusion about the benefits of medical and public health interventions, with substantial impact on risk-taking and health-seeking behaviors, eroding trust in health authorities and compromising the effectiveness of public health responses and policies. Standardized measures are needed to quantify the harmful impacts of the infodemic in a systematic and methodologically robust manner, as well as harmonizing highly divergent approaches currently explored for this purpose.

The Good Talk! A Serious Game to Boost People's Competence to Have Open Conversations About COVID-19: Protocol for a Randomized Controlled Trial.

Background: Vaccine hesitancy is one of the many factors impeding efforts to control the COVID-19 pandemic. Exacerbated by the COVID-19 infodemic, misinformation has undermined public trust in vaccination, led to greater polarization, and resulted in a high social cost where close social relationships have experienced conflict or disagreements about the public health response.

Objective: The purpose of this paper is to describe the theory behind the development of a digital behavioral science intervention-The Good Talk!-designed to target vaccine-hesitant individuals through their close contacts (eg, family, friends, and colleagues) and to describe the methodology of a research study to evaluate its efficacy.

Empowering Health Workers to Build Public Trust in Vaccination: Experience from the International Pediatric Association's Online Vaccine Trust Course, 2020-2021.

Background: The quality of interactions between health workers (HWs) and caregivers is key in vaccine acceptance. To optimize this, HWs need knowledge about best vaccine communication practices in person and on social media. Most pre-service curricula do not include such approaches.

WHO competency framework for health authorities and institutions to manage infodemics: its development and features.

Background: In April 2020, the World Health Organization (WHO) Information Network for Epidemics produced an agenda for managing the COVID-19 infodemic. "Infodemic" refers to the overabundance of information-including mis- and disinformation. In this agenda it was pointed out the need to create a competency framework for infodemic management (IM).

Infodemics: A new challenge for public health.

The COVID-19 information epidemic, or "infodemic," demonstrates how unlimited access to information may confuse and influence behaviors during a health emergency. However, the study of infodemics is relatively new, and little is known about their relationship with epidemics management. Here, we discuss unresolved issues and propose research directions to enhance preparedness for future health crises.

A Public Health Research Agenda for Managing Infodemics: Methods and Results of the First WHO Infodemiology Conference.

Background: An infodemic is an overflow of information of varying quality that surges across digital and physical environments during an acute public health event. It leads to confusion, risk-taking, and behaviors that can harm health and lead to erosion of trust in health authorities and public health responses. Owing to the global scale and high stakes of the health emergency, responding to the infodemic related to the pandemic is particularly urgent.

Infodemic Signal Detection During the COVID-19 Pandemic: Development of a Methodology for Identifying Potential Information Voids in Online Conversations.

Background: The COVID-19 pandemic has been accompanied by an : excess information, including false or misleading information, in digital and physical environments during an acute public health event. This infodemic is leading to confusion and risk-taking behaviors that can be harmful to health, as well as to mistrust in health authorities and public health responses. The World Health Organization (WHO) is working to develop tools to provide an evidence-based response to the infodemic, enabling prioritization of health response activities.

EARS - A WHO Platform for AI-Supported Real-Time Online Social Listening of COVID-19 Conversations.

As the COVID-19 pandemic evolves, the accompanying infodemic is being amplified through social media and has challenged effective response. The WHO Early AI-supported Response with Social Listening (EARS) is a platform that summarizes real-time information about how people are talking about COVID-19 in public spaces online in 20 pilot countries and in four languages. The aim of the platform is to better integrate social listening with other data sources and analyses that can inform infodemic response.

WHO Digital Intelligence Analysis for Tracking Narratives and Information Voids in the COVID-19 Infodemic.

The COVID-19 pandemic is the first to unfold in the highly digitalized society of the 21st century and is therefore the first pandemic to benefit from and be threatened by a thriving real-time digital information ecosystem. For this reason, the response to the infodemic required development of a public health social listening taxonomy, a structure that can simplify the chaotic information ecosystem to enable an adaptable monitoring infrastructure that detects signals of fertile ground for misinformation and guides trusted sources of verified information to fill in information voids in a timely manner. A weekly analysis of public online conversations since 23 March 2020 has enabled the quantification of running shifts of public interest in public health-related topics concerning the pandemic and has demonstrated the frequent resumption of information voids relevant for public health interventions and risk communication in an emergency response setting.