Sponsorship Disclosure in Social Media Influencer Marketing: The Algorithmic and Non-Algorithmic Barriers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it