ALGORITHMS, AESTHETICS AND THE CHANGING NATURE OF CULTURAL CONSUMPTION ONLINE
Bibliographic record
Abstract
This paper examines the development of digital subcultures and microtrends in a social media landscape increasingly driven by algorithms. We explore the increasing proliferation of subcultures defined by aesthetic categories which we refer to as “microtrends. In this paper we draw from a combined mixed-methods exploration– a visual discourse analysis taken in conjunction with critical technoculture analysis (CDTA) – of content shared to the popular hashtag #aesthetic across three different algorithmically driven social media platforms: TikTok, Instagram and Youtube. We aim to extend scholarship on digital subculture formation by examining the intersection of identity formation, algorithmic capitalism and user practices surrounding microtrends through the lens of user engagement and self identity guided by three central questions: (1) What tactics and practices constitute user participation in microtrends? (2) How does user engagement with microtrends function as an act of relational self expression? (3) What are user discourses surrounding microtrend participation? Three novel user practices are identified - aesthetic consistency, aesthetic anxiety, and aesthetic creation- which when taken together comprise of a process that we term “self-discretization” wherein users “do the work” of abstracting and fragmenting their identities for the sake of attaining visibility within a datafied digital environment. Ultimately this paper argues that in an increasingly algorithmic cultural landscape users begin to internalize not just the messaging, but also the logics of algorithmic capitalism and regimes of datafication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".