{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T07:33:13Z","timestamp":1790839993788,"version":"4.1.0"},"reference-count":17,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T00:00:00Z","timestamp":1591315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGSPATIAL Special"],"published-print":{"date-parts":[[2020,6,5]]},"abstract":"<jats:p>The past several years have witnessed a huge surge in the use of social media platforms during mass convergence events such as health emergencies, natural or human-induced disasters. These non-traditional data sources are becoming vital for disease forecasts and surveillance when preparing for epidemic and pandemic outbreaks. In this paper, we present GeoCoV19, a large-scale Twitter dataset containing more than 524 million multilingual tweets posted over a period of 90 days since February 1, 2020. Moreover, we employ a gazetteer-based approach to infer the geolocation of tweets. We postulate that this largescale, multilingual, geolocated social media data can empower the research communities to evaluate how societies are collectively coping with this unprecedented global crisis as well as to develop computational methods to address challenges such as identifying fake news, understanding communities' knowledge gaps, building disease forecast and surveillance models, among others.<\/jats:p>","DOI":"10.1145\/3404820.3404823","type":"journal-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:03:47Z","timestamp":1594209827000},"page":"6-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":167,"title":["GeoCoV19"],"prefix":"10.1145","volume":"12","author":[{"given":"Umair","family":"Qazi","sequence":"first","affiliation":[{"name":"Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Imran","sequence":"additional","affiliation":[{"name":"Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ferda","family":"Ofli","sequence":"additional","affiliation":[{"name":"Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,7,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.2196\/19016"},{"key":"e_1_2_1_2_1","volume-title":"Large Arabic Twitter Dataset on COVID-19. arXiv preprint arXiv:2004.04315","author":"Alqurashi S.","year":"2020"},{"key":"e_1_2_1_3_1","volume-title":"A large-scale COVID-19 Twitter chatter dataset for open scientific research---an international collaboration. arXiv preprint arXiv:2004.03688","author":"Banda J. 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