The platformization of the follower factory: Para-platforms, automation, and labor in the market for social media engagements
Bibliographic record
Abstract
This article examines the emerging illicit, sprawling yet obfuscated global market for artificial social media engagements, which inflates follower counts and engagement metrics on social media profiles and posts. The organization of this market has previously been characterized using industrial metaphors such as “click farms,” “follower factories,” and “digital sweatshops” primarily based in the Global South. Using a mixed-methods approach that integrates ethnography with digital methods, this research delineates the platformization of the follower factory, highlighting a shift toward automation rather than manual interaction. This has facilitated the rapid expansion of a multi-sided market, enabling resellers to scale up and consequently necessitating a more complex labor organization that includes marketing and customer service, which have shaped cottage industries across the Global South. This market capitalizes on social media platform economies, using the existing infrastructure and user bases to operate. In other words, the engagement market has become centered on what we term a para-platform ecosystem, which, while operating alongside, remains reliant on social media platform infrastructure. By examining and conceptualizing platformization and platform ecosystems “from below,” as well as the conflictual asymmetry yet productive relationship between the para-platform ecosystem and social media platforms, this article provides an unprecedented description of the engagement market while challenging and expanding the boundaries of platform theory.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".