How the world's collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter
| Autor: | Andrew J. Reagan, Jane Lydia Adams, Roby Muhamad, Thayer Alshaabi, Peter Sheridan Dodds, Michael Vincent Arnold, David Rushing Dewhurst, Joshua R. Minot, Christopher M. Danforth |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Viral Diseases Epidemiology Social Sciences 02 engineering and technology Geographical locations Medical Conditions 0302 clinical medicine Sociology Pandemic Medicine and Health Sciences 0202 electrical engineering electronic engineering information engineering Psychology Attention 030212 general & internal medicine Virus Testing Data Management Language Multidisciplinary Social Communication Computer Science - Social and Information Networks Public relations Infectious Diseases Social Networks Medicine 020201 artificial intelligence & image processing Coronavirus Infections Network Analysis Brazil Research Article Computer and Information Sciences Physics - Physics and Society Science FOS: Physical sciences Physics and Society (physics.soc-ph) 03 medical and health sciences Politics Diagnostic Medicine Humans Social media China Set (psychology) Pandemics Socioeconomic status Retrospective Studies Social and Information Networks (cs.SI) Series (stratigraphy) Divergence (linguistics) SARS-CoV-2 business.industry Data Visualization Biology and Life Sciences COVID-19 Covid 19 South America Communications Collective Human Behavior People and places business Social Media |
| Zdroj: | PLoS ONE PLoS ONE, Vol 16, Iss 1, p e0244476 (2021) |
| Popis: | In confronting the global spread of the coronavirus disease COVID-19 pandemic we must have coordinated medical, operational, and political responses. In all efforts, data is crucial. Fundamentally, and in the possible absence of a vaccine for 12 to 18 months, we need universal, well-documented testing for both the presence of the disease as well as confirmed recovery through serological tests for antibodies, and we need to track major socioeconomic indices. But we also need auxiliary data of all kinds, including data related to how populations are talking about the unfolding pandemic through news and stories. To in part help on the social media side, we curate a set of 2000 day-scale time series of 1- and 2-grams across 24 languages on Twitter that are most 'important' for April 2020 with respect to April 2019. We determine importance through our allotaxonometric instrument, rank-turbulence divergence. We make some basic observations about some of the time series, including a comparison to numbers of confirmed deaths due to COVID-19 over time. We broadly observe across all languages a peak for the language-specific word for 'virus' in January 2020 followed by a decline through February and then a surge through March and April. The world's collective attention dropped away while the virus spread out from China. We host the time series on Gitlab, updating them on a daily basis while relevant. Our main intent is for other researchers to use these time series to enhance whatever analyses that may be of use during the pandemic as well as for retrospective investigations. 13 pages, 6 figures, 3 tables, website: http://compstorylab.org/covid19ngrams/ |
| Databáze: | OpenAIRE |
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