The Problem Early in the COVID-19 pandemic, an “infodemic” of conflicting information spread across social media. Health professionals, trusted for accurate medical information, posted COVID-19 tweets reflecting personal politics, values and emotions like fear and anger. Such messaging risks politicizing science, increasing public confusion, eroding trust in experts and fueling misinformation. The Approach To address these challenges, Dr. Lee applied a multi-method computational analysis of COVID19 tweets from self-identified health professionals on social media: Collected and analyzed over 7 million tweets, isolating 41,000 from about 9,600 health professionals in early 2020. Used text, social network and sentiment analysis to identify key topics: political clusters and emotions. Explored how negative sentiments and political views in professionals’ tweets might influence public trust, understanding and behavior during the pandemic. Understanding Early Pandemic Health Communication “My goal is to improve how we communicate health information by using computational modeling and big data to promote clear, trusted and inclusive public dialogue.” Hsuan-Wei “Wayne” Lee, PhD Short Term Impact Showed early pandemic expert tweets were influenced by political polarization, highlighting misinformation risks. Showed real-time sentiment and network analysis can track expert communication. Longer Term Impact Promote ethical social media use by encouraging clear, accurate health communication. Advance data-driven strategies for timely outbreak and “infodemic” management. Societal Impact Funding for this research will support innovative use of real-time sentiment and network analysis to leverage social media for health communication. It aims to advance data-driven “infodemic” management, ensuring timely delivery of accurate health information. This work offers societal benefits in the following areas: For more information visit https://health.lehigh.edu/research-partners or email INRSRCH@lehigh.edu 19 Policy: Shapes guidelines to ensure public health messages on social media are accurate and unbiased. Data Driven Innovation: Harnesses advanced analytical methods to produce transparent insights. Community/Culture: Delivering clearer health information, boosting overall health equity and trust.
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