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Cited by 107Ethics and Social Impacts of AIBig Data Technologies and ApplicationsTechnology's Impact on Media

The DOI matches its Crossref record; 72 of the 73 references it cites were found in a registry or an index.

Heuristics of the algorithm: Big Data, user interpretation and institutional translation

Open access
Bolin, Göran, Andersson Schwarz, Jonas·2015·Big Data & Society
DOI 10.1177/2053951715608406
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Abstract

Intelligence on mass media audiences was founded on representative statistical samples, analysed by statisticians at the market departments of media corporations. The techniques for aggregating user data in the age of pervasive and ubiquitous personal media (e.g. laptops, smartphones, credit cards/swipe cards and radio-frequency identification) build on large aggregates of information (Big Data) analysed by algorithms that transform data into commodities. While the former technologies were built on socio-economic variables such as age, gender, ethnicity, education, media preferences (i.e. categories recognisable to media users and industry representatives alike), Big Data technologies register consumer choice, geographical position, web movement, and behavioural information in technologically complex ways that for most lay people are too abstract to appreciate the full consequences of. The data mined for pattern recognition privileges relational rather than demographic qualities. We argue that the agency of interpretation at the bottom of market decisions within media companies nevertheless introduces a ‘heuristics of the algorithm’, where the data inevitably becomes translated into social categories. In the paper we argue that although the promise of algorithmically generated data is often implemented in automated systems where human agency gets increasingly distanced from the data collected (it is our technological gadgets that are being surveyed, rather than us as social beings), one can observe a felt need among media users and among industry actors to ‘translate back’ the algorithmically produced relational statistics into ‘traditional’ social parameters. The tenacious social structures within the advertising industries work against the techno-economically driven tendencies within the Big Data economy.

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Citations

Cites

Research works printed but not linked: 8

  1. Andriani P, 2011, Management, 14, 89
  2. Bolin G, 2014, Mittfåra och marginal, 229
  3. Castells M (2000) The Rise of the Network Society , 2 ed. Cambridge, MA, & Oxford: Blackwell.
  4. Folbre N, 1982, Cambridge Journal of Economics, 6, 317
  5. Ivory C, 2009, Ephemera: Theory & Politics in Organization, 9, 131
  6. Karlsson M, 2014, Dagensanalys.se
  7. Parks L, 2014, International Journal of Communication, 8, 2518
  8. Smythe D, 1977, Canadian Journal of Political and Social Theory, 1, 1

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Metadata

Authors
Bolin, G., & Andersson Schwarz, J.
Affiliations
  • Södertörn University (SE)
  • Department of Media & Communication Studies, Södertörn University, Huddinge, Sweden
Corresponding author
Bolin, Göran
ORCID
Bolin, Göran
Venue
Big Data & Society
Publisher
SAGE Publications
Published
2015-10-06
Volume
2
Issue
2
ISSN
2053-9517 (ISSN-L)
DOI
10.1177/2053951715608406
OpenAlex
W2286657468
Cited by
107
References
73
Citations by year
2026: 1 · 2025: 7 · 2024: 17 · 2023: 7 · 2022: 14 · 2021: 16 · 2020: 28 · 2019: 6 · 2018: 4 · 2017: 4 · 2016: 3
FWCI
10.68
Type
journal article
Language
en
Open access
Licence
Locations
  • Big Data & Society (publishedVersion, cc-by-nc)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
  • Social Sciences › Communication › Technology's Impact on Media
Keywords
big data, media audiences, user interpretation, institutional translation, social categories, human agency, advertising industry, data commodification

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