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• W3015331567 abstract "Using a random 10% sample of tweets authored from 2019-09-01 through 2020-04-30, 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 22.49 $pm$ 3.26 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." @default.
• W3015331567 created "2020-04-17" @default.
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• W3015331567 date "2020-04-07" @default.
• W3015331567 modified "2023-09-27" @default.
• W3015331567 title "Divergent modes of online collective attention to the COVID-19 pandemic are associated with future caseload variance" @default.
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• W3015331567 hasPublicationYear "2020" @default.
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