An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest
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Abstract (arXiv)
While disclosures relating to various forms of Internet advertising are well established and follow specific formats, endorsement marketing disclosures are often open-ended in nature and written by individual publishers. Because such marketing often appears as part of publishers' actual content, ensuring that it is adequately disclosed is critical so that end-users can identify it as such. In this paper, we characterize disclosures relating to affiliate marketing---a type of endorsement based marketing---on two popular social media platforms: YouTube & Pinterest. We find that only roughly one-tenth of affiliate content on both platforms contains disclosures. Based on our findings, we make policy recommendations geared towards various stakeholders in the affiliate marketing industry, highlighting how both social media platforms and affiliate companies can enable better disclosure practices.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Mar 2018.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 1; 2026: 4.
10 papers.
- 2023Closing the Loop: Testing ChatGPT to Generate Model Explanations to Improve Human Labelling of Sponsored Content on Social Mediacited by 9
- 2024Across Platforms and Languages: Dutch Influencers and Legal Disclosures on Instagram, YouTube and TikTokcited by 5
- 2025Evaluating LLM-Generated Legal Explanations for Regulatory Compliance in Social Media Influencer Marketingcited by 4
- 2022Dark patterns in e-commerce: a dataset and its baseline evaluationscited by 3
- 2024The Monetisation of Toxicity: Analysing YouTube Content Creators and Controversy-Driven Engagementcited by 3
- 2023A Multimodal Analysis of Influencer Content on Twittercited by 2
- 2026Turning Trust to Transactions: Tracking Affiliate Marketing and FTC Compliance in YouTube's Influencer Economycited by 0
- 2026BetXplain: An Explanation-Annotated Dataset for Detecting Manipulative Betting Advertisements on Social Mediacited 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
- 2026BetXplain: An Explanation-Annotated Dataset for Detecting Manipulative Betting Advertisements on Social Media25 Jun 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
- 2024The Monetisation of Toxicity: Analysing YouTube Content Creators and Controversy-Driven Engagement1 Aug 2024
- 2024Across Platforms and Languages: Dutch Influencers and Legal Disclosures on Instagram, YouTube and TikTok17 Jul 2024
- 2023A Multimodal Analysis of Influencer Content on Twitter6 Sep 2023
- 2023Closing the Loop: Testing ChatGPT to Generate Model Explanations to Improve Human Labelling of Sponsored Content on Social Media8 Jun 2023
- 2022Dark patterns in e-commerce: a dataset and its baseline evaluations12 Nov 2022