Across Platforms and Languages: Dutch Influencers and Legal Disclosures on Instagram, YouTube and TikTok
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Abstract (arXiv)
Content monetization on social media fuels a growing influencer economy. Influencer marketing remains largely undisclosed or inappropriately disclosed on social media. Non-disclosure issues have become a priority for national and supranational authorities worldwide, who are starting to impose increasingly harsher sanctions on them. This paper proposes a transparent methodology for measuring whether and how influencers comply with disclosures based on legal standards. We introduce a novel distinction between disclosures that are legally sufficient (green) and legally insufficient (yellow). We apply this methodology to an original dataset reflecting the content of 150 Dutch influencers publicly registered with the Dutch Media Authority based on recently introduced registration obligations. The dataset consists of 292,315 posts and is multi-language (English and Dutch) and cross-platform (Instagram, YouTube and TikTok). We find that influencer marketing remains generally underdisclosed on social media, and that bigger influencers are not necessarily more compliant with disclosure standards.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 4 of its 18 references that are in the map. First posted on arXiv 17 Jul 2024.
Builds on
4 of 18 references.
References · 4
- 2018An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterestcited by 10
- 2023Closing the Loop: Testing ChatGPT to Generate Model Explanations to Improve Human Labelling of Sponsored Content on Social Mediacited by 9
- 2020Characterising and Detecting Sponsored Influencer Posts on Instagramcited by 7
- 2024InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content Detectioncited by 4
Built on by
Citing papers per year: 2025: 2; 2026: 3.
5 papers.
- 2025Evaluating LLM-Generated Legal Explanations for Regulatory Compliance in Social Media Influencer Marketingcited by 4
- 2025Towards High-Fidelity Synthetic Multi-platform Social Media Datasets via Large Language Modelscited by 0
- 2026Turning Trust to Transactions: Tracking Affiliate Marketing and FTC Compliance in YouTube's Influencer Economycited by 0
- 2026ChildSafeAds Shared Task 2026: Commercial Content in Child-Facing YouTube Videoscited by 0
- 2026N"urnberg NLP at ChildSafeAds 2026: Structurally Dissimilar Voter Ensembles under Four Levels of Data Accesscited by 0
- 2026N"urnberg NLP at ChildSafeAds 2026: Structurally Dissimilar Voter Ensembles under Four Levels of Data Access28 Sep 2026
- 2026ChildSafeAds Shared Task 2026: Commercial Content in Child-Facing YouTube Videos19 Aug 2026
- 2026Turning Trust to Transactions: Tracking Affiliate Marketing and FTC Compliance in YouTube's Influencer Economy4 Mar 2026
- 2025Evaluating LLM-Generated Legal Explanations for Regulatory Compliance in Social Media Influencer Marketing9 Oct 2025
- 2025Towards High-Fidelity Synthetic Multi-platform Social Media Datasets via Large Language Models2 May 2025