TikTok's Research API: Problems Without Explanations

Carlos Entrena-Serrano; Martin Degeling; Raziye Buse \c{C}etin; Salvatore Romano

REVIEW 4 major objections 6 minor 2 cited by

TikTok's Research API: Problems Without Explanations

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read TikTok's Research API silently fails to return metadata for roughly one in eight donated public videos, with no error message explaining why.

desk verdict Real problems with TikTok's Research API, but the paper's own arithmetic makes its headline failure rate unreliable. read the letter →

arxiv 2506.09746 v2 pith:LFXVQFGF submitted 2025-06-11 cs.CY

Carlos Entrena-Serrano , Martin Degeling , Salvatore Romano , Raziye Buse Çetin This is my paper · ORCID

classification cs.CY

keywords TikTokResearchAPIdatadonationsalgorithmauditsDigitalServicesActcompletenessplatformtransparencywebscrapingadvertising

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

After Europe's Digital Services Act pushed TikTok to open its Research API to independent researchers, this audit tried to use it the way data-donation studies do: take a list of roughly 260,000 TikTok URLs donated by users and look up each video's metadata through the API. About one in eight videos could not be retrieved at all, and the API gave no error message saying whether the video was deleted, private, from a Canadian account, or something else. By scraping TikTok directly, the authors found that 62.7% of the unretrievable posts were actually still public on the platform, and they identified recurring categories of missing content: TikTok's own official videos, advertisements, and about 1% of creators whose videos never appear in the API. The paper's conclusion is that the Research API in its current state cannot serve research that needs complete, consistent, up-to-date data, and that researchers need scraping as an independent check.

What carries the argument

The load-bearing mechanism is the data-donation pipeline: a donated list of TikTok URLs is fed to the Research API's video endpoint in batches of 100, and any ID that returns no metadata is then checked by scraping the public TikTok site to decide whether it is deleted or private or public-but-missing. The key quantities are the initial failure set of 70,239 IDs, the eventual recovery of 12,373 IDs through individual re-querying, and the final classification of the remaining 18,961 IDs into categories (Canada, advertisements, TikTok-authorized videos, accounts excluded wholesale, and an unexplained remainder). The analysis also documents a 'corrupted batch' effect in which one unavailable ID in a batch makes other public IDs in the same batch appear unavailable, which is itself an API inconsistency.

What would settle it

Run an independent crawler, from different IPs and without logging in, over the 70,239 initially missing IDs and compare its public/private classification with the paper's; if most of the videos the paper labels 'public but missing from the API' are actually inaccessible to the crawler, the 12.46% failure estimate collapses. Alternatively, if TikTok's Research API begins returning metadata for the dashboard's 10 persistently missing videos and the rate drops below one in eight on a fresh donated sample, the paper's conclusion would no longer hold.

Watch

Extended reading notes

Core claim

The central claim is that TikTok's Research API has a completeness and consistency problem, not merely a documentation gap. When 260,000 donated TikTok URLs were queried, 70,239 returned no metadata; after repeated individual queries and scraping, 18% of those were recovered, 36% had been deleted or made private, and 46% were confirmed public yet absent from the API. Within that public-but-missing share, Canadian creators (a documented restriction) and advertisements each accounted for a similar share, TikTok's own official videos were consistently absent, and 163 accounts had none of their videos accessible even though most were public. Because the API's error responses do not distinguish these causes, the authors conclude that a data-donation pipeline built on the API will silently drop roughly one in eight donated videos.

Load-bearing premise

The audit treats the authors' scraper's view of TikTok, what the public site shows from their vantage point, as the ground truth for whether a video is public, and treats the donated 260,000-URL list as representative of what data-donation research encounters.

Editorial extensions

If this is right

Data-donation studies of TikTok will silently lose roughly 12.46% of donated videos, and because the loss is concentrated in ads, TikTok's own content, and certain creators, it is not a random sample of the missing content.
API-only research cannot distinguish 'the creator deleted this' from 'TikTok is withholding a public video', so moderation and removal analyses built on error codes will be unreliable.
Any finding that relies on the Research API's completeness, such as prevalence of ads or creator-level comparisons, needs an external scraping check before it can be interpreted.
The public dashboard makes the failure reproducible and observable over time, allowing researchers to check whether a problem they see is unique to their account or systemic.

Reading between the lines

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

Because ads and TikTok's own company videos are overrepresented among the missing content, studies of political advertising and platform self-promotion that rely solely on the API will undercount exactly the content regulators care about under the DSA.
The 'corrupted batch' effect implies that non-retrieval is not always a property of the individual video; failure rates may depend on request composition, so even re-running the same query can change results.
The 12.46% figure is computed from one donated URL collection; extending the same scraping-plus-API check to random samples of For You Page content in multiple EU countries would show whether the failure rate is a stable property of the API or an artifact of that donor group.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The subject is important and the qualitative findings are plausible, but you cannot trust the headline number as written. The paper's central claim is that TikTok's Research API fails to return metadata for one in eight donated videos (12.46%). That statistic does not reconcile with the counts reported in the text, and the paper never explains the gap.

What is genuinely new here: the data-donation test with roughly 260,000 URLs is a realistic audit scenario, and the exclusion categories are concrete and useful—TikTok's own official videos, advertisements, Canadian accounts, and a set of about 1% of creators whose videos are entirely absent. The batch-corruption observation (a single non-public ID causing still-public IDs in the same batch to be dropped) is new and practically important for anyone using batch queries. The dashboard is a reasonable ongoing monitor, though it only tracks ten videos.

The soft spots are not minor. The arithmetic is internally inconsistent: the summary says 18% retrieved and 83% remained unavailable (which sums to 101%), and the category shares of 36% and 46% do not cleanly map onto the reported 18,961 IDs still not retrievable after re-querying. The 12.46% headline appears to be derived from the scraper-based estimate that 46% of the 70,239 initially missing videos were public but not available via the API, divided by 260,000. But the text explicitly says only 18,961 IDs were confirmed still not retrievable, which would give about 7.3% rather than 12.46%. If the 18,961 is only a subset because the individual re-query was interrupted, the paper never says so. That is load-bearing: the one-in-eight statistic is the main contribution, and it is not reproducible from the paper's own counts.

Also, the scraping ground truth is not documented—no classifier rules, no checks for false positives or negatives, no donor recruitment details. The paper does not release code or the full queried ID list, so independent verification is limited.

The conclusion that the API is incomplete is probably right, and the qualitative examples support it. But the current version does not support the specific quantitative claim. The paper deserves a serious referee because the topic matters for DSA compliance and platform accountability, but it needs heavy revision: fix the arithmetic, clearly separate observed re-query results from scraper-derived projections, document the scraping methodology, and release the data or code. I would send it to peer review with a request for major revision, not desk reject it. As it stands, I would not cite the 12.46% figure in my own work.

Referee Report

4 major / 6 minor

Summary. The manuscript reports an empirical audit of TikTok's Research API. Using a list of approximately 260,000 TikTok URLs obtained through data donations, the authors attempted to retrieve metadata via the official API. They report that 70,239 posts initially returned no metadata; after batch and individual re-querying they recovered 12,373 posts, leaving 18,961 IDs still non-retrievable. They scraped the platform to classify non-retrievable posts, finding that about 36% were deleted/private and that a large share were publicly available but not returned by the API. They highlight several categories of excluded content (TikTok company videos, advertisements, and accounts such as Canadian creators), report a second scraping-based experiment, and maintain a public dashboard monitoring 10 unavailable videos. The central claim is that the API is unreliable for data-donation research, with roughly one in eight donated videos missing metadata with no explanation.

Significance. The central claim is policy-relevant and the paper provides a needed field test of a regulated data-access mechanism; if the numbers were correct, the result would support the position that the TikTok Research API is not yet DSA-compliant for data-donation workflows. The paper contributes a large-scale empirical dataset, an external scraping benchmark, a reproducible failure-detection approach, and a publicly available monitoring dashboard. However, the main headline statistic is not internally consistent with the raw counts provided, and the paper does not release its data or scraper code; the quantitative basis for the policy conclusion therefore needs substantial clarification and repair.

major comments (4)

[Experiment 1 / Summary of findings (pp. 7–8, 13)] The paper's headline statistic is not reproducible from its own counts. In Experiment 1, after batch re-querying, 'Approximately 32,000 TikTok videos remained for testing'; after individual re-querying, 12,373 additional posts were retrieved and '18961 IDs are still not retrievable' (p. 8). Yet the Summary of findings states that only 18% of the 70,239 were retrieved and that 46% (approximately 32,310) of videos were public but unavailable via the API (p. 13). The directly observed remaining count is 18,961, which is 27% of 70,239, not 46%. The 46% figure appears to derive from an extrapolation or from the scraped classification of the initial non-retrieved set, but no rule for extrapolation is given, and the relation to the 18,961 re-query result is never explained. If the observed count is used, the failure rate is 18,961/260,000, approximately 7.3%, not the claimed 12.46%. Because the 'one in eight' figure is the central quantitative finding, this inconsistency is load-bearing and must be resolved.
[Summary of findings (p. 13)] The percentage breakdown in the summary is internally inconsistent. The text reports that 18% of videos were retrieved and then refers to 'the 83 percent that remained unavailable' (18% + 83% = 101%). It then attributes 36% of the unavailable videos to deletion/private status and 46% to public-but-unavailable, and adds 'the remaining 21% of videos' for which the reason is unknown; 36% + 46% + 21% = 103% if all are percentages of the same base. If instead the 21% is a sub-share of the 46% category (e.g., Canada 12%, ads 13%, unknown 21%), that should be stated explicitly and Figure 6 should define its denominator unambiguously. As written, the figure cannot be read without guessing which shares are shares of what.
[Conclusion (p. 17)] The conclusion asserts that 'almost 10,000 advertisements' are not accessible through the API, but no table, formula, or classification step in the paper leads to this number. The only ad-related quantitative detail in the experiments is the qualitative discussion of Figures 3–5 and the absence of a count in the 'Advertisements' section. If this number comes from the 46% category or from a separate analysis of the dashboard, that source must be specified, and the sample size used for the estimate should be reported.
[Experiment 1 (pp. 6–7)] The generalizability of the headline rate to data-donation research depends on how the 260,000-URL sample was constructed, but the paper provides no donor-recruitment details. The authors do not state who the donors were, how their data was collected, or whether the donated list over-represents particular creators, regions, or video types. Similarly, the scraper used to classify 'public' versus 'not public' is not described: no rules for handling age-restricted, geo-blocked, or regionally unavailable videos, and no validation of the scraper against the API. Without these details, the 12.46% figure cannot be interpreted as a general property of the API rather than a property of this particular donation sample.

minor comments (6)

[p. 8] The number '18961' should be written as '18,961' for readability.
[p. 13] '12,46%' uses a decimal comma; the rest of the paper uses dots, so this should be '12.46%'.
[pp. 7 and 13] The 62.7% figure for publicly available posts in Experiment 1 and the 46% public-but-unavailable figure in the summary are never reconciled; the paper should state whether these are measured at different stages of the re-query process.
[Figure 7 (p. 14)] The caption says 'Videos available ... within the first 100 videos' but the axis describes the number of creators; clarify the unit being plotted and how the percentages were computed.
[Known Limitations (p. 5)] The reference to 'Daikeler et al. (2024)' is not accompanied by a full bibliographic entry; please add it to the references.
[Monitoring APIs (pp. 15–16)] The dashboard methodology should be documented in the paper or on the dashboard itself, including query timing, exact API endpoints used, error handling, and how the 10 monitored videos were selected.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the report is an empirical measurement of API non-retrieval against an external scraping baseline, with no fitted parameters or definitional identities in the derivation.

full rationale

The paper's central claim is an empirical measurement: a set of donated TikTok URLs is queried through the Research API, non-retrieved IDs are then checked by scraping the public platform, and the paper reports the share that remain unavailable. There is no derivation chain in which an output is defined in terms of its own input. The 12.46% figure is a projected failure share computed from the observed non-retrieval categories and the initial donation list; it is not the output of a fitted model and no parameter is calibrated to the target statistic. The scraping step is an external benchmark, not an assumption that already contains the conclusion. The paper's self-citations, chiefly Daikeler et al. (2024) for data-quality criteria, are not load-bearing and do not establish any of the measured results. The apparent discrepancies among the reported counts (e.g., 18,961 still-not-retrievable IDs versus the 46% public-but-unavailable share) are a reproducibility or internal-consistency concern, not circularity: an inconsistent count is not an input that is renamed as a prediction. The authors also disclose that the rechecking was interrupted on May 16, 2025, which is a stated limitation rather than a circular step. Because the conclusions follow from direct observation of API behavior compared with platform availability, the analysis is self-contained and not circular.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters or invented entities. The core assumptions are the validity of scraping as ground truth, the representativeness of the donated URL sample, and the completeness of TikTok's documented exclusion rules; these are domain assumptions that the audit depends on but does not verify.

assumptions (3)

domain assumption Scraping the TikTok web interface is a valid ground truth for whether a video is publicly available and whether it is marked as an ad.
The categorization of videos as deleted, private, public, or ads relies on the authors' scraping implementation; no code or classification rule is provided.
domain assumption The donated dataset of approximately 260,000 TikTok URLs is representative of the videos that data-donation studies encounter.
The recruitment and composition of the donation dataset is not described; the one-in-eight statistic is computed only for this sample.
domain assumption TikTok's documented restrictions (48-hour delay, Canada, minors, age 18+) are the only intended exclusions from the Research API.
The paper treats undocumented exclusions as problems; if the documentation is incomplete, some unexplained absences may be intended but undisclosed.

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Cite this review

Pith. "Pith review of TikTok's Research API: Problems Without Explanations." pith.science (2026). https://pith.science/paper/LFXVQFGF

@misc{pith2026250609746,
  author       = {Pith},
  title        = {Pith review of: TikTok's Research API: Problems Without Explanations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LFXVQFGF}},
  note         = {Machine review of arXiv:2506.09746}
}

read the original abstract

Following the Digital Services Act of 2023, which requires Very Large Online Platforms (VLOPs) and Very Large Online Search Engines (VLOSEs) to facilitate data accessibility for independent research, TikTok augmented its Research API access within Europe in July 2023. This action was intended to ensure compliance with the DSA, bolster transparency, and address systemic risks. Nonetheless, research findings reveal that despite this expansion, notable limitations and inconsistencies persist within the data provided. Our experiment reveals that the API fails to provide metadata for one in eight videos provided through data donations, including official TikTok videos, advertisements, and content from specific accounts, without an apparent reason. The API data is incomplete, making it unreliable when working with data donations, a prominent methodology for algorithm audits and research on platform accountability. To monitor the functionality of the API and eventual fixes implemented by TikTok, we publish a dashboard with a daily check of the availability of 10 videos that were not retrievable in the last month. The video list includes very well-known accounts, notably that of Taylor Swift. The current API lacks the necessary capabilities for thorough independent research and scrutiny. It is crucial to support and safeguard researchers who utilize data scraping to independently validate the platform's data quality.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Auditing Meta and TikTok Research API Data Access under Article 40(12) of the Digital Services Act
cs.CY 2026-01 conditional novelty 7.0 of 10

TikTok and Meta research APIs expose only about 75% and 50% of user-visible posts, respectively, and strip most contextual metadata, making independent auditing of systemic risks structurally biased.
PoliTok-DE: A Multimodal Dataset of Political TikToks and Deletions From Germany
cs.SI 2025-09 reject novelty 6.0 of 10

PoliTok-DE is a proposed TikTok dataset with deletion tracking; the abstract's claimed scale and statistics are not supported by the paper's body.

Reference graph

Works this paper leans on

7 extracted references · 7 canonical work pages · cited by 2 Pith papers

[1]

TikTok authorized videos 9

work page
[2]

Accounts excluded from the API 10

work page
[3]

New videos take up to 48 TIKTOK’S RESEARCH API: PROBLEMS WITHOUT EXPLANATIONS | 6 hours to be added to the search engine

Advertisements 11 Summary of ﬁndings 13 Experiment 2: Scraping 14 Monitoring APIs: a Public Dashboard 15 Conclusion 16 Credits Authors: Carlos Entrena-Serrano 1 , Martin Degeling, Salvatore Romano, Raziye Buse Çetin. The contribution from AI Forensics is funded by core grants from Open Society Foundations, Luminate, and Limelight Foundation. All other con...

work page 2024
[4]

Getting Started

made public by a creator who is aged 18 and over; 2. AND, are posted in the regions of US, Europe and Rest of the World; 3. AND do not belong to Canada. 3 The reason behind TikTok's 48-hour delay for content retrieval via oﬃcial APIs is unclear, especially since comparable platforms do not impose such restrictions on researcher data access. This limitatio...

work page 2025
[5]

TikTok authorized videos From time to time TikTok posts company-sponsored videos related to oﬃcial announcements. This includes videos shown to new users about the risks of challenges, but more prominently, the video that TikTok CEO Shou Zi Chew posted in response to the introduction of the TikTok ban bill in the US Congress with more than 30 million view...

work page 2024
[6]

None of their videos could be retrieved although the majority is publicly available

Accounts excluded from the API There are also a number of accounts that seem to be excluded from the research API all together. None of their videos could be retrieved although the majority is publicly available. From our dataset, we could identify 163 of such accounts of which at least 5 diﬀerent videos were listed, but for which none returned any metada...

work page
[7]

Sponsored Content

Advertisements When ads are shown in the For You Feed, TikTok allows advertisers to hide their real identity behind a username of their choice (Figure 3) or a randomly generated username (Figure 4), accompanied by the label “Sponsored Content”. Figure 3: Screenshot of a faked ad username on an advertisement that was published on July 14, 2022. Screenshot ...

work page 2022

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

TikTok authorized videos 9

work page

[2] [2]

Accounts excluded from the API 10

work page

[3] [3]

New videos take up to 48 TIKTOK’S RESEARCH API: PROBLEMS WITHOUT EXPLANATIONS | 6 hours to be added to the search engine

Advertisements 11 Summary of ﬁndings 13 Experiment 2: Scraping 14 Monitoring APIs: a Public Dashboard 15 Conclusion 16 Credits Authors: Carlos Entrena-Serrano 1 , Martin Degeling, Salvatore Romano, Raziye Buse Çetin. The contribution from AI Forensics is funded by core grants from Open Society Foundations, Luminate, and Limelight Foundation. All other con...

work page 2024

[4] [4]

Getting Started

made public by a creator who is aged 18 and over; 2. AND, are posted in the regions of US, Europe and Rest of the World; 3. AND do not belong to Canada. 3 The reason behind TikTok's 48-hour delay for content retrieval via oﬃcial APIs is unclear, especially since comparable platforms do not impose such restrictions on researcher data access. This limitatio...

work page 2025

[5] [5]

TikTok authorized videos From time to time TikTok posts company-sponsored videos related to oﬃcial announcements. This includes videos shown to new users about the risks of challenges, but more prominently, the video that TikTok CEO Shou Zi Chew posted in response to the introduction of the TikTok ban bill in the US Congress with more than 30 million view...

work page 2024

[6] [6]

None of their videos could be retrieved although the majority is publicly available

Accounts excluded from the API There are also a number of accounts that seem to be excluded from the research API all together. None of their videos could be retrieved although the majority is publicly available. From our dataset, we could identify 163 of such accounts of which at least 5 diﬀerent videos were listed, but for which none returned any metada...

work page

[7] [7]

Sponsored Content

Advertisements When ads are shown in the For You Feed, TikTok allows advertisers to hide their real identity behind a username of their choice (Figure 3) or a randomly generated username (Figure 4), accompanied by the label “Sponsored Content”. Figure 3: Screenshot of a faked ad username on an advertisement that was published on July 14, 2022. Screenshot ...

work page 2022