Becky Kate White

UNSTRUCTURED The information environment shapes what health information people encounter, how they interpret it, and whether they act on it. It is, therefore, a structural determinant of health. Yet public health scholarship and practice often reduce it to communication issue, in essence treating it asa vehicle for addressing problems in health information delivery through faster correction of misinformation better messaging and more effective social marketing. Addressing information environment challenges in health is especially slow moving from research into practice, because academic studies are narrow by design while public health practitioners work in settings that are complex, underfunded, and pressed to act in the present on many fronts at once. This Viewpoint sets out five structural tensions that fragment how public health institutions currently address misinformation and the information environment in health. They recur across research, practice, and policy, and each persists because both sides of it are needed. They run between 1) content and structure, 2) information and discourse, 3) emergency and sustained action, 4) universal and context-specific approaches, and 5) territorial governance and the transnational flow of health information. They also clarify what different kinds of evidence can support, and why evidence from narrowly defined interventions is often difficult to translate into responses that must operate across systems, communities, and time. These tensions matter because data do not become health action on their own; they must be translated into evidence, communicated as information, discussed and trusted enough to guide decisions. Taken together, they reveal the points at which this chain is weakened, distorted, or rendered difficult to govern. Current attention and effort concentrate on what claims or narratives can be measured and corrected, while the structural conditions that produce harmful information and discourse are harder to measure and harder to act on. The aim is not to resolve these tensions once and for all, but to use them diagnostically; demonstrating where evidence is strong, where governance is weak, and where trust, meaning, and context shape whether health information can support action. Infodemiology is distinct from other fields studying the information environment because its purpose is health. Its task is to develop approaches and measures suited to these tensions, attentive to how trust is earned and how people act on health information over time and grounded in the mandate of health systems to work not only against disease but toward wellbeing.

Background: With the advances in digital information sharing channels, democratization of content, and access, as well as social shifts in information exchange, we live in increasingly complex information environments. How people process and manage this is layered with multiple determinants that can impact information seeking, health behaviors, and public health. Understanding the dynamics of the information environment in priority populations and its impact on communities and individuals is critical for those working in public health and health emergencies. Objective: This study aimed to provide an overview of the approaches to and implementation of information environment assessments as they relate to public health and health emergencies. Methods: We conducted a rapid scoping review of the approaches to, and implementation of information environment assessments. The search followed guidance from the Joanna Briggs Institute on conducting systematic scoping reviews, and our reporting is in line with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews. We included both academic and gray literature in the English language. As this is an emerging field, an additional step involved input from an informal expert group to identify any further tools or approaches. Studies that assessed, described, or discussed approaches to assessing the information environment were included. We excluded papers where the information environment was not the primary focus, or the focus was on individual components only. Two authors (BKW and SVM) independently screened results for inclusion. Results: A total of 17 publications were identified through the structured literature and internet searches, with an additional 5 sourced from the informal expert group. The review highlighted a significant variety in the breadth and number of domains covered in an assessment, including information needs, seeking, access, production, engagement, information quality, and reach. Some assessments adopted a comprehensive, systems-oriented approach, examining factors influencing information beyond the individual level to encompass broader systemic dynamics, while others were significantly narrower in scope. Conclusions: The COVID-19 pandemic has intensified interest in understanding how the information environment shapes people’s access to, engagement with, and ability to act on health information. Assessing the information environment is a critical step in identifying and understanding barriers and facilitators that impact different populations and identifying opportunities for strengthening systems. However, a universally accepted approach for such assessments in public health and health emergencies is currently lacking. This paper contributes to the literature by synthesizing current knowledge on assessment tools and frameworks, providing a foundation for future research and development in this area.

Issue Addressed: During the coronavirus disease 2019 (COVID‐19) pandemic, the infodemic has been highly visible. Vaccine‐related advice and evidence‐based guidelines for breastfeeding women have lagged, and, at times have been contradictory and confusing. Breastfeeding is an important public health issue with long‐lasting health benefits for infants and mothers. This online social listening study aimed to understand breastfeeding‐related narratives on social media during the early stages of the Australian COVID‐19 vaccine roll‐out.Methods: Public content about breastfeeding and COVID‐19 vaccines from Facebook pages with Australian‐based administrators were accessed via the CrowdTangle platform. All related content posted between 1 December 2020–31 December 2021 was included. Content (n = 29 567) was coded to an adapted vaccine narrative typology, and emergent themes were inductively identified.Results: Most posts were promoting, sharing or discussing reliable information. However, the comments responding to these posts varied more widely. Several consistent questions, concerns, and information voids were evident, and misinformation and conflicting information contributed to confusion and challenges with decision‐making.Conclusions: These findings highlight the complexities associated with decision‐making. Pregnant and breastfeeding women continue to be a priority group for vaccination, yet there were gaps in data, evidence and information. Incorporating social listening and infodemic management into public health work is important.So What?: Information voids, when identified early, can help inform public health messaging. Increasing social listening and infodemic training and skills in the health promotion workforce can help build future capacity and give insight into optimising public health communication and addressing misinformation.

Determine the feasibility of a gamified therapy (occupational therapy, physiotherapy, speech pathology) prescription app developed for children with neurodisability for delivering school and home therapy programs (the Zingo app). A mixed-methods feasibility study was conducted with children (and their parents, therapists, and teachers) with neurodisability (n = 8, female= 5) who were prescribed a 4-week individualized therapy program by their usual treating therapist using Zingo. Primary outcome measures were program adherence, engagement, app quality, and user experience, collected with quantitative and qualitative methods. Mean adherence to the program was 58.0% (SD 27.2). Our combined Engagement Index (EI) score was 74.4% (SD 11.7). App quality measured using Mobile Application Rating Scale- User version was 4.6/5 (SD 0.7, n = 6) for parents, 4.6/5 (SD 0.5, n = 5) for teachers, and 4.4/5 (SD 0.6, n = 6) for therapists. Thematic analysis of semi-structured interviews yielded a primary theme of "app as motivator" for therapy. Adherence findings were affected by COVID-19 outbreak however remain comparable with other studies in this cohort. EI findings compared favorably with other studies. The findings are supportive of the feasibility of Zingo for delivering home and school therapy programs for children with neurodisability and was found to motivate therapy program completion.

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. The World Health Organization Early Artificial Intelligence–Supported Response with Social Listening (EARS) platform was developed to overcome some of these challenges. Objective: This paper describes the development of the EARS platform, including data sourcing, development, and validation of a machine learning categorization approach, as well as the results from the pilot study. Methods: Data for EARS are collected daily from web-based conversations in publicly available sources in 9 languages. Public health and social media experts developed a taxonomy to categorize COVID-19 narratives into 5 relevant main categories and 41 subcategories. We developed a semisupervised machine learning algorithm to categorize social media posts into categories and various filters. To validate the results obtained by the machine learning–based approach, we compared it to a search-filter approach, applying Boolean queries with the same amount of information and measured the recall and precision. Hotelling T2 was used to determine the effect of the classification method on the combined variables. Results: The EARS platform was developed, validated, and applied to characterize conversations regarding COVID-19 since December 2020. A total of 215,469,045 social posts were collected for processing from December 2020 to February 2022. The machine learning algorithm outperformed the Boolean search filters method for precision and recall in both English and Spanish languages (P<.001). Demographic and other filters provided useful insights on data, and the gender split of users in the platform was largely consistent with population-level data on social media use. Conclusions: The EARS platform was developed to address the changing needs of public health analysts during the COVID-19 pandemic. The application of public health taxonomy and artificial intelligence technology to a user-friendly social listening platform, accessible directly by analysts, is a significant step in better enabling understanding of global narratives. The platform was designed for scalability; iterations and new countries and languages have been added. This research has shown that a machine learning approach is more accurate than using only keywords and has the benefit of categorizing and understanding large amounts of digital social data during an infodemic. Further technical developments are needed and planned for continuous improvements, to meet the challenges in the generation of infodemic insights from social media for infodemic managers and public health professionals.

Issue addressed: The COVID‐19 pandemic has seen evidence and advice evolve quickly. Since the start of the pandemic there has been confusion and concern about breastfeeding and COVID‐19, and advice for this group has at times been contradictory. The volume of information on social media has exacerbated this. This study aimed to understand breastfeeding‐related COVID‐19 information sharing on social media during the global and Australian vaccine roll‐out.Methods: The CrowdTangle platform was used to source data from December 2020 to December 2021. Posts were categorised to intent and source and mapped to a timeline of pandemic‐related events and announcements. Descriptive analysis was used to understand data distribution patterns and qualitative analysis for post‐intent.Results: A total of 945 posts were included. Post‐interactions ranged from 0 to 6500. Vaccine‐related posts were the highest in number and increased over time. Non‐profit organisations shared the highest number of posts (n = 241), but interactions were highest with personal and government accounts. Peaks in posts and interactions mapped to key pandemic‐related announcements and events.Conclusion: These results describe the breastfeeding and COVID‐19 related content shared on Facebook over 13 months, and the associated interactions. Breastfeeding is an important public health issue and breastfeeding women have experienced conflicting and confusing breastfeeding‐related information during the COVID‐19 pandemic. Better understanding of social media usage, and the monitoring of changes in usage, as an emergency unfolds, can help target communications. This article adds to the evidence in understanding user reactions to COVID‐19 related breastfeeding information on social media.So what?: Social listening is an important part of health communication and infodemic management. Understanding how users react to and engage with COVID‐19 related breastfeeding information on social media can help to understand how the general public perceives and responds to health advice and other information being shared.

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. The World Health Organization Early Artificial Intelligence–Supported Response with Social Listening (EARS) platform was developed to overcome some of these challenges. OBJECTIVE This paper describes the development of the EARS platform, including data sourcing, development, and validation of a machine learning categorization approach, as well as the results from the pilot study. METHODS Data for EARS are collected daily from web-based conversations in publicly available sources in 9 languages. Public health and social media experts developed a taxonomy to categorize COVID-19 narratives into 5 relevant main categories and 41 subcategories. We developed a semisupervised machine learning algorithm to categorize social media posts into categories and various filters. To validate the results obtained by the machine learning–based approach, we compared it to a search-filter approach, applying Boolean queries with the same amount of information and measured the recall and precision. Hotelling T2 was used to determine the effect of the classification method on the combined variables. RESULTS The EARS platform was developed, validated, and applied to characterize conversations regarding COVID-19 since December 2020. A total of 215,469,045 social posts were collected for processing from December 2020 to February 2022. The machine learning algorithm outperformed the Boolean search filters method for precision and recall in both English and Spanish languages (P<.001). Demographic and other filters provided useful insights on data, and the gender split of users in the platform was largely consistent with population-level data on social media use. CONCLUSIONS The EARS platform was developed to address the changing needs of public health analysts during the COVID-19 pandemic. The application of public health taxonomy and artificial intelligence technology to a user-friendly social listening platform, accessible directly by analysts, is a significant step in better enabling understanding of global narratives. The platform was designed for scalability; iterations and new countries and languages have been added. This research has shown that a machine learning approach is more accurate than using only keywords and has the benefit of categorizing and understanding large amounts of digital social data during an infodemic. Further technical developments are needed and planned for continuous improvements, to meet the challenges in the generation of infodemic insights from social media for infodemic managers and public health professionals.

Background: The support of her infant’s father is one of the most important factors influencing a mother’s breastfeeding success, and an increasing number of interventions are targeted towards fathers. Engaging fathers as agents to influence a maternal behavior is potentially problematic, yet few studies report on maternal experiences. Objective: This study aims to explore mothers’ perspectives of their partners’ use of Milk Man, a father-focused breastfeeding smartphone app, and the acceptability of this approach. Materials and methods: New mothers (N = 459) whose partners had access to the app completed a questionnaire at six weeks postpartum. These data were used to determine knowledge, use and perspectives of the app. A sentiment analysis was conducted on responses to an open-ended question seeking maternal perspectives of the app. Results: Just over a quarter of mothers (28%) had been shown something from the app, and 37% had discussed something from Milk Man with their partner. There were 162 open-ended responses related to mothers’ perspectives of the app. Relevant responses (n = 129) were coded to an overall sentiment node and then to a total of 23 child nodes (sub-nodes). Most comments were positive (94), with a smaller number either negative (25) or neutral (21). Negative comments related to the usability of the app and not its intent or content. Conclusion: Mothers found the father-focussed breastfeeding app to be acceptable. When designing interventions targeting one group to affect the behaviour of another, inclusion of measures to gain the perspectives of both should be seen as an imperative.

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. This can serve as a foundation for a systematic, evidence-based approach to monitoring, identifying, and mitigating future infodemic harms in emergency preparedness and prevention. Objective: In this paper, we summarize the Fifth World Health Organization (WHO) Infodemic Management Conference structure, proceedings, outcomes, and proposed actions seeking to identify the interdisciplinary approaches and frameworks needed to enable the measurement of the burden of infodemics. Methods: An iterative human-centered design (HCD) approach and concept mapping were used to facilitate focused discussions and allow for the generation of actionable outcomes and recommendations. The discussions included 86 participants representing diverse scientific disciplines and health authorities from 28 countries across all WHO regions, along with observers from civil society and global public health–implementing partners. A thematic map capturing the concepts matching the key contributing factors to the public health burden of infodemics was used throughout the conference to frame and contextualize discussions. Five key areas for immediate action were identified. Results: The 5 key areas for the development of metrics to assess the burden of infodemics and associated interventions included (1) developing standardized definitions and ensuring the adoption thereof; (2) improving the map of concepts influencing the burden of infodemics; (3) conducting a review of evidence, tools, and data sources; (4) setting up a technical working group; and (5) addressing immediate priorities for postpandemic recovery and resilience building. The summary report consolidated group input toward a common vocabulary with standardized terms, concepts, study designs, measures, and tools to estimate the burden of infodemics and the effectiveness of infodemic management interventions. Conclusions: Standardizing measurement is the basis for documenting the burden of infodemics on health systems and population health during emergencies. Investment is needed into the development of practical, affordable, evidence-based, and systematic methods that are legally and ethically balanced for monitoring infodemics; generating diagnostics, infodemic insights, and recommendations; and developing interventions, action-oriented guidance, policies, support options, mechanisms, and tools for infodemic managers and emergency program managers.

Background: The COVID-19 pandemic has had global impacts and caused some health systems to experience substantial pressure. The need for accurate health information has been felt widely. Chatbots have great potential to reach people with authoritative information, and a number of chatbots have been quickly developed to disseminate information about COVID-19. However, little is known about user experiences of and perspectives on these tools.Objective: This study aimed to describe what is known about the user experience and user uptake of COVID-19 chatbots.Methods: A scoping review was carried out in June 2021 using keywords to cover the literature concerning chatbots, user engagement, and COVID-19. The search strategy included databases covering health, communication, marketing, and the COVID-19 pandemic specifically, including MEDLINE Ovid, Embase, CINAHL, ACM Digital Library, Emerald, and EBSCO. Studies that assessed the design, marketing, and user features of COVID-19 chatbots or those that explored user perspectives and experience were included. We excluded papers that were not related to COVID-19; did not include any reporting on user perspectives, experience, or the general use of chatbot features or marketing; or where a version was not available in English. The authors independently screened results for inclusion, using both backward and forward citation checking of the included papers. A thematic analysis was carried out with the included papers.Results: A total of 517 papers were sourced from the literature, and 10 were included in the final review. Our scoping review identified a number of factors impacting adoption and engagement including content, trust, digital ability, and acceptability. The papers included discussions about chatbots developed for COVID-19 screening and general COVID-19 information, as well as studies investigating user perceptions and opinions on COVID-19 chatbots.Conclusions: The COVID-19 pandemic presented a unique and specific challenge for digital health interventions. Design and implementation were required at a rapid speed as digital health service adoption accelerated globally. Chatbots for COVID-19 have been developed quickly as the pandemic has challenged health systems. There is a need for more comprehensive and routine reporting of factors impacting adoption and engagement. This paper has shown both the potential of chatbots to reach users in an emergency and the need to better understand how users engage and what they want.