{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:04:58Z","timestamp":1760234698976,"version":"build-2065373602"},"reference-count":15,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T00:00:00Z","timestamp":1623801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>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 \u2018inner city neighborhoods\u2019 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.<\/jats:p>","DOI":"10.3390\/data6060064","type":"journal-article","created":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T10:27:07Z","timestamp":1623839227000},"page":"64","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas over a 255-Day Period"],"prefix":"10.3390","volume":"6","author":[{"given":"Sara","family":"Melotte","sequence":"first","affiliation":[{"name":"Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA 90292, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5988-8305","authenticated-orcid":false,"given":"Mayank","family":"Kejriwal","sequence":"additional","affiliation":[{"name":"Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA 90292, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"917","DOI":"10.2105\/AJPH.2020.305766","article-title":"We\u2019re not all in this together: on COVID-19, intersectionality, and structural inequality","volume":"110","author":"Bowleg","year":"2020","journal-title":"Am. J. Public Health"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.puhe.2020.05.006","article-title":"Poverty, inequality and COVID-19: The forgotten vulnerable","volume":"183","author":"Patel","year":"2020","journal-title":"Public Health"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1111\/1475-5890.12232","article-title":"COVID-19 and Inequalities","volume":"41","author":"Blundell","year":"2020","journal-title":"Fisc. Stud."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1007\/s40615-020-00833-4","article-title":"Racial, economic, and health inequality and COVID-19 infection in the United States","volume":"8","author":"Abedi","year":"2020","journal-title":"J. Racial Ethn. Health Disparities"},{"key":"ref_5","unstructured":"Gruzd, A., and Mai, P. (2020). COVID-19 Twitter Dataset, Scholars Portal Dataverse."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e19273","DOI":"10.2196\/19273","article-title":"Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set","volume":"6","author":"Chen","year":"2020","journal-title":"JMIR Public Health Surveill"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1145\/3404820.3404823","article-title":"GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information","volume":"12","author":"Qazi","year":"2020","journal-title":"Sigspatial Spec."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Baran, E., and Dimitrov, D. (2021, June 15). TweetsCOV19-A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic. Available online: https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3340531.3412765.","DOI":"10.1145\/3340531.3412765"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2790","DOI":"10.1007\/s10489-020-02029-z","article-title":"Design and analysis of a large-scale COVID-19 tweets dataset","volume":"51","author":"Lamsal","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1038\/d41586-019-02235-x","article-title":"Disinformation\u2019s spread: Bots, trolls and all of us","volume":"571","author":"Starbird","year":"2019","journal-title":"Nature"},{"key":"ref_11","unstructured":"Loria, S. (2021, June 15). Textblob Documentation. Release 0.15. Available online: https:\/\/buildmedia.readthedocs.org\/media\/pdf\/textblob\/latest\/textblob.pdf."},{"key":"ref_12","unstructured":"Gupta, R.K., Vishwanath, A., and Yang, Y. (2020). Covid-19 twitter dataset with latent topics, sentiments and emotions attributes. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Banda, J.M., Tekumalla, R., Wang, G., Yu, J., Liu, T., Ding, Y., and Chowell, G. (2020). A large-scale COVID-19 Twitter chatter dataset for open scientific research\u2014An international collaboration. arXiv.","DOI":"10.3390\/epidemiologia2030024"},{"key":"ref_14","unstructured":"Alqurashi, S., Alhindi, A., and Alanazi, E. (2020). Large arabic twitter dataset on covid-19. arXiv."},{"key":"ref_15","unstructured":"Feng, Y., and Zhou, W. (2020). Is working from home the new norm? An observational study based on a large geo-tagged covid-19 twitter dataset. arXiv."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/6\/6\/64\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:16:55Z","timestamp":1760163415000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/6\/6\/64"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,16]]},"references-count":15,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["data6060064"],"URL":"https:\/\/doi.org\/10.3390\/data6060064","relation":{},"ISSN":["2306-5729"],"issn-type":[{"type":"electronic","value":"2306-5729"}],"subject":[],"published":{"date-parts":[[2021,6,16]]}}}