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 large-scale,
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.

该文介绍了 GeoCoV19—— 一个包含 524 百万条推文的大规模 Twitter 数据集，通过基于地名词典的方法来推断推文的地理位置，而这一大规模、多语言、地理定位的社交媒体数据可以帮助研究社区评估社会如何共同应对这一前所未有的全球危机，以及建立计算方法来应对如识别假新闻，理解社区的知识差距，建立疾病预测和监测模型等挑战。