A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research-An International Collaboration

Overview

Journal Epidemiologia (Basel)

Publisher MDPI

Specialty Public Health

Date 2022 Nov 23

PMID 36417228

Authors

Juan M Banda

Ramya Tekumalla

Guanyu Wang

Jingyuan Yu

Tuo Liu

Yuning Ding

Ekaterina Artemova

Elena Tutubalina

Gerardo Chowell

Affiliations

Soon will be listed here.

Abstract

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

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