The DSA's Blind Spot: Algorithmic Audit of Advertising and Minor Profiling on TikTok

Abstract

Adolescents spend an increasing amount of their time in digital environments where their still-developing cognitive capacities leave them unable to recognize or resist commercial persuasion. Article 28(2) of the Digital Service Act (DSA) responds to this vulnerability by prohibiting profiling-based advertising to minors. However, the regulation's narrow definition of "advertisement" excludes current advertising practices including influencer marketing and promotional content that serve functionally equivalent commercial purposes. We provide the first empirical evidence of how this definitional gap operates in practice through an algorithmic audit of TikTok. Our approach deploys sock-puppet accounts simulating a pair of minor and adult users with distinct interest profiles. The content recommended to these users is automatically annotated, enabling systematic statistical analysis across four video categories: containing formal, disclosed, undisclosed and none advertisement; as well as advertisement topical relevance to user's interest. Our findings reveal a stark regulatory paradox. TikTok demonstrates formal compliance with Article 28(2) by shielding minors from profiled formal advertisements, yet both disclosed and undisclosed ads exhibit significant profiling aligned with user interests (5-8 times stronger than for adult formal advertising). The strongest profiling emerges within undisclosed commercial content, where brands/creators fail to label promotional content/paid partnership and the platform neither corrects this omission nor prevents its personalized delivery to minors. We argue that protecting minors requires expanding the regulatory definition of advertisement to encompass brand/influencer marketing and extending the Article 28(2) prohibition accordingly, ensuring that commercial content cannot circumvent protections merely by operating outside formal advertising channels.

Figures (5)

• Figure 1: Ad distribution by ad type and ad topic match per user. While minors are recommended less ads in total, such ads are not properly disclosed and are heavily personalized to match their interest.
• Figure 2: Ad topic distribution. While formal ads distribution indicate no significant personalization, disclosed and undisclosed ads show strong diagonal dominance.
• Figure 3: Predicted-true confusion matrices for ad type and ad topic labels for each of human annotators.
• Figure 4: Screenshot of the custom-developed tool providing an overview of the collected data. The displayed videos were presented to a user BeautyMinor (a 16-year-old minor with an expressed interest in beauty). The tags determine whether the annotation model determined any kind of advertisement or not. The faces and personal identifying information were blurred or anonymised to preserve creators' privacy.
• Figure 5: Screenshot of the custom-developed tool providing a detail of the video observed by a user. The displayed video was presented to a user FitnessMinor (a 17-year-old minor with an expressed interest in fitness). The tool displays automatic content analysis (including the reasoning of the model) together with the screenshot and URL to the video, streamlining the manual annotation process. The displayed video represents an example of paid partnership content. The faces and personal identifying information were hidden or anonymised to preserve creators' privacy.