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REVIEW 3 major objections 5 minor 33 references

Revisiting Algorithmic Audits of TikTok: Poor Reproducibility and Short-term Validity of Findings

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Sockpuppeting audits of TikTok fail to reproduce, and their findings on what drives personalisation hold only in the short term.

desk verdict A valuable, honest replication study whose central reproducibility argument holds up; the specific 'watch is strongest' finding needs a caveat until the GDPR inference is independently confirmed. read the letter →

arxiv 2504.18140 v1 pith:S45FF55G submitted 2025-04-25 cs.IR cs.SI

Matej Mosnar , Adam Skurla , Branislav Pecher , Matus Tibensky , Jan Jakubcik , Adrian Bindas , Peter Sakalik , Ivan Srba This is my paper · ORCID
classification cs.IRcs.SI
keywords algorithmicauditsockpuppetingTikTokreproducibilitypersonalisationrecommendersystemsGDPRdataaccessshort-termvalidity
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 tries to rerun earlier sockpuppeting audits of TikTok's For You page—audits that create automated fake users to see what steers recommendations—and asks whether their results survive contact with a changed platform. It reports that they do not: the earlier audits are poorly reproducible because code, data, and procedural details were missing, and because TikTok's content, interface, and bot detection moved on. Rerunning the scenarios in early 2025, the paper finds that the watch action now provides the strongest personalisation signal, stronger than liking or following, and that the effect grows with watch duration. It also finds that conclusions flip depending on the evaluation metric chosen, so the one-shot audit approach itself is the problem. If the paper is right, regulators cannot rely on a single audit snapshot; they need reproducible, longitudinal audit methods.

What carries the argument

The load-bearing machinery is the paired-control sockpuppet audit. Two fresh TikTok accounts, identical except for one manipulated personalisation factor (location, watch duration, liking, or following), scroll 250 For You videos per session for four sessions; the feeds are compared through video popularity trends and hashtag similarity (strict Jaccard and a lenient substring-based 'basic match'). A post-audit data request under European privacy law is then used to check whether the bot's actions were actually logged, which is what exposes the missing explicit actions on the web interface. This machinery lets the paper separate what the recommender did from what the audit recorded, and it is the basis for both the reproducibility critique and the metric-sensitivity claim.

What would settle it

Run the same watch-duration versus explicit-action scenarios through TikTok's mobile app while intercepting network traffic to record every request. If likes and follows sent from the app change the feed as much as or more than watch duration, the paper's claim that explicit actions have little effect on the web and that watch is the strongest signal is contradicted. Alternatively, recompute the paper's comparisons using human-annotated video topics instead of hashtag similarity; if the ranking of personalisation factors changes, the watch-dominance finding is a metric artifact.

Watch

Extended reading notes

Core claim

The paper's central discovery is that the standard sockpuppet audit, applied to TikTok's For You page, fails a basic reproducibility check: trying to rerun two earlier audits after a gap of roughly three years required about nine person-months, extensive reverse-engineering, methodological fixes, and still produced different conclusions. In the new data, the implicit watch action has the strongest personalisation impact, with longer or repeated watching strengthening the effect, whereas likes and follows show an exploration phase followed by exploitation after around 1,000 videos. The previous hierarchy—follow first, watch about as strong as like—no longer holds. A post-audit check via user-requestable privacy data shows that explicit like and follow actions are missing from the recorded histories for most web-interface accounts, suggesting these actions may not reach the recommender at all on the web, which would bias any audit run through the browser. Finally, changing the evaluation metric (strict Jaccard vs. lenient basic match, or video popularity) is enough to reverse the apparent conclusions, so the paper frames its findings as evidence that algorithmic-audit findings are short-lived and method-dependent.

Load-bearing premise

The explanation for the like and follow results assumes the user-requestable data file is a complete record of every action the account performed, so an action missing from it was never received by the recommender rather than logged elsewhere or filtered out for bot accounts.

Editorial extensions

If this is right

  • Any single-run audit that releases no code, no data, and no precise scenario details cannot be independently verified, so regulators cannot tell whether a changed result reflects a changed algorithm or a changed audit.
  • Findings about which user actions drive TikTok personalisation carry an expiration date: the same scenarios that put follow first in earlier audits put watch first in early 2025, so one-shot studies should be labelled with the date and platform state they captured.
  • If explicit actions are indeed missing from web-interface signal paths, future TikTok audits should run through the mobile app or verify recorded actions with user-requestable data before drawing conclusions about likes and follows.
  • Audit conclusions are metric-dependent: strict hashtag Jaccard, lenient basic match, and video-popularity trends can point in opposite directions, so an audit should report several evaluation measures and make its choice explicit.
  • Because feed diversity has risen sharply since earlier audits (similarity between two bots doing the same thing fell from about 35% to about 10–11%), personalisation effects are now harder to detect and audits need larger sample sizes or stronger scenarios.

Reading between the lines

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

  • An implication the paper leaves open: if explicit actions really are dropped on the web pipeline, the earlier 'follow is strongest' finding may have been an artifact of the web interface, and a mobile-app replication could restore the old ranking.
  • Relatedly, the paper's 'watch is strongest' result may describe the web pipeline more than the recommender itself; on mobile, where likes and follows are recorded, the ranking of personalisation factors could differ.
  • The observed switch from exploration to exploitation for likes at roughly 1,000 interactions suggests a practical audit threshold: runs shorter than that may systematically understate explicit-action influence, so run length should be reported and treated as a boundary condition.
  • The interest-ratio landing at 36%, the low end of the earlier 30–50% band, could be reused as a moving benchmark: future audits that fall outside the band would trigger suspicion of an algorithm change before any deeper analysis.
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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 re-runs and extends prior TikTok sockpuppeting audits (Boeker and Urman 2022; Vombatkere et al. 2024; partly Mousavi et al. 2024) in January-February 2025. For RQ1 it documents concrete reproducibility barriers: missing or unusable released code, incomplete methodology descriptions, content and platform change, and bot bans or proxy failures. For RQ2 it compares location, watch duration, liking, and following via bot scenarios and two hashtag-similarity metrics plus video popularity, concluding that watch action now provides the strongest personalization signal, with like and follow showing an initial exploration phase. The authors release code and data and argue for longitudinal, more authentic, reproducible audits.

Significance. If the RQ1 findings are taken at face value, the paper makes a valuable contribution to the algorithmic-auditing literature by showing, in a documented multi-month replication effort, that current one-shot sockpuppeting audits are hard to reproduce and that conclusions are time-sensitive and metric-sensitive. The public release of code and data is a clear strength and should be credited. The RQ2 ranking, however, is the weaker part of the paper: several scenarios ran only once, no uncertainty quantification is reported, the main 'watch is strongest' claim depends on an unverified GDPR-export inference, and the authors themselves show that metric choice flips conclusions. The paper is honest in its Limitations section, but the abstract and Section 7 state the ranking without the same caveats. As a replication study, the central reproducibility argument is sound; as a claim about the current causal effect of user actions on TikTok personalization, it needs substantially more support.

major comments (3)
  1. [Section 4 and Section 5] The claim that explicit actions (like and follow) are not taken into consideration by the recommender rests on an inference from GDPR data exports: Section 4 states that 'while the watch history had an almost exact match, the explicit actions (like and follow) were missing for most of the accounts.' Absence from a GDPR export does not establish that the actions never reached the recommender; the signals could be stored in a separate pipeline, logged for anti-abuse purposes but excluded from the user-facing export, or affected by the study's own reversibility procedure described in Section 6. The paper itself hedges in Section 5 ('may be biased by the recommender not taking explicit actions into consideration'), but the abstract and Section 7 present 'the watch action provides the strongest personalisation impact' without that caveat. Because this finding is load-bearing for RQ2 and is cited in the contributions, the authors should either verify the inference (e.g., by running a mobile-app audit or otherwise confirming that explicit web actions are absent from the recommender's inputs) or reframe the ranking as an observation conditional on incomplete knowledge of how web actions are processed.
  2. [Section 5, Table 1, Table 3] The quantitative personalization comparisons lack error bars and significance tests, while several scenarios were run only once (Table 1, 'Rep.' column). This is especially problematic because the noise level is high: Section 5 reports that feed similarity between two control users ranges from 2% to 28% (average 11%). Against this baseline, reported differences such as the -2.06% watch effect and the +9.06% random-like effect in Table 3 are not convincingly distinguishable from noise. Bot bans, proxy misconfiguration, and incomplete sessions (acknowledged in Section 6) further reduce the effective sample for some scenarios. Without confidence intervals, significance tests, or at least per-run variability, the claim that watch action is 'the strongest' personalisation factor is not established by the reported measurements.
  3. [Section 5, Table 2 vs. Table 3] The paper demonstrates a strong dependence on the evaluation metric: Table 2 (video popularity) shows no consistent personalization advantage for the personalised user (e.g., random implicit feedback gives -2.11% for the personalised user versus -37.60% for the control), while Table 3 (hashtag basic match similarity) is used to conclude that watch is strongest. The authors acknowledge in the text and in Section 6 that changing the metric or its strictness can lead to 'completely different findings.' Given this acknowledged metric sensitivity, choosing one metric as the basis for the headline ranking is not robust. The authors should either report the ranking across all metrics and discuss disagreement explicitly in the conclusions, or restrict the headline claim to the specific metric used.
minor comments (5)
  1. [Section 3, Table 1] In the watch-duration scenario list, the items are numbered '1) 50% [S9]; 2) 200% [S10]; or 4) 400% [S11]'; the numbering skips '3)'. Please fix the enumeration.
  2. [Section 4] The statement that 'some of the bots (in 5 cases) were banned' is followed by '15 more accounts were banned after finishing the audit study.' It would be clearer to state the total number of accounts, the scenarios in which bans occurred, and the timing relative to data collection.
  3. [Section 4] The claim that 'there are no audits on Instagram or YouTube Shorts' is too absolute unless it is explicitly scoped to the systematic review's search date and method; please add the cutoff or qualify the statement.
  4. [Figures 2 and 3] The y-axis label 'ratio' is not defined in the figure captions; please state that it is the fraction of videos containing at least one predefined hashtag or substring, as described in the text.
  5. [Section 6] The Limitations paragraph lists three important threats (hashtag-based metrics, generic hashtags, and flagged bots) but does not explicitly connect them to the specific claims in Section 5; adding one or two sentences mapping each limitation to the affected finding would improve transparency.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an empirical replication audit whose conclusions are compared against external reference studies, with no fitted parameters renamed as predictions.

full rationale

The paper is an empirical replication study, not a derivation from fitted parameters. Its RQ1 and RQ2 conclusions are drawn from freshly collected bot-audit data (video metadata, hashtag similarity, play counts) and are compared against the published findings of Boeker and Urman, Vombatkere et al., and Mousavi et al. The methodological choices (scenario definitions, hashtag list, watch durations, metrics) are inputs to the experiment and are explicitly varied; the paper even demonstrates metric-sensitivity by reporting both Jaccard and basic-match similarity, which is a robustness check rather than a circular move. No parameter is fitted to a subset of the data and then renamed as a prediction. The GDPR post-audit check is an auxiliary validity probe; the inference that explicit actions may be ignored by the web-interface recommender is a stated, hedged interpretation (Section 5: 'may be biased by the recommender not taking explicit actions into consideration'), and while it has alternative explanations, it is not a definitional reduction of the paper's conclusions. Self-citations ([22], [24], [26]) are prior YouTube audit works cited as background or as motivation for continuous auditing; they do not carry the TikTok-specific empirical claims. The Limitations section explicitly flags hashtag-metric dependence, which weakens generalizability but does not make the argument circular. Overall, no load-bearing step reduces to its own inputs.

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

The central claims rest on a set of hand-chosen audit parameters and domain assumptions rather than on fitted physical parameters. The most consequential choices are the hashtag interest list, the similarity metric, and watch-time thresholds, because the paper shows these choices can alter conclusions. The GDPR-based inference adds an untested assumption about TikTok's data export completeness. No new theoretical entities are introduced.

free parameters (4)
  • Predefined interest hashtag list = 34 hand-selected hashtags (movie, foodtiktok, gaming, cat, comedy, etc.)
    Used to define 'videos of interest' in watch and like scenarios (Table 1); the hashtag-ratio results in Figures 2 and 3 depend directly on this list, which the authors adapted from Boeker and Urman without independent validation.
  • Hashtag similarity strictness = Jaccard, basic match, substring match
    Section 3 defines three similarity measures; Section 5 shows conclusions differ by measure. The choice is not grounded in an external benchmark, and the authors flag it as a limitation.
  • Watch duration thresholds = 25%, 50%, 75%, 100%, 200%, 400% of video duration; 120-second cap
    The dose-response conclusion that only very long watches (400%) strongly boost personalization (Figure 3) depends on these hand-selected thresholds.
  • Bots' session length and number of sessions = 250 videos per session, 4 sessions per scenario with roughly one-day breaks
    Exploration versus exploitation comparisons (around 1000 videos) and noise estimates are defined by this length; no sensitivity analysis is reported.
assumptions (4)
  • domain assumption Automated sockpuppet bots are a valid proxy for real user behavior on TikTok's For You page.
    All measurements are bot feeds, and the paper cites Vombatkere et al. [28] noting bots lack authenticity and diversity; GDPR checks show bot behavior differs from logged mobile behavior, so this assumption is load-bearing.
  • domain assumption Hashtag co-occurrence and play count are valid proxies for content similarity and personalization.
    Every quantitative finding in Section 5 uses these heuristics rather than content annotations; the authors themselves state that hashtag-based metrics may skew findings.
  • domain assumption Absence of explicit actions in the GDPR export means the web interface did not transmit those signals to the recommender.
    Section 4 uses missing likes and follows in GDPR data to conclude the recommender may ignore web explicit actions; alternative explanations such as data retention policies, separate processing pipelines, or bot detection are not controlled.
  • domain assumption A one-day break between sessions is sufficient for TikTok's recommender to incorporate prior feedback.
    The scenario design assumes latent personalization develops between sessions; no evidence or control condition validates this timing.

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Cite this review

Pith. "Pith review of Revisiting Algorithmic Audits of TikTok: Poor Reproducibility and Short-term Validity of Findings." pith.science (2026). https://pith.science/paper/S45FF55G

@misc{pith2026250418140,
  author       = {Pith},
  title        = {Pith review of: Revisiting Algorithmic Audits of TikTok: Poor Reproducibility and Short-term Validity of Findings},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S45FF55G}},
  note         = {Machine review of arXiv:2504.18140}
}
read the original abstract

Social media platforms are constantly shifting towards algorithmically curated content based on implicit or explicit user feedback. Regulators, as well as researchers, are calling for systematic social media algorithmic audits as this shift leads to enclosing users in filter bubbles and leading them to more problematic content. An important aspect of such audits is the reproducibility and generalisability of their findings, as it allows to draw verifiable conclusions and audit potential changes in algorithms over time. In this work, we study the reproducibility of the existing sockpuppeting audits of TikTok recommender systems, and the generalizability of their findings. In our efforts to reproduce the previous works, we find multiple challenges stemming from social media platform changes and content evolution, but also the research works themselves. These drawbacks limit the audit reproducibility and require an extensive effort altogether with inevitable adjustments to the auditing methodology. Our experiments also reveal that these one-shot audit findings often hold only in the short term, implying that the reproducibility and generalizability of the audits heavily depend on the methodological choices and the state of algorithms and content on the platform. This highlights the importance of reproducible audits that allow us to determine how the situation changes in time.

Figures

Figures reproduced from arXiv: 2504.18140 by the authors.

Figure 1
Figure 1. The similarity in feeds of control and personalised users for the [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Comparison between the like and watch actions using the percentage of videos that contain predefined user interests. We can observe a steady increase for the watch action and a strong exploration aspect for the like action at the start, followed by strong exploitation. to strong exploitation after interacting with enough videos (around 1000). However, it is important to note that the comparison between explicit and … view at source ↗
Figure 3
Figure 3. Watching less than 100% of the video does not seem to [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (1 more)
Figure 3
Figure 3. Figure 3: Comparison between the effects of different watch [PITH_FULL_IMAGE:figures/full_fig_p009_3.png]

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Reference graph

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