Design and analysis of a large-scale COVID-19 tweets dataset

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.

Keywords: Crisis computing; Network analysis; Sentiment analysis; Social computing; Twitter data.

Fig. 1

Daily distribution of tweets in the COV19Tweets Dataset

Fig. 2

Resource utilization graphs for the VM (24 hours)

Fig. 3

Daily distribution of tweets in the GeoCOV19Tweets Dataset

Fig. 4

COVID-19 sentiment trend, since April 24, 2020 to July 17, 2020

Fig. 5

Network Analysis: Overview of the GeoCOV19Tweets Dataset

Fig. 6

Country specific outlier hashtags detected using Network Analysis

Fig. 7

Network diagram in Fig. 5 expanded by a scale factor

Fig. 8

World view of COVID-19 Sentiment

Fig. 9

Region-specific view of COVID-19 Sentiment (color scale for this figure is same as of Fig. 8)