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Big Data is Too Small: Research Implications of Class Inequality for Online Data Collection

Abstract : Digital technology has enabled researchers to access massive amounts of online data and analyze it almost instantaneously. This chapter explains the state of digital inequality, and offers a social class critique of using online data for research, as well as makes suggestions for addressing these weaknesses. It proposes that social class be more thoroughly studied as a concept in these and other digital studies, as well as included as part of any study using Big Data analytics. Research has shown class inequalities with social media usage for activism, in general, but less is known about digital activist network inequality. When Chris Anderson wrote his article in 2008, conducting social network analysis required the use of clunky software. Just as with network analysis, a variety of disciplines use automated text analysis, from history and film studies to computer science and sociology.
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https://hal-sciencespo.archives-ouvertes.fr/hal-03823826
Contributor : Hélène Saint-Gal Connect in order to contact the contributor
Submitted on : Friday, October 21, 2022 - 11:12:02 AM
Last modification on : Saturday, October 22, 2022 - 4:41:15 AM

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  • HAL Id : hal-03823826, version 1

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Jen Schradie. Big Data is Too Small: Research Implications of Class Inequality for Online Data Collection. June Deery; Andrea Press. Media and Class. TV, Film, and Digital Culture, Routledge, 2017, 9781138229792. ⟨hal-03823826⟩

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