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.

截至 2020 年 7 月 17 日，已有超过 1300 万人被诊断出感染了新型冠状病毒 (COVID-19)，已有 50 万人因这种传染病丧生。世界卫生组织于 2020 年 3 月 11 日宣布 COVID-19 爆发为大流行病。从那时起，社交媒体平台与大流行病相关的内容呈指数级增长。过去，人们观察到 Twitter 数据对于提取与任何危机相关的态势感知信息是必不可少的。本文介绍了COV19Tweets 数据集(Lamsal 2020a)，这是一个大型 Twitter 数据集，包含超过 3.1 亿条 COVID-19 特定英语推文及其情绪评分。数据集的地理版本，GeoCOV19Tweets 数据集（Lamsal 2020b），还介绍了。本文详细讨论了数据集的设计，并对两个数据集中的推文进行了分析。这些数据集是公开发布的，预计它们将有助于更好地理解与当前流行病相关的公共话语的空间和时间维度。根据统计数据，数据集（Lamsal 2020a、2020b）总共被访问了超过 74.5k 次。