How the world's collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter

• Published in: PloS one Vol. 16; no. 1; p. e0244476
• Main Authors: Alshaabi, Thayer, Arnold, Michael V, Minot, Joshua R, Adams, Jane Lydia, Dewhurst, David Rushing, Reagan, Andrew J, Muhamad, Roby, Danforth, Christopher M, Dodds, Peter Sheridan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 06-01-2021; Public Library of Science (PLoS)
• Subjects: Antibodies; Attention; Biology and Life Sciences; Computer and Information Sciences; Coronavirus Infections - etiology; Coronavirus Infections - psychology; Coronaviruses; COVID-19; COVID-19 - etiology; COVID-19 - psychology; Digital media; Disease transmission; Epidemics; Humans; Language; Languages; Media coverage; Medicine and Health Sciences; Pandemics; Pandemics - statistics & numerical data; People and places; Public opinion; Retrospective Studies; SARS-CoV-2 - pathogenicity; Serological tests; Social Media - trends; Social networks; Social Sciences; Socio-economic aspects; Time series; Time-series analysis; Turbulent flow; United States; Vaccines; Viral diseases; Viruses; United States; Vermont; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0244476

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.