Influencing Children: Food Cues in YouTube Content from Child and Youth Influencers

Study Context & Rationale

• YouTube is among the most-used social platforms by children and adolescents worldwide.

  • Austria: $70\%$ of $11\text{–}17$-year-olds use YouTube several times a week.

  • Germany: $80\%$ of $12\text{–}19$-year-olds use it several times a week.

  • Platform specifics:

  • Nominal age limit of $13$, but YouTube’s Terms allow all ages with parental consent.

  • Sign-in is not required for most channels; children as young as $3$ are frequent viewers.

• Social media influencers derive persuasive power from para-social relationships.

  • Their content often includes paid or unpaid brand promotion.

  • Monitoring in Germany and Austria shows many undisclosed brand mentions.

  • Some influencers are themselves minors (so-called “child influencers”).

• Public-health concern: digital marketing of High-Fat, Sugar and/or Salt (HFSS) foods contributes to childhood obesity.

  • Meta-analyses link HFSS advertising to unhealthy diets and increased obesity prevalence.

  • Regulatory debates are active in Germany (proposal for complete HFSS ad ban to children) and Austria (stricter rules under discussion).

Study Objectives

• Provide empirical evidence on:

  1. Nutrient quality of food and beverage cues in child/youth influencers’ YouTube videos.

  2. Creative marketing strategies (“power variables”) used to present these cues.

• Support policy decisions on digital food-marketing regulation.

Methodology

• Design: Exploratory quantitative content analysis following WHO’s “CLICK” Social Media Influencer Marketing Protocol (Version 3, 2024).

• Sampling frame:

  • Search date: August 2022 via noxinfluencer.com.

  • Inclusion filters: >$50{,}000$ followers, German-language content, flagged “Made-for-Kids,” and clear on-screen presenters <$16$ years.

  • Final sample: $7$ child/youth influencer channels (6 female, 1 male; median age $12.5$ years, range $9\text{–}16$).

• Video sample:

  • Time window: $1$ July 2021 – $31$ June 2022.

  • Systematic random selection: $\le 2$ videos per month per channel (total $162$ videos; $33.8$ h).

• Coding scheme (two broad facets):

  1. Exposure variables – brand visibility, product type, context, etc.

  2. Power variables – appeal tactics (child/adolescent cues, positive language, consumption display, disclaimers, etc.).

• Nutrient profiling: WHO Europe Nutrient Profile Model v2 (2023).

  • Each cue classified as “Permitted,” “Not Permitted,” or “Miscellaneous.”

• Reliability testing:

  • $10\%$ (5 videos) double-coded.

  • Acceptable agreement: Krippendorff’s \alpha>0.67 and Fleiss \kappa>0.21.

  • Low-reliability categories (“primary/secondary persuasive appeal,” “brand equity characters,” “celebrity endorsers”) excluded.

• Statistics: Descriptive plus $\chi^2$ tests; effect size via Cramer’s $V$ (small $0.04$, medium $0.13$, large $0.22$). Significance at p<0.05.

Descriptive Characteristics of Influencer Channels

• Combined subscribers: $6.2$ million (median $0.85$ million; range $0.1–2.6$ million).

• Total uploads across channels: $6{,}158$ (median $520$).

• Engagement (for $88\%$ of sampled videos): median $2{,}839$ likes; $0$ comments (range $0–1{,}086$).

Exposure Findings

• Presence of food cues:

  • In $66.7\%$ ( $n=108$ ) of videos; average $27$ cues per hour.

  • $1.2\%$ of videos contained brand-only adverts; $32.1\%$ had no food cues.

• Total food cues identified: $\mathbf{n=901}$ (brand-only cues excluded from nutrient analysis).

• Top food categories (WHO-NPM):

  1. Chocolate & sugar confectionery, energy bars, sweet toppings/desserts – $19.8\%$.

  2. Fresh/frozen fruit, vegetables, legumes – $17.1\%$.

  3. Cakes, sweet biscuits, pastries – $8.2\%$.

  4. Ready-made & convenience dishes – $8.0\%$.

Nutrient Profiling Results

• Classification:

  • Not permitted for marketing to children: $67\%$ ( $n=604$ ).

  • Permitted: $30.4\%$ ( $n=274$ ).

  • Miscellaneous/undetermined: $2.6\%$ ( $n=23$ ).

• Nutrient thresholds exceeded (among not-permitted items):

  • Sodium > cut-off: $66.6\%$.

  • Energy density high: $63.8\%$.

  • Total fat high: $40.2\%$.

  • Saturated fat high: $27.8\%$.

  • Total sugars high: $27.7\%$.

  • Added sugars present: $62.1\%$.

  • Non-sugar sweeteners present: $7.3\%$.

• Nutrient profile summary:

  • $31.8\%$ met all cut-offs; $42.6\%$ exceeded one; $25.7\%$ exceeded two or more critical nutrients.

Power (Creative Strategy) Findings

Appeal Tactics

• Coded as appealing to:

  • Children: $85\%$ of cues.

  • Adolescents: $81.8\%$.

  • Either group: $86.5\%$.

• Association with nutrient class:

  • Child-appealing cues: $91.1\%$ not-permitted vs $71.9\%$ permitted ( p<0.001 ).

  • Adolescent-appealing cues: $88.4\%$ not-permitted vs $67.2\%$ permitted ( p<0.001 ).

  • Both groups: $92.4\%$ not-permitted vs $73.7\%$ permitted ( p<0.001 ).

Brand Visibility & Claims

• Brand logo alone visible: $36.8\%$.

• Packaged product visible: $45.7\%$.

• Unbranded product visible (e.g., plate of food): $65.4\%$.

• Health/nutrition claims: $8.7\%$ (organic, whole-grain, “healthy food,” etc.).

• Physical-activity claims: $9.6\%$.

• Disclaimers: $3.6\%$ (only $2\%$ linked product to a “balanced diet”).

• Health-washing elements: $2.4\%$.

Branding Category vs. Nutrient Class

• Overall cues: Unbranded $48.7\%$; Branded $41.2\%$; Retail/supermarket/store brand $7\%$; Misc $10.1\%$.

• Among permitted products, only $7.7\%$ were branded, vs $46.5\%$ of not-permitted products ( $\chi^2$, p<0.001, large $V=0.45$ ).

Cue Context & Language

• Location: Home $81.1\%$; outdoors $11.4\%$; supermarket $3.4\%$; exercise $1.7\%$; events $1.2\%$.

• Descriptive tone: Neutral $58.5\%$; Positive $34.6\%$; Negative $6.9\%$.

  • Positive tone more common for not-permitted cues ( $36.9\%$ ) than permitted ( $28.1\%$ ), $p=0.012$.

• Presentation mode:

  • Consumed + verbal reference: $42.2\%$.

  • Not consumed + verbal reference: $38.7\%$.

  • Not consumed, no verbal ref.: $15.2\%$.

  • Consumed, no verbal ref.: $3.9\%$.

Disclosure & Sponsorship Transparency

• Zero videos used explicit ad disclosures (e.g., “#ad,” “#Werbung,” gifting notices).

• Only one video description linked to a brand page (for a not-permitted product).

Discussion & Implications

• Key insight: Children are heavily exposed to appealing, largely unhealthy food cues via child/youth influencers on YouTube.

  • $\sim 2/3$ of cues fail WHO standards; chocolate & confectionery most frequent.

  • Persuasive features (positive language, on-screen consumption, child-targeted visuals) amplify potential impact.

• Platform policy (YouTube’s 2020 ban on paid food ads in kids’ content) insufficient because embedded influencer content falls outside detectable advertising formats and lacks mandatory disclosure.

• Regulatory momentum:

  • Germany: proposal for outright ban on unhealthy food promotions to children.

  • Austria: exploring stricter digital-marketing controls.

  • Evidence from Chile shows HFSS presentation bans reduce exposure.

• Potential strategies:

  • Enforce mandatory, recognizable disclosure for all paid, gifted or affiliate promotions.

  • Consider outright prohibition of HFSS product placement in content targeting under-18 audiences.

  • Develop AI-based monitoring tools to detect violations across digital platforms.

  • Encourage/promote healthy product placements (fruit, vegetables, water) leveraging influencer appeal.

Study Limitations

• Platform scope limited to YouTube; children also use Instagram, TikTok, streaming shows (~$132$ min/day in Germany).

• Cross-sectional content analysis cannot confirm behavioral outcomes; need longitudinal designs.

• Some persuasive variables excluded due to inter-coder reliability issues; future coding manuals (WHO 2024) now simplified.

Ethical, Funding & Administrative Notes

• No human participants; hence no ethics committee approval required.

• Funding: Austrian Federal Ministry of Social Affairs, Health, Care and Consumer Protection (Grant $2022{-}0.359.107$). Funder had no role in study execution.

• No competing interests declared by authors.

• Data available upon reasonable request to corresponding author.

Connections to Broader Literature & Theory

• Builds on Persuasion Knowledge Model $\text{(PKM)}$—children’s limited capacity to recognize covert marketing makes disclosures critical.

• Aligns with emotional-conditioning research—positive affect and para-social ties foster brand preference with minimal cognitive elaboration.

• Reinforces meta-analytic evidence linking HFSS ad exposure to increased caloric intake and obesity risk.

Key Numerical Summary (Quick Reference)

• Videos analyzed: $n=162$ ( $33.8$ h )

• Food cues: $n=901$ ( $27$ per hour )

• Not-permitted cues: $67\%$

• Child-appealing cues among not-permitted: $91.1\%$

• Branded among not-permitted: $46.5\%$

• Positive verbal tone for not-permitted: $36.9\%$

• Explicit ad disclosures: $0\%$

Take-Home Messages for Exam Preparation

• Memorize WHO NPM’s central role in determining “permitted vs. not permitted.”

• Recall the dominant categories: confectionery tops exposure; fruit/veg appear but in healthier minority.

• Understand “exposure” vs. “power” variables and examples of each.

• Be prepared to critique platform self-regulation and discuss policy interventions (full HFSS bans, mandatory disclosures, AI monitoring).

• Link findings to persuasion theories (PKM, emotional conditioning, para-social interaction).

My overall impression of the research and findings in this article is that it provides a comprehensive and concerning empirical analysis of child-directed online food advertising on YouTube. The study highlights the significant exposure children face to unhealthy food cues, primarily through child and youth influencers, and underscores the limitations of current platform policies. What is particularly striking and might be surprising is the finding that $67\%$ of the identified food cues were classified as "Not permitted for marketing to children" by the WHO Europe Nutrient Profile Model v2, with chocolate and sugar confectionery being the most frequent categories. Furthermore, the complete absence ($0\%$) of explicit ad disclosures like "#ad" or "#Werbung" is a critical insight, revealing how covertly these promotions are embedded within content. This definitely reinforces the idea that platform self-regulation is insufficient because embedded influencer content often falls outside detectable advertising formats, making it harder for children, who have limited persuasion knowledge, to recognize it as marketing. This changes one's opinion by confirming the pervasive and often hidden nature of unhealthy food marketing to children online. Regarding the use of influencers to promote foods, the research indicates that influencers derive persuasive power from para-social relationships and use appealing tactics like positive language and on-screen consumption. This aligns with persuasion knowledge models, suggesting that children's limited capacity to recognize covert marketing makes these influencer endorsements particularly impactful and concerning. The fact that child-appealing cues were significantly associated with not-permitted products ($91.1\%$) further highlights the ethical dilemma of leveraging such influence to promote potentially harmful products to a vulnerable audience. The study implicitly frames this as a public health issue that requires stronger regulatory interventions, such as mandatory disclosures or outright prohibition of HFSS product placement in content targeting under-18 audiences, given the demonstrated link to increased caloric intake and obesity risk.