Heuristics of the algorithm: Big Data, user interpretation and institutional translation
Open accessAbstract
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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The amount and diversity of consumer data favor their algorithmic processing.
“The amount and the diversity of consumer data favor their algorithmic processing (Bolin and Andersson Schwarz, 2015).”
From How online advertising targets consumers: The uses of categories and algorithmic tools by audience planners (2024) · cites this paper as Bolin and Andersson Schwarz, 2015
As less human agency is incorporated in their building, post-demographic categories lack explainability.
“As less human agency is incorporated in their building, these categories lack explainability (Bolin and Andersson Schwarz, 2015); they have been coined”
From How online advertising targets consumers: The uses of categories and algorithmic tools by audience planners (2024) · cites this paper as Bolin and Andersson Schwarz, 2015
Algorithmically produced categories often need to be 'translated back' into more classical narrative categories.
“Several scholars have noted that algorithmically produced categories often need to be “translated back” into more classical narrative categories”
From How online advertising targets consumers: The uses of categories and algorithmic tools by audience planners (2024) · cites this paper as Bolin and Andersson Schwarz, 2015
Early academic works on big data technologies described these tools as post-demographic machines operating a switch from a 'Gaussian' to a 'Paretian' world.
“In early academic works on big data technologies, these tools have been described as post-demographic machines (Rogers, 2009), operating a switch from a “Gaussian” to a “Paretian” world (Bolin and Andersson Schwarz, 2015).”
From How online advertising targets consumers: The uses of categories and algorithmic tools by audience planners (2024) · cites this paper as Bolin and Andersson Schwarz, 2015
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Trust, Disconnection, Minimizing risk and Apathy
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Reklam Profesyonellerinin Gözünden Veriye Dayalı Reklamcılığın Bugünü ve Geleceği
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- 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)
- 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
- gold
- doi.org
- Locations
- Big Data & Society (publishedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Social Sciences › Decision Sciences › Information Systems and Management › 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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