Listening to community concerns in the COVID-19 infodemic: A WHO digital approach

Abstract

Abstract Background The Infodemic (too much information including false or misleading information in digital and physical environments) during the COVID-19 pandemic has led to confusion, risk-taking and behaviors that can amplify outbreaks, and reduce effectiveness of pandemic response efforts. To address this challenge, the WHO Information Network for Epidemics (EPI-WIN), in collaboration with research partners, developed a public health Infodemic intelligence analysis methodology for weekly analysis of digital media data to identify, categorize, and understand key concerns expressed in online conversations. Methods Thirty-five keyword-based searches (per language) using Meltwater Explore and Google Trends were created and grouped according to a set of pandemic public health taxonomy categories developed specifically for this analysis. The taxonomy has five thematic categories of conversation about COVID-19 and public health response: (1) the cause of the illness, (2) the illness, (3) the treatment, (4) the interventions and (5) Information. Results The two most recurring topics to attract increasing interest were Vaccines and Asymptomatic transmission followed by Immunity, Cause of the virus, Vulnerable communities and Reduction of movement, and Risk factors based on demographics and risk of misinformation. Conclusions The application of this taxonomy to online social listening week-on-week resulted in a better in-time understanding of the evolution and dynamics of high velocity conversations about COVID-19 globally during the pandemic and proposes a quantifiable approach to support planning of risk communication response. Key messages Describe widespread innovation in social listening methods for greater accountability to affected populations. Formulate insights into how digital media can be better utilized for more rapidly responding to the evolving needs of communities.