How Politics Affect Pandemic Forecasting: Spatio-Temporal Early Warning Capabilities of Different Geo-Social Media Topics in the Context of State-level Political Leaning

Dorian Arifi^1,2*Bernd Resch^1,3Mauricio Santillana^4,5Steffen Knoblauch^6,7,8Sven Lautenbach⁶Thomas Jaenisch^10,9Ivonne Morales¹⁰

• ¹Interdisciplinary Transformation University, Linz, Austria
• ²Geoinformatics Department - Z_GIS, University of Salzburg, Salzburg, Salzburg, Austria
• ³Center for Geographic Analysis, Harvard University, Cambridge, California, United States
• ⁴Machine Intelligence Group for the Betterment of Health and the Environment, Northeastern, Boston, United States
• ⁵Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, United States
• ⁶Klaus Tschira Stiftung, Heidelberg Institute for Geoinformation Technology, Heidelberg, Baden-Württemberg, Germany
• ⁷Heidelberg University, GIScience Chair, Heidelberg, Germany
• ⁸Heidelberg University, Interdisciplinary Centre of Scientific Computing, Heidelberg, Germany
• ⁹University of Colorado Boulder, Colorado School of Public Health, Aurora, United States
• ¹⁰Heidelberg University Hospital, Department of Infectious Diseases, Heidelberg, Germany

Due to political polarization, adherence to public health measures varied across US states during the COVID-19 pandemic. Although social media posts have been shown effective in anticipating COVID-19 surges, the impact of political leaning on the effectiveness of different topics for early warning remains mostly unexplored. Our study examines the spatio-temporal early warning potential of different geo-social media topics across republican, democrat, and swing states.Using keyword filtering, we identified 8 COVID-19-related geo-social media topics. We then utilized Chatterjee's rank correlation to assess their early warning capability for COVID-19 cases 7 to 42 days in advance across six infection waves. A mixed-effect model was used to evaluate the impact of timeframe and political leaning on the early warning capabilities of these topics.Many topics exhibited significant spatial clustering over time, with quarantine and vaccination-related posts occurring in opposing spatial regimes in the second timeframe. We also found significant variation in the early warning capabilities of geo-social media topics over time and across political clusters. In detail, quarantine related geo-social media post were significantly less correlated to COVID-19 cases in republican states than in democrat states. Further, preventive measure and quarantine-related posts exhibited declining correlations to COVID-19 cases over time, while the correlations of vaccine and virus-related posts with COVID-19 infections.Our results highlight the need for a dynamic spatially targeted approach that accounts for both how regional geosocial media topics of interest change over time and the impact of local political ideology on their epidemiological early warning capabilities.