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?:abstract
  • As of July 17, 2020, more than thirteen million people have been diagnosed with the Novel Coronavirus (COVID-19), and half a million people have already lost their lives due to this infectious disease The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020 Since then, social media platforms have experienced an exponential rise in the content related to the pandemic In the past, Twitter data have been observed to be indispensable in the extraction of situational awareness information relating to any crisis This paper presents COV19Tweets Dataset (Lamsal 2020a), a large-scale Twitter dataset with more than 310 million COVID-19 specific English language tweets and their sentiment scores The dataset’s geo version, the GeoCOV19Tweets Dataset (Lamsal 2020b), is also presented The paper discusses the datasets’ design in detail, and the tweets in both the datasets are analyzed The datasets are released publicly, anticipating that they would contribute to a better understanding of spatial and temporal dimensions of the public discourse related to the ongoing pandemic As per the stats, the datasets (Lamsal 2020a, 2020b) have been accessed over 74 5k times, collectively © 2020, Springer Science+Business Media, LLC, part of Springer Nature
is ?:annotates of
?:creator
?:journal
  • Applied_Intelligence
?:license
  • unk
?:publication_isRelatedTo_Disease
?:source
  • WHO
?:title
  • Design and analysis of a large-scale COVID-19 tweets dataset
?:type
?:who_covidence_id
  • #935302
?:year
  • 2020

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