Melotte, S.; Kejriwal, M. A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas over a 255-Day Period. Data2021, 6, 64.
Melotte, S.; Kejriwal, M. A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas over a 255-Day Period. Data 2021, 6, 64.
Melotte, S.; Kejriwal, M. A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas over a 255-Day Period. Data2021, 6, 64.
Melotte, S.; Kejriwal, M. A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas over a 255-Day Period. Data 2021, 6, 64.
Abstract
One of the unfortunate findings from the ongoing COVID-19 crisis is the disproportionate impact the crisis has had on people and communities who were already socioeconomically disadvantaged. It has, however, been difficult to study this issue at scale and in greater detail using social media platforms like Twitter. Several COVID-19 Twitter datasets have been released, but they have very broad scope, both topically and geographically. In this paper, we present a more controlled and compact dataset that can be used to answer a range of potential research questions (especially pertaining to computational social science) without requiring extensive preprocessing or tweet-hydration from the earlier datasets. The proposed dataset comprises tens of thousands of geotagged (and in many cases, reverse-geocoded) tweets originally collected over a 255-day period in 2020 over 10 metropolitan areas in North America. Since there are socioeconomic disparities within these cities (sometimes to an extreme extent, as witnessed in `inner city neighborhoods’ in some of these cities), the dataset can be used to assess such socioeconomic disparities from a social media lens, in addition to comparing and contrasting behavior across cities.
Keywords
COVID-19; Twitter; Geo-Tagged; Metropolitan; Computational Social Science
Subject
Social Sciences, Media studies
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
