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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/2264| Divergent modes of online collective attention to the COVID-19 pandemic are associated with future caseload variance | |
| David Rushing Dewhurst. Thayer Alshaabi. Michael V. Arnold. Joshua R. Minot. Christopher M. Danforth. Peter Sheridan Dodds. | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://arxiv.org/pdf/2004.03516v1.pdf | |
| 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 | |
| arxiv.org | |
| 2020 | |
| Artículo | |
| https://arxiv.org/pdf/2004.03516v1.pdf | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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| 1101370.pdf | 2.48 MB | Adobe PDF | Visualizar/Abrir |