A Comparative Study of Consumer Protection Regulation in the Case of Online Influencers' Hidden Advertisement: Towards the Development of a Universal Regulatory Framework
Bibliographic record
Abstract
Social media has become highly influential, with statistics showing that TikTok, one of the leading social media platforms, has over 600 million active users. The popularity of these platforms has led to a rise of "influencers" who are individuals with a strong targeted audience of followers, carefully built through their online activities such as posting photographs and short videos on the platforms. As their posts attract significant attention from their followers, the influencers may, in return for commercial products being featured on their posts, receive benefits which mainly include direct financial benefits, and indirect financial benefits such as free products. This study explores the legal problems relating to online influencers' hidden advertisement. Arguably, influencers exploit the trust of their fans by posting sponsored advertisement in the forms of text, photographs, and short videos on their social platforms without clearly marking them as, or directly indicating that they are, advertisement. Such content impacts the protection of consumers' rights and interests, and may be perceived as misleading. This study, therefore, seeks to examine the current legal frameworks on hidden advertisement in several countries and regions, namely: the United States, the United Kingdom, the European Union, Canada, the Association of Southeast Asian Nations (ASEAN), and China. As the regulation of hidden advertisement is arguably on an early stage of development, this study hypothesizes that online influencers' hidden advertisement could be reduced by an introduction of a universal regulatory model which offers a clear framework for reducing hidden advertising and offer a general guidance in the area of influencers' advertising sponsorship. The study will also discuss the problems of a lack of governance and legislation in the area. Finally, the study will propose possible solutions for a universal regulation framework to better regulate online influencers' hidden advertisement.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".