Turning Trust to Transactions: Tracking Affiliate Marketing and FTC Compliance in YouTube's Influencer Economy

Chen Sun; Yash Vekaria; Zubair Shafiq; Rishab Nithyanand

cs.CYMar 4, 2026

Turning Trust to Transactions: Tracking Affiliate Marketing and FTC Compliance in YouTube's Influencer Economy

Authors: Chen Sun, Yash Vekaria, Zubair Shafiq, Rishab Nithyanand

Organizations: University of Iowa · UC Davis

Abstract

YouTube has evolved into a powerful platform where creators monetize their influence through affiliate marketing, raising concerns about transparency and ethics, especially when creators fail to disclose their affiliate relationships. Although regulatory agencies like the US Federal Trade Commission (FTC) have issued guidelines to address these issues, non-compliance and consumer harm persist, and the extent of these problems remains unclear. In this paper, we introduce tools, developed with insights from recent advances in Web measurement and NLP research, to examine the state of the affiliate marketing ecosystem on YouTube. We apply these tools to a 10-year dataset of 2 million videos from nearly 540,000 creators, analyzing the prevalence of affiliate marketing on YouTube and the rates of non-compliant behavior. Our findings reveal that affiliate links are widespread, yet disclosure compliance remains low, with most videos failing to meet FTC standards. Furthermore, we analyze the effects of different stakeholders in improving disclosure behavior. Our study suggests that the platform is highly associated with improved compliance through standardized disclosure features. We recommend that regulators and affiliate partners collaborate with platforms to enhance transparency, accountability, and trust in the influencer economy.

Figures & tables

Figure 1: Overview of our approach for detecting affiliate links and analyzing disclosure compliance.

Source	# videos	# hyperlinks	# channels
Reddit ( $\mathcal{D}_{\text{Reddit}}$ )	1.88M	3.78M	480.0K
Random ( $\mathcal{D}_{\text{Random}}$ )	64.2K	216.0K	58.7K
Trending ( $\mathcal{D}_{\text{Trending}}$ )	46.6K	120.1K	7.5K
Shopping ( $\mathcal{D}_{\text{Shopping}}$ )	10.9K	90.2K	2.9K
Total ( $\mathcal{D}_{\text{}}$ )	2.00M	4.13M	539.9K

Table 1: Summary of our dataset. Numbers reflect the number of unique videos, hyperlinks, and channels per source.

Compliance	Clarity of	Clarity of	Fraction of
status	compensation	relationship	videos (%)
CC	Clear	Explicit or Grouped	12.20
	Clear	Mixed Group	9.31
PC	Ambiguous	Explicit or Grouped	2.95
PC	Ambiguous	Mixed Group	6.35
	Total		18.61

Table 2: Breakdown of disclosure clarity in

\mathcal{D}_{\text{Affiliate}}

. Definitions of CC (clearly compliant), PC (possibly compliant), and NC (non-compliant) disclosures are in Section 2.3 .

	1–100K	100K–1M	1M+	Total
AV (%)	6.4 (+3.2)	12.7 (+4.0)	11.4 (+3.4)	8.5 (+3.2)
AC (%)	6.0 (+2.3)	17.0 (+3.0)	20.5 (+3.4)	7.7 (+2.5)
NALPV	5.8 (+1.7)	6.7 (+2.6)	4.6 (+1.1)	5.7 (+1.7)
FLAL (%)	46.0 (+1.8)	38.5 (+7.3)	29.2 (+4.9)	39.5 (+5.6)
NC (%)	61.4 (-17.6)	62.3 (-17.3)	74.7 (-9.3)	65.1 (-15.8)
PC (%)	23.8 (+7.9)	20.7 (+6.1)	14.5 (+4.2)	20.3 (+6.7)

Table 3: Prevalence of affiliate links and rates of (non-) compliance by channel size for videos uploaded between 2018–2024. Values in parentheses show changes from 2015–2018. Bolded cells indicate values that differ significantly from other channel size tiers within the same 2018–2024 period (

p<.05

). Comparing across time periods for each channel size, all differences were found to be statistically significant (

p<.05

) and thus not highlighted. A two-sided

z

-test of proportions was used to test the significance of differences in percentage of affiliate videos and channels, as well as the compliance rate across sources. A two-sided Welch’s

t

-test was used to test the significance of differences in number of affiliate links and affiliate fraction of links per video.

Category	AV	FLAL	NC	PC	CC
	(%)	(%)	(%)	(%)	(%)
Autos & Veh	17.1	52.2	65.3	20.0	14.7
Comedy	5.8	29.3	92.3	4.9	2.9
Education	14.6	36.1	66.1	21.3	12.6
Entertainment	7.3	35.2	75.1	12.9	12.0
Film & Anim	7.0	34.9	78.0	12.8	9.2

Table 4: Prevalence of affiliate links and rates of (non-) compliance of videos in

\mathcal{D}_{\text{Affiliate}}

across top 15 categories. Bolded cells indicate that the value was a full standard deviation above the mean for the corresponding metric computed over all subgroups.

Comparison	Metric (m)	$\Delta^{(m)}$	Effect (95% CI)
$\mu^{(m)}_{\text{Guidance}}-\mu^{(m)}_{\text{No guidance}}$	NC	-3.53	[-19.70, 13.63]
	PC	-7.58	[-22.73, 7.58]
	CC	11.11	[1.52, 21.21]
$\mu^{(m)}_{\text{Shopping}}-\mu^{(m)}_{\text{RR}}$	NC	-44.78	[-48.71, -40.63]
	PC	0.13	[-3.42, 3.57]
	CC	44.65	[41.03, 48.32]

Table 5: Effect of affiliate program guidance (top) and platform-level built-in tools (bottom) on compliance metrics. Values in bold indicate a statistically significant effect size at the corresponding confidence level.

Period	Source	AV (%)	AC (%)	NALPV	FLAL (%)	NC (%)	PC (%)	CC (%)
8/20-12/23	$\mathcal{D}_{\text{Trending}}$	15.01	17.95	3.64	25.50	86.43	8.53	5.04
	$\mathcal{D}_{\text{RR}}$	8.47	7.94	5.71	38.81	64.02	22.36	13.62
	$\Delta_{\text{Trending - RR}}$	6.54	10.02	-2.07	-13.31	22.41	-13.83	-8.58
1/24-12/24	$\mathcal{D}_{\text{Shopping}}$	50.06	51.52	10.20	79.05	20.70	17.39	61.90
	$\mathcal{D}_{\text{Trending}}$	15.81	18.06	$4.19^{*}$	$29.03^{*}$	83.61 ∗	10.15	6.24
	$\mathcal{D}_{\text{RR}}$	$6.18^{*}$	$6.17^{*}$	$5.08^{*}$	$41.50^{*}$	66.13	20.94	12.93

Table 6: Prevalence of affiliate links, disclosure rate and compliance across videos in different sources.

\mathcal{D}_{\text{RR}}

includes both

\mathcal{D}_{\text{Reddit}}

and

\mathcal{D}_{\text{Random}}

. Bolded cells indicate the highest values for a metric within a comparison. In each comparison period, all pairwise differences are statistically significant at

p<0.05

. A two-sided

z

-test of proportions is used to test the significance of differences in percentage of affiliate videos and channels, as well as the compliance rate across sources. A two-sided Welch’s

t

-test is used to test the significance of differences in number of affiliate links and affiliate fraction of links per video.

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Label	Description with example	Compliance Level
Clarity of Compensation
Clear	Clearly explain that the creator is compensated when a viewer uses the associated affiliate link. Example: “If you click on this link and purchase a product, I get a small commission at no cost to you.”	Compliant
Ambiguous	Do not explicitly mention the monetary benefit from viewers’ use of the affiliate link, instead using vague language to imply support. Example: “Support the channel through these links.”	Possibly compliant
None	Do no mention of any form of compensation associated with the affiliate links, instead using only technical terms which explicitly disallow by FTC FAQs ( FTC 2024b ) . Example is: “This is an affiliate link.”.	Non-compliant
Clarity of Relationship
Explicit individual	Disclosure placed directly alongside each affiliate link for maximum transparency Example: ”This is a sponsored link for <PRODUCT> : <affiliate link> .”	Compliant

Appendix

Table 7: Disclosure labels with descriptions, examples, and corresponding FTC compliance levels.

Classifier	Precision	Recall	F1
	(%)	(%)	(%)
Disclosure detection	94.7	98.9	96.8
(Total) Compensation	98.6	98.6	98.6
Clear	98.9	96.9	97.8
Ambiguous	99.0	99.8	99.5
None	98.0	99.0	98.5

Appendix

Table 8: Performance of our three disclosure segment classifiers on their respective holdout validation datasets. For the clarity of compensation and relationship classifiers, we also report the performance for each label.

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The rise of `kidfluencers' on YouTube has raised ethical concerns about child digital labor and exploitation. While emerging legislation attempts to regulate this ecosystem, empirical evidence linking exploitation to engagement remains scarce, given the difficulty of operationalizing exploitation at scale. This study presents a multimodal AI audit of 5,051 videos across 79 kidfluencer channels, using weak supervision to detect exploitation signals without large-scale manual labels. We aggregate noisy labeling functions -- including LLM-based classification of titles and GPT-4 Vision analysis of thumbnails and descriptions across six literature-grounded dimensions -- to assign a probabilistic exploitation score to each video. A multi-annotator validation study (N=107) shows strong agreement with human judgment (macro-average F1

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-3.8\%

, n.s.), suggesting the platform rewards commodification of the child's identity and labor over traditional advertising. These findings challenge policy frameworks focused solely on financial trusts, showing that engagement is systematically tied to the intensive, performative labor of children.

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