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In confronting the global spread of the coronavirus disease COVID-19 pandemicwe must have coordinated medical, operational, and political responses. In allefforts, data is crucial. Fundamentally, and in the possible absence of avaccine for 12 to 18 months, we need universal, well-documented testing forboth the presence of the disease as well as confirmed recovery throughserological tests for antibodies, and we need to track major socioeconomicindices. But we also need auxiliary data of all kinds, including data relatedto how populations are talking about the unfolding pandemic through news andstories. To in part help on the social media side, we curate a set of 2000day-scale time series of 1- and 2-grams across 24 languages on Twitter that aremost 'important' for April 2020 with respect to April 2019. We determineimportance through our allotaxonometric instrument, rank-turbulence divergence.We make some basic observations about some of the time series, including acomparison to numbers of confirmed deaths due to COVID-19 over time. We broadlyobserve 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 throughMarch and April. The world's collective attention dropped away while the virusspread out from China. We host the time series on Gitlab, updating them on adaily basis while relevant. Our main intent is for other researchers to usethese time series to enhance whatever analyses that may be of use during thepandemic as well as for retrospective investigations.

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