Sponsorship Disclosure in Social Media Influencer Marketing: The Algorithmic and Non-Algorithmic Barriers
Bibliographic record
Abstract
The growth of social media influencer marketing has created sophisticated opportunities for deceptive marketing practices to proliferate online. While sponsorship disclosures alert consumers to the commercial nature of social media content and are required in different jurisdictions around the world, many influencer ads do not incorporate disclosures that comply with applicable laws. Building on Bourdieu’s theory of field as the theoretical lens, this research examines the disconnect between formal regulation and on-the-ground influencer marketing practices in Canada by investigating the current barriers to compliant sponsorship disclosure. Using semi-structured interviews with influencer relations professionals, who play an intermediary role between brands and influencers, the research explicates the (1) algorithmic challenges (e.g., algorithmic deprioritization or shadowbanning) and (2) non-algorithmic challenges (e.g., lengthy disclosure processes) to maintaining high disclosure standards. The research identifies the strategies influencer intermediaries can use to achieve upfront and conspicuous disclosure in a social media landscape where the algorithmic determinants of success are unpredictable, and the adaptation of applicable legal frameworks is traditionally slow.
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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 | 0.041 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".