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MIDAS Webinar: Characterizing Public Opinions Towards COVID-19 for Infoveillance

The MIDAS Webinar Series features research by MIDAS members, and is open to the public. 

Date: Friday, July 29th, 2022
Time: 12:00pm – 1:00pm Eastern (USA)

Register here: Zoom link

Topic: Characterizing Public Opinions Towards COVID-19 for Infoveillance

Speaker: Dr. Shi Chen, University of North Carolina Charlotte

Abstract: Infosurveillance/infoveillance substantially complements our current case-based, genomic, and serosurveillance systems for health emergencies such as the current COVID-19 pandemic. Large volumes of social media data provide almost instantaneous and accurate depiction of public discourse of COVID-19, especially public perceptions of the risk of the pandemic, sentiments towards various interventions (e.g., non-pharmaceutical interventions NPIs and vaccinations), and other intermingled societal issues. These insights are critical to infer potential behavioral changes during the pandemic and predict the epidemic dynamics. In this study, we demonstrate several different approaches in analyzing unstructured textual data and characterizing dynamic public opinions during the course of COVID-19, including regular expression (regex), latent Dirichlet allocation (LDA), and data-driven deep learning based bidirectional encoder representation transformers (BERT). We comprehensively sample COVID-19 related discussions on Twitter since early 2020. We show that BERT has the best performance in identifying real-time public opinions of various aspects of COVID-19. This study highlighted the importance of effective infosurveillance from large social media data during health emergencies for public health decision makers.


Bio
: Dr. Shi Chen’s research interest include how individual, spatial, and temporal heterogeneity determine both environmentally-transmitted and directly-transmitted pathogen transmission dynamics. He also explores how the interaction among these sources of heterogeneity influence the pathogen transmission; multiple transmission pathways (e.g. airborne, waterborne, direct contact-based), and how environmental factors (e.g., temperature, photoperiod, etc) influence insect life history, population dynamics, and vector-borne disease dynamics.

 

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