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After Big Data Failed

The Enduring Allure of Numbers in the Wake of the 2016 US Election

Bibliographic Data

ID19574828
AuthorsYanni Alexander Loukissas (0000-0002-3023-6220, Georgia Institute of Technology), Yanni Loukissas, A Pollock (0000-0003-3112-2476, Georgia Institute of Technology)
Year2017
Volume3
Pages16-20
Publication date2017-02-17
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEngaging Science Technology and Society (JOURNAL)
Journal identifiersISSN: 2413-8053 • E-ISSN: 2413-8053
PublisherSociety for Social Studies of Science (4S) (PUBLISHER)
DOI10.17351/ests2017.150
OpenAlexW2588934864
LanguageEN
Citations received6

When widespread polling failed to accurately predict the 2016 US presidential election, producers and consumers of data didn’t abandon faith in numbers. Instead, they have reconfigured their relationships with big data. Producers are formulating redemption narratives, blaming specific datasets or poor interpretation, and the broader reception looks similar. Seeking an explanation for Trump’s unexpected victory, news audiences are calling out failed pre-election polling numbers, while at the same time embracing empirically dubious exit polls. This Critical Engagement piece argues that Science and Technology Studies scholarship has prepared us to see that polling errors would not undo the prestige and power of quantitative methods, but rather reveal the intensity of our attachment to data as a readily available arbiter. We show that data’s ambivalent qualities make it a durable ground for claims-making, with the capacity to be mobilized to do different kinds of work: blame, exoneration, and broader sense-making

Ambivalence · Blame · Narrative · Political economy · Political science · Politics · Polling · Presidential election · Prestige · Scholarship · Sociology · Victory · Big Data Technologies and Applications · Computer Science · Data Visualization and Analytics · Information Systems Theories and Implementation · Law · Psychology · Social Psychology

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Unique citing works6
Citations per year0.75
Citation span2018 - 2024 (7)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 3

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Open DOIOpen Access
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