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.
Highlights
Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Suite 1001, 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 been difficult to study this issue in greater detail using social media sources like Twitter
What is missing is a carefully controlled dataset that would enable computational social scientists in specific contexts to study the issue from a social media lens without much hassle
Summary
In addition to its medical consequences, the ongoing COVID-19 crisis has revealed (if not exacerbated) deep inequalities in our society [1,2,3,4]. GeoCOV19Tweets dataset, originally obtained by filtering English tweets from the Twitter streaming API by using a continuously updated, expansive list of keywords and hashtags [7]. Our primary goal in publishing this dataset is to enable social scientists and digital humanities scholars with a less technical background to study COVID-19 in metropolitan contexts, over a longitudinal period, through a social media lens. For this reason, our dataset is compact and places a high premium on accurate geotagging, the details of which are described subsequently
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