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. 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.

[1]  V. Balakrishnan,et al.  Infodemic and fake news – A comprehensive overview of its global magnitude during the COVID-19 pandemic in 2021: A scoping review , 2022, International Journal of Disaster Risk Reduction.

[2]  S. Rubinelli,et al.  WHO competency framework for health authorities and institutions to manage infodemics: its development and features , 2022, Human Resources for Health.

[3]  M. Ries The COVID-19 Infodemic: Mechanism, Impact, and Counter-Measures—A Review of Reviews , 2022, Sustainability.

[4]  Saji K. Mathew,et al.  The disaster of misinformation: a review of research in social media , 2022, International Journal of Data Science and Analytics.

[5]  N.C.M. Theunissen,et al.  The importance of social media users’ responses in tackling digital COVID-19 misinformation in Africa , 2022, Digital health.

[6]  Matthew R. DeVerna,et al.  Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal , 2021, Scientific Reports.

[7]  Rosamund F. Lewis,et al.  Infodemics: A new challenge for public health , 2021, Cell.

[8]  Bulent Dilmac,et al.  Do we experience pandemic fatigue? current state, predictors, and prevention , 2021, Current Psychology.

[9]  B. Gellin,et al.  Social media strategies to affect vaccine acceptance: a systematic literature review , 2021, Expert review of vaccines.

[10]  Kulkarni,et al.  A Public Health Research Agenda for Managing Infodemics: Methods and Results of the First WHO Infodemiology Conference , 2021, JMIR infodemiology.

[11]  F. El-Jardali,et al.  Application of the eHealth Literacy Model in Digital Health Interventions: Scoping Review , 2020, Journal of medical Internet research.

[12]  H. Larson,et al.  Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA , 2021, Nature Human Behaviour.

[13]  M. De Domenico,et al.  Studying the COVID-19 infodemic at scale , 2021, Big Data Soc..

[14]  K. Mandl,et al.  Limited Role of Bots in Spreading Vaccine-Critical Information Among Active Twitter Users in the United States: 2017-2019. , 2020, American Journal of Public Health.

[15]  Wenting Yang,et al.  The Prediction of Infectious Diseases: A Bibliometric Analysis , 2020, International journal of environmental research and public health.

[16]  Deborah Bunker,et al.  Who do you trust? The digital destruction of shared situational awareness and the COVID-19 infodemic , 2020, International Journal of Information Management.

[17]  Elizabeth L. Petrun Sayers,et al.  Communication missteps during COVID‐19 hurt those already most at risk , 2020, Journal of Contingencies and Crisis Management.

[18]  M. De Domenico,et al.  Framework for Managing the COVID-19 Infodemic: Methods and Results of an Online, Crowdsourced WHO Technical Consultation , 2020, Journal of medical Internet research.

[19]  C. Betsch,et al.  Monitoring behavioural insights related to COVID-19 , 2020, The Lancet.

[20]  P. Dickmann,et al.  Earlier detection of public health risks – Health policy lessons for better compliance with the International Health Regulations (IHR 2005): Insights from low-, mid- and high-income countries , 2019, Health Policy.

[21]  Christoph Lutz,et al.  Digital inequalities in the age of artificial intelligence and big data , 2019, Human Behavior and Emerging Technologies.

[22]  William M. K. Trochim,et al.  Introduction to a special issue on concept mapping. , 2017, Evaluation and program planning.

[23]  Chris Del Mar,et al.  Clinicians’ Expectations of the Benefits and Harms of Treatments, Screening, and Tests: A Systematic Review , 2017, JAMA internal medicine.

[24]  H. V. van Oers,et al.  Concept mapping as a method to enhance evidence-based public health. , 2017, Evaluation and program planning.

[25]  G. Gigerenzer,et al.  Overcoming the knowledge-behavior gap: The effect of evidence-based HPV vaccination leaflets on understanding, intention, and actual vaccination decision. , 2014, Vaccine.