{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:30:27Z","timestamp":1777455027184,"version":"3.51.4"},"reference-count":28,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data &amp; Society"],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>We are experiencing a historical moment characterized by unprecedented conditions of virality: a viral pandemic, the viral diffusion of misinformation and conspiracy theories, the viral momentum of ongoing Hong Kong protests, and the viral spread of #BlackLivesMatter demonstrations and related efforts to defund policing. These co-articulations of crises, traumas, and virality both implicate and are implicated by big data practices occurring in a present that is pervasively mediated by data materialities, deeply rooted dataist ideologies that entrench processes of datafication as granting objective access to truth and attendant practices of tracking, data analytics, algorithmic prediction, and data-driven targeting of individuals and communities. This collection of papers explores how data (and their absences) is figuring in the making of the discourses, lived realities, and systemic inequalities of the uneven impacts of the coronavirus pandemic.<\/jats:p>","DOI":"10.1177\/2053951720971009","type":"journal-article","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T00:00:50Z","timestamp":1604966450000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["Viral Data"],"prefix":"10.1177","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5167-0499","authenticated-orcid":false,"given":"Agnieszka","family":"Leszczynski","sequence":"first","affiliation":[{"name":"Geography, Western University, London, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6034-3262","authenticated-orcid":false,"given":"Matthew","family":"Zook","sequence":"additional","affiliation":[{"name":"Geography, University of Kentucky, Lexington, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,11,9]]},"reference":[{"key":"bibr1-2053951720971009","unstructured":"Aaronson D, Hartley D, Mazumder B (2019 [2017])\n                      The effects of the 1930s HOLC \u201cRedlining\u201d Maps\n                      . Federal Reserve Bank of Chicago Working Paper WP 2017-2. Available at: https:\/\/www.chicagofed.org\/publications\/working-papers\/2017\/wp2017-12 (accessed 20 July 2020)."},{"key":"bibr2-2053951720971009","unstructured":"Angwin J, Larson J, Mattu S, et\u00a0al. (2016) Machine Bias There\u2019s software used across the country to predict future criminals. And it\u2019s biased against blacks.\n                      ProPublica\n                      , 23 May. Available at: https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing (accessed 20 July 2020)."},{"key":"bibr500-2053951720971009","doi-asserted-by":"crossref","unstructured":"Bowe E, Simmons, E and Mattern S (2020) Learning from lines: Critical COVID data visualizations and the quarantine quotidian.\n                      Big data & society, 7\n                      (2), 2053951720939236.","DOI":"10.1177\/2053951720939236"},{"key":"bibr3-2053951720971009","unstructured":"Buolamwini, J. & Gebru, T. Gender shades: intersectional accuracy disparities in commercial gender classification. 2018.\n                      Proceedings of Machine Learning Research\n                      Vol 81: pp. 1\u201315. Vol 81 of PoMLR comes from the Conference on Fairness, Accountability and Transparency, 23-24 February 2018, New York, NY, USA."},{"key":"bibr4-2053951720971009","volume-title":"Parable of the Sower","author":"Butler OE","year":"1993"},{"key":"bibr5-2053951720971009","unstructured":"CBC News (2020) More people ready to be \u2018actively anti-racist\u2019 in light of George Floyd\u2019s death, Waterloo activist says.\n                      CBC News\n                      , 03 June. Available at: https:\/\/www.cbc.ca\/news\/canada\/kitchener-waterloo\/george-floyd-death-unrest-racism-waterloo-region-1.5596645 (accessed 21 June 2020)."},{"key":"bibr6-2053951720971009","doi-asserted-by":"publisher","DOI":"10.1177\/2043820620933860"},{"key":"bibr7-2053951720971009","doi-asserted-by":"publisher","DOI":"10.1177\/2043820620929797"},{"key":"bibr800-2053951720971009","doi-asserted-by":"crossref","unstructured":"D\u2019Ignazio C and Klein LF (2020) Seven intersectional feminist principles for equitable and actionable COVID-19 data.\n                      Big Data & Society 7\n                      (2): 2053951720942544.","DOI":"10.1177\/2053951720942544"},{"key":"bibr8-2053951720971009","volume-title":"Inequality by Design: Cracking the Bell Curve Myth","author":"Fischer CS","year":"1996"},{"key":"bibr9-2053951720971009","volume-title":"The Mismeasure of Man","author":"Gould SJ","year":"1996"},{"key":"bibr801-2053951720971009","doi-asserted-by":"crossref","unstructured":"Gruzd A and Mai P (2020) Going viral: How a single tweet spawned a COVID-19 conspiracy theory on Twitter.\n                      Big Data & Society, 7\n                      (2), 2053951720938405.","DOI":"10.1177\/2053951720938405"},{"key":"bibr10-2053951720971009","doi-asserted-by":"publisher","DOI":"10.1080\/24694452.2017.1293500"},{"key":"bibr12-2053951720971009","doi-asserted-by":"publisher","DOI":"10.1177\/1748048514568758"},{"key":"bibr8025-2053951720971009","doi-asserted-by":"crossref","unstructured":"Maalsen S and Dowling R (2020) Covid-19 and the accelerating smart home.\n                      Big Data & Society 7\n                      (2): 2053951720938073.","DOI":"10.1177\/2053951720938073"},{"key":"bibr13-2053951720971009","doi-asserted-by":"publisher","DOI":"10.1177\/1536504213511210"},{"key":"bibr812-2053951720971009","doi-asserted-by":"crossref","unstructured":"Milan S (2020) Techno-solutionism and the standard human in the making of the COVID-19 pandemic.\n                      Big Data & Society 7\n                      (2): 2053951720966781.","DOI":"10.1177\/2053951720966781"},{"key":"bibr813-2053951720971009","doi-asserted-by":"crossref","unstructured":"Milne R and Costa A (2020) Disruption and dislocation in post-COVID futures for digital health.\n                      Big Data & Society 7\n                      (2): 2053951720949567.","DOI":"10.1177\/2053951720949567"},{"key":"bibr814-2053951720971009","doi-asserted-by":"crossref","unstructured":"Pelizza, A. (2020). \u201cNo disease for the others\u201d: How COVID-19 data can enact new and old alterities.\n                      Big Data & Society 7\n                      (2), 2053951720942542.","DOI":"10.1177\/2053951720942542"},{"key":"bibr815-2053951720971009","doi-asserted-by":"crossref","unstructured":"Poom A, J\u00e4rv O, Zook M, and Toivonen T (2020) COVID-19 is spatial: Ensuring that mobile Big Data is used for social good.\n                      Big Data & Society 7\n                      (2): 2053951720952088.","DOI":"10.1177\/2053951720952088"},{"key":"bibr14-2053951720971009","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2020.100066"},{"key":"bibr805-2053951720971009","doi-asserted-by":"crossref","unstructured":"Sandvik KB (2020) \u201cSmittestopp\u201d: If you want your freedom back, download now.\n                      Big Data & Society 7\n                      (2): 2053951720939985.","DOI":"10.1177\/2053951720939985"},{"key":"bibr810-2053951720971009","doi-asserted-by":"crossref","unstructured":"Taylor L (2020) The price of certainty: How the politics of pandemic data demand an ethics of care.\n                      Big Data & Society 7\n                      (2): 2053951720942539.","DOI":"10.1177\/2053951720942539"},{"key":"bibr16-2053951720971009","volume-title":"Twitter and Tear Gas: The Power and Fragility of Networked Protest","author":"Tufekci Z","year":"2017"},{"key":"bibr17-2053951720971009","doi-asserted-by":"publisher","DOI":"10.24908\/ss.v12i2.4776"},{"key":"bibr18-2053951720971009","doi-asserted-by":"publisher","DOI":"10.14426\/mm.v2i1.55"},{"key":"bibr19-2053951720971009","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-020-01453-y"},{"key":"bibr20-2053951720971009","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.6548"}],"container-title":["Big Data &amp; Society"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/2053951720971009","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/2053951720971009","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/2053951720971009","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T12:58:07Z","timestamp":1777381087000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/2053951720971009"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":28,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["10.1177\/2053951720971009"],"URL":"https:\/\/doi.org\/10.1177\/2053951720971009","relation":{},"ISSN":["2053-9517","2053-9517"],"issn-type":[{"value":"2053-9517","type":"print"},{"value":"2053-9517","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7]]},"article-number":"2053951720971009"}}