Abstract
BackgroundCOVID-19 pandemic was accompanied by an Infodemic (overabundance of information, including misinformation and disinformation, both online and offline); in response to this Infodemic, WHO launched the EARS platform (Early AI-assisted Response with Social Listening), showing real-time information about how people are talking about COVID-19 online. This information is intended to serve health information professionals to understand narratives and needs of the general public, in order to inform policy or communications decisions.MethodsData is collected daily from online conversations in publicly available sources, including Twitter, online forums, and blogs in English, French, Spanish and Portuguese, for 20 pilot countries. Once the data is collected, it is processed and classified into 39 categories, according to a set of pandemic public health taxonomy. The classification is made based on semi-supervised machine learning.ResultsTop 5 categories across regions are Covid-19 vaccine, Transmission settings, Personal measures, Testing and Industry (industry refers to the impact of the pandemic on the economy). We find that conversations around Covid-19 vaccines usually rank in the second or third position in all regions and represent 9%-12% of the conversation.ConclusionsThe configuration and application of the EARS platform has enabled progress towards more scalable and sustainable social listening to inform Infodemic management and response, compared to previous methods which were more manual, required data scientists in the team, or had fewer analytics capabilities. Future work will focus on gradually adding more data sources which can expand coverage and representativity.Key messages Discuss social listening methods for greater accountability to affected populations.Formulate insights into how digital media and information technology can be better utilized for more rapidly responding to the evolving needs of communities.
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