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. 2025 Jul 1:13:1618347.
doi: 10.3389/fpubh.2025.1618347. eCollection 2025.

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   3 , Mauricio Santillana  4   5 , Steffen Knoblauch  6   7   8 , Sven Lautenbach  6 , Thomas Jaenisch  9   10 , Ivonne Morales  10
Affiliations

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 et al. Front Public Health. .

Abstract

Objectives: 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.

Methods: Using keyword filtering, we identified eight 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.

Results: 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.

Conclusion: 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.

Keywords: epidemiological early warning; geo-social media; political polarization; spatio-temporal epidemiology; spatio-temporal semantic analysis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
US state based political clusters. Please find a table with state abbreviations and the corresponding full state names in Table 3 in the Supplementary Appendix.
Figure 2
Figure 2
Timeframes capturing different waves of COVID-19 cases based on local minima.
Figure 3
Figure 3
Chatterjee's rank correlation for each geo-social media topic for mainland US states in timeframe 2. Please find a table with state abbreviations and the corresponding full state names in Table 3 in the Supplementary Appendix.
Figure 4
Figure 4
Local spatial autocorrelation over Chatterjee's rank correlation for each geo-social media topic for mainland US states in timeframe 2. Please find a table with state abbreviations and the corresponding full state names in Table 3 in the Supplementary Appendix.
Figure 5
Figure 5
Chatterjee's rank correlation for each geo-social media topic per political cluster and per timeframe.
See this image and copyright information in PMC

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