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REVIEW 3 major objections 5 minor 94 references

When Kids Mode Isn't For Kids: Investigating TikTok's "Under 13 Experience"

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read TikTok's Kids Mode serves 83% non-child-directed videos, an audit finds.

desk verdict First audit of TikTok Kids Mode with a genuine methodological caveat: the 83% not-child-directed figure rests on like-count matching that lacks reported per-category validation, so the headline is plausible but not yet airtight. read the letter →

arxiv 2507.00299 v1 pith:AU63ERTQ submitted 2025-06-30 cs.HC cs.CR

Olivia Figueira , Pranathi Chamarthi , Tu Le , Athina Markopoulou This is my paper
classification cs.HCcs.CR
keywords TikTokKidsModechild-directedcontentCOPPAauditingForYoupageparentalcontrolsvideorepetitionunder-13experience
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper attempts to establish that TikTok's "Under 13 Experience" (Kids Mode) is not actually delivering a children's service in the content it shows. The authors built a new auditing method to crawl Kids Mode's For You page, matched each observed video back to a video on TikTok's regular mode, and manually labeled 485 unique videos under COPPA's definition of child-directed content. They report that 83% of those videos are not child-directed, that a small number contain sexually explicit or profane material, and that advertisements appear without being labeled as ads. They argue this matters because children may find Kids Mode unappealing or repetitive and switch to TikTok's regular mode, where they face well-documented privacy and safety risks. The paper also claims Kids Mode lacks parental consent, functional parental controls, and accessibility features.

What carries the argument

The load-bearing mechanism is a match-by-likes video identification pipeline. Because Kids Mode omits the Share button and its browser URLs do not map to regular-mode video IDs, the crawler uses UIAutomator2 to read each video's author profile name and like count from the interface, then queries the author's profile using the TikTok-Api for videos with a matching like count, recording exact, multiple, and closest matches. For each observation it captures screenshots and detects scene changes with OpenCV, and two human labelers validate the match by comparing those frames to the candidate regular-mode videos. The same labeling pipeline applies six binary content questions (L1-L6) based on COPPA and YouTube's "made for kids" guidance, turning child-directedness into a countable, auditable dataset.

What would settle it

Record the Kids Mode screen for every observation, then compare the recorded frames against the matched regular-mode video's frames and compute the mismatch rate separately for exact, multiple, and closest matches; if the mismatch rate is substantial for the 485 unique videos, the reported 83% child-directed and 9 inappropriate-content counts would rest on wrong video identities and the conclusion would not be supported.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central discovery is that TikTok's Kids Mode curates content that mostly does not meet the COPPA standard for child-directed material. Across 1,471 video observations (485 unique videos) collected from US test accounts, manual labeling with six COPPA- and YouTube-derived questions found that 83% (401/485) of unique videos were not child-directed, that only 84 videos (17%) were child-directed by at least one of four child-directed labels, and that no video satisfied all four labels. The dataset also contained nine unique videos with sexually explicit or profane content, six advertisement videos that were not disclosed as ads in TikTok's metadata, and frequent repetition, with 24% of neutral-collection videos repeated at least twice and one sequence of 17 videos repeated. The paper additionally claims that TKM lacks parental consent, meaningful parental controls, and accessibility settings, and that varying account age and username produced no statistically significant difference in the content shown. The authors conclude that Kids Mode is "for children by name only," that it may push children toward TikTok's regular mode, and that regulators should scrutinize the service.

Load-bearing premise

The paper's conclusion depends on each Kids Mode video being correctly matched to the same video on TikTok's regular mode by author name and like count; TikTok can change like counts or delete videos, so a wrong match would mislabel the content shown to the child.

Editorial extensions

If this is right

  • If the 83% figure is correct, TikTok's public description of the Under 13 Experience as a curated children's service misstates what users actually receive.
  • A child who finds Kids Mode repetitive and not child-oriented has a plausible incentive to lie about age and move to regular mode, where the paper documents abundant child-directed content alongside known safety and privacy risks.
  • Because TKM lacks a parental consent step and meaningful parental controls, parents cannot exercise the oversight COPPA contemplates for under-13 users.
  • The auditing method can be reused on other feature-limited, child-directed short-video platforms without needing a Share button or official API cooperation.
  • The absence of statistically significant differences across account ages and usernames implies TKM is not personalizing by age, so a single neutral account can represent the experience.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural next audit would test whether the low child-directed share is stable over time or reflects a small, stale inventory; the paper's own repetition data hint at the latter, but a longitudinal crawl could confirm it.
  • If TikTok exposed real video IDs inside Kids Mode, the matching uncertainty would disappear, making the 83% figure directly checkable without the authors' indirect matching steps.
  • Regulators could extend the YouTube-style made-for-kids labeling obligation to TikTok, requiring child-directed labels on TKM videos and on any content shown to under-13 users.
  • The paper's case study suggests a concrete design fix: repurposing the abundant child-directed content already on regular mode could raise TKM's child-directed share far above 17% without creating new content.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper presents an auditing methodology for TikTok's "Under 13 Experience" (Kids Mode, TKM), a feature-limited version of TikTok for US users under 13. Because TKM lacks a share button and hides video URLs, the authors identify observed videos by extracting the author's profile name and like count and matching these to videos on the author's profile in TikTok's regular mode, using exact, multiple, and closest-by-likes matches. They then capture screenshots of representative scenes to validate matches manually. The resulting dataset contains 1471 TKM video observations, of which 1438 were matched and labeled by two researchers. The authors define six binary content labels based on COPPA and YouTube's child-directed guidance, and report that 83% (401/485) of unique TKM videos were not child-directed, that 9 videos contained inappropriate content, and that TKM lacks parental consent flows and accessibility features. They also run experiments varying account age and gender-typed usernames, finding no statistically significant differences in content, and document frequent video repetition. The paper contributes both a methodology for auditing feature-limited, black-box child-directed services and an empirical characterization of TKM's content.

Significance. If the results are reliable, this is a significant contribution to the emerging literature on algorithmic auditing of child-directed online services. The paper addresses an important, understudied platform configuration and grounds its content analysis in COPPA's definition of child-directed content, which gives the work direct regulatory relevance. Methodologically, the authors solve a real data-collection problem (identifying videos without a share button or visible URL) and provide a replicable pipeline that could be extended to other short-form video platforms. The study is also refreshingly honest about several limitations, including the dynamic nature of content and the acknowledged underpowered statistical tests. The central claim, however, rests on a manually constructed dataset and an approximate video-matching procedure, so the reliability of the headline 83% figure is not yet fully established.

major comments (3)
  1. [§3.1.1, Table 1] The video-identification step is load-bearing for every downstream analysis, but the paper does not report match-validation statistics. Section 3.1.1 states that the number of likes shown in TKM can differ from the same video in regular mode and that some videos no longer exist; the fallback categories 'closest-match-by-likes' and 'multiple-match-by-likes' cover 382 of 1471 observations (about 26%) per Table 1. The screenshot-based validation is described, but no per-category confirmation or rejection rates are given, and no inter-rater reliability for the validation step is reported. If a non-trivial fraction of the closest or multiple matches are wrong, the six content labels are applied to the wrong videos and the 83% not-child-directed estimate (401/485) is not trustworthy. Please report the number of confirmed, rejected, and excluded cases per match category, and ideally a sensitivity analysis that recomputes the headline proportion under an assumed match-error rate.
  2. [§3.1.2, Table 2] The manual labeling procedure lacks quantitative reliability evidence. The paper reports that two researchers independently labeled and then discussed until 'full consensus' was reached, but it does not report inter-rater reliability (e.g., Cohen's kappa) or the number and resolution of disagreements. Since the central claim is a single point estimate derived entirely from these six binary labels, the labeling step needs a quantitative reliability assessment. Additionally, the conversion of 'maybe' responses to binary 'yes'/'no' is described only in passing; the paper should state how many 'maybe' responses occurred per label and how they were resolved, because this affects the reported proportions.
  3. [§4.3, Table 4] The claim that account age and username do not affect TKM content is used to justify using a single neutral account for the larger Neutral Dataset, but the statistical evidence is weak. The Fisher-Freeman-Halton tests are applied to roughly 50 videos per account and sparse category counts, and the paper itself acknowledges in Section 4.3 that the tests are 'limited by our dataset size' and 'relatively low frequencies.' With these conditions, 'no statistically significant difference' is not evidence of equivalence; it is consistent with an underpowered test. Please add effect-size measures, confidence intervals, or a formal equivalence test, or moderate the conclusion to 'no detectable difference in this sample.' Without this, the representativeness of the Neutral Dataset (and hence the aggregate 83% figure) remains a weaker inference than the text suggests.
minor comments (5)
  1. [§3.2.2] The text says the Neutral Data Collection attempts to collect and match 500 TKM videos, but Table 1 reports 535 observed and 516 matched; please clarify the relationship between the target of 500 and the reported counts.
  2. [Appendix B, Table 6] Section 4.3 refers to two experiments in Appendix B Table 6, and the Appendix B text says the table shows E1C1 and E2C2, but the table caption says E1C1 and E2C1; please reconcile the naming.
  3. [§4.2.1] The phrase '15 repeated videos sequences' should read '15 repeated video sequences' (or equivalent) for grammatical clarity.
  4. [§3.1.1, Reference [73]] The software library is referred to both as 'TikTok-Api' and (in the reference) as 'TikTok-Api'; please standardize the capitalization.
  5. [§5.4] The ethical considerations paragraph states the study raises no ethical issues, but the paper analyzes videos that may depict children (label L3) and content created by third parties; a sentence addressing creator privacy and the handling of potentially identifying data would strengthen the ethics statement.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claim is an observational measurement labeled against external standards, and the one self-citation is not load-bearing.

full rationale

The paper's central claim (83% of TKM videos are not child-directed) is an observational measurement, not a derived prediction. The six content labels are explicitly anchored to external standards: COPPA's definition of child-directed services and content, and YouTube's 'made for kids' documentation, and the manual labeling was performed by two researchers with consensus. The video-identification step (matching a TKM observation to a regular-mode video by profile name and like count, then validating with screenshots) is an approximation that could introduce measurement error, but it is not a fitted parameter and the conclusion is not defined in terms of it: the same labels would produce the same statistic regardless of which matching heuristic is used, although possibly on a different set of videos. The only self-citation, [15], is used to support the observation that TKM lacks a parental-consent process, which is also directly documented by the authors' screenshots and app inspection; that citation is not load-bearing for the content-curation claim. No equation or definition in the paper reduces the output to its input, and no uniqueness theorem or prior-work ansatz is invoked to force the choice of labels. Potential concerns about match quality and missing per-category validation rates are validity/correctness issues, not circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim depends on a chain of measurement assumptions: the API's correctness, the matching procedure's validity, and the manual labels' objectivity. None of these are machine-checked or supported by externally released artifacts, which is the main source of uncertainty.

assumptions (4)
  • domain assumption COPPA's definition of child-directed content can be operationalized as four binary labels (L1-L4) applied to each video.
    The paper extends a legal standard for whole services to individual videos without external validation.
  • domain assumption The TikTok-Api third-party library returns accurate video metadata and profile data.
    No verification against TikTok's official API is provided; the paper relies on this library for matching and metadata.
  • domain assumption Match-by-likes plus screenshot comparison correctly identifies the video shown in TKM.
    The paper notes like counts may differ and some videos no longer exist; manual validation is used but mismatches would transfer labels to wrong videos.
  • domain assumption The two researchers' manual labeling reaches ground truth.
    No inter-rater reliability metric reported; 'full consensus' is stated but not measured.

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Cite this review

Pith. "Pith review of When Kids Mode Isn't For Kids: Investigating TikTok's "Under 13 Experience"." pith.science (2026). https://pith.science/paper/AU63ERTQ

@misc{pith2026250700299,
  author       = {Pith},
  title        = {Pith review of: When Kids Mode Isn't For Kids: Investigating TikTok's "Under 13 Experience"},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AU63ERTQ}},
  note         = {Machine review of arXiv:2507.00299}
}
read the original abstract

TikTok, the social media platform that is popular among children and adolescents, offers a more restrictive "Under 13 Experience" exclusively for young users in the US, also known as TikTok's "Kids Mode". While prior research has studied various aspects of TikTok's regular mode, including privacy and personalization, TikTok's Kids Mode remains understudied, and there is a lack of transparency regarding its content curation and its safety and privacy protections for children. In this paper, (i) we propose an auditing methodology to comprehensively investigate TikTok's Kids Mode and (ii) we apply it to characterize the platform's content curation and determine the prevalence of child-directed content, based on regulations in the Children's Online Privacy Protection Act (COPPA). We find that 83% of videos observed on the "For You" page in Kids Mode are actually not child-directed, and even inappropriate content was found. The platform also lacks critical features, namely parental controls and accessibility settings. Our findings have important design and regulatory implications, as children may be incentivized to use TikTok's regular mode instead of Kids Mode, where they are known to be exposed to further safety and privacy risks.

Figures

Figures reproduced from arXiv: 2507.00299 by the authors.

Figure 1
Figure 1. TikTok’s Kids Mode Auditing Methodology Overview. (1) Data Collection: Our crawler script initiates and controls the crawl of TikTok videos on the mobile device with the TikTok app installed. (1a) For each video observed, the crawler will (1b) record the unique profile name and number of likes and capture screenshots of representative scenes in the video. The crawler uses the TikTok-Api [73] to find the video on the… view at source ↗
Figure 2
Figure 2. TikTok’s Kids Mode and TikTok’s Regular Mode “For You” Page (FYP) Interfaces. Figure (a) presents a screenshot from the TKM FYP and indicates the features and in￾formation provided (i.e., “Like” button, number of likes, “Report” button, and the profile name and photo of the video’s author.) Figure (b) presents a screenshot from TikTok’s regular mode and indicates the additional features provided (i.e., comments, “Bo… view at source ↗
Figure 3
Figure 3. Frequencies of Content Categories Across TKM Labeled Content Dataset. This graph visualizes the frequen￾cies of content categories across the unique videos in our TKM Labeled Content Dataset and across all observations (i.e., including repeated observations). We observed 55 content categories, and due to space, we omit 26 categories for which we observed frequencies fewer than 10. See Appendix A [PITH_FULL_IMAGE:fi… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: TikTok’s Kids Mode Settings and Screen Time Limit Pop-Up Page. This figure presents four screenshots from TKM to expand on the discussion in Section 4.4. Figure (a) shows the Settings page, (b) shows the Screen Time Limit page that appears after the first hour of TKM u…

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Reference graph

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.