COVID-19 Pandemic Attention Dynamics Within State Level Twitter Discourse}
Conference Year
January 2022
Abstract
The COVID-19 pandemic has captured collective attention on an unprecedented scale in the era of social media. New data streams have been created to guide public health inventions, from traditional testing and hospitalization data to new creative sources like wastewater surveillance. Social media data can contribute as well, providing an alternative to traditional polling methods with real-time feedback. We aggregate tweets by state to quantify the dynamics of attention around vaccination and masking from 2020 to present. We measure the associations between public attention and pandemic outcomes like vaccination rates, hospitalizations and deaths, while controlling for demographic variables.
Primary Faculty Mentor Name
Chris Danforth
Secondary Mentor Name
Peter Dodds
Student Collaborators
Amelia Tarren
Status
Graduate
Student College
College of Arts and Sciences
Second Student College
College of Engineering and Mathematical Sciences
Program/Major
Complex Systems
Primary Research Category
Social Sciences
Secondary Research Category
Engineering & Physical Sciences
Tertiary Research Category
Health Sciences
COVID-19 Pandemic Attention Dynamics Within State Level Twitter Discourse}
The COVID-19 pandemic has captured collective attention on an unprecedented scale in the era of social media. New data streams have been created to guide public health inventions, from traditional testing and hospitalization data to new creative sources like wastewater surveillance. Social media data can contribute as well, providing an alternative to traditional polling methods with real-time feedback. We aggregate tweets by state to quantify the dynamics of attention around vaccination and masking from 2020 to present. We measure the associations between public attention and pandemic outcomes like vaccination rates, hospitalizations and deaths, while controlling for demographic variables.