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Our analysis on more than 26 million coronavirus disease 2019 (COVID-19) tweets shows that large-scale surveillance of public discourse is feasible with computationally lightweight classifiers by out-of-the-box utilization of these representations.<\/jats:p>","DOI":"10.3390\/make2040032","type":"journal-article","created":{"date-parts":[[2020,11,29]],"date-time":"2020-11-29T21:00:57Z","timestamp":1606683657000},"page":"603-616","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Large-Scale, Language-Agnostic Discourse Classification of Tweets During COVID-19"],"prefix":"10.3390","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3581-2231","authenticated-orcid":false,"given":"Oguzhan","family":"Gencoglu","sequence":"first","affiliation":[{"name":"Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,29]]},"reference":[{"key":"ref_1","first-page":"157","article-title":"WHO declares COVID-19 a pandemic","volume":"91","author":"Cucinotta","year":"2020","journal-title":"Acta-Bio-Med. 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