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Divergent modes of online collective attention to the COVID-19 pandemic are associated with future caseload variance

D. R. Dewhurst, T. Alshaabi, M. Arnold, J. R. Minot, C. M. Danforth, and P. S. Dodds

Times cited: 15

Abstract:

Using a random 10\% sample of tweets authored from 2019-09-01 through 2020-03-25, we analyze the dynamic behavior of words (1-grams) used on Twitter to describe the ongoing COVID-19 pandemic. Across 24 languages, we find two distinct dynamic regimes: One characterizing the rise and subsequent collapse in collective attention to the initial Coronavirus outbreak in late January, and a second that represents March COVID-19-related discourse. Aggregating countries by dominant language use, we find that volatility in the first dynamic regime is associated with future volatility in new cases of COVID-19 roughly three weeks (average 22.7 $\pm$ 2.17 days) later. Our results suggest that surveillance of change in usage of epidemiology-related words on social media may be useful in forecasting later change in disease case numbers, but we emphasize that our current findings are not causal or necessarily predictive.
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BibTeX:

@Misc{dewhurst2020a,
  author =	 {Dewhurst, David Rushing and Alshaabi, Thayer and
                  Arnold, Michael V. and Minot, Joshua R. and
                  Danforth, Christopher M. and Dodds, Peter Sheridan},
  title =	 {Divergent modes of online collective attention to
                  the {C}{O}{V}{I}{D}-19 pandemic are associated with future
                  caseload variance},
  year =	 {2020},
  note =	 {Available online at
                  \href{https://arxiv.org/abs/2004.03516}{https://arxiv.org/abs/2004.03516}},
}
 
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