AI-Generated Algorithmic Virality

Martin Degeling; Miazia Sch\"uler; Natalia Stanusch; Raziye Buse \c{C}etin; Salvatore Romano; Silvia Semenzin

REVIEW 3 major objections 5 minor 9 references

AI-Generated Algorithmic Virality

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

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Pith's one-line read One in four top TikTok search results is AI-generated, report finds.

desk verdict A transparent, useful first measurement of AI slop prevalence in search results, with a plausible headline figure that reviewers should stress-test on account cold-start effects. read the letter →

arxiv 2508.01042 v1 pith:TQPNXMGY submitted 2025-08-01 cs.CY

Natalia Stanusch , Martin Degeling , Salvatore Romano , Raziye Buse Çetin , Miazia Schüler , Silvia Semenzin This is my paper · ORCID

classification cs.CY

keywords syntheticAIimageryslopTikToksearchresultsInstagramAgenticAccountslabelingalgorithmicvirality

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 synthetic AI imagery is a common and mostly unlabelled presence in TikTok search results, and that most of it is produced by a small number of high-volume automated accounts. The authors manually annotated the top 30 search results for 13 hashtags across Spain, Germany, and Poland on TikTok and Instagram in June 2025. They find roughly one in four TikTok search results contains AI-made visuals, while Instagram shows far less. They also propose a taxonomy of 'Agentic AI Accounts' that post exclusively or mainly AI content via automated pipelines, and argue these accounts exploit platform algorithms. If true, this would mean recommendation systems are being gamed at scale by automated AI content, outpacing current labelling and moderation.

What carries the argument

The central object is the 'Agentic AI Account' (AAA), defined as an account that posts synthetic AI imagery exclusively or primarily, using partial or fully automated pipelines for research, creation, and distribution. The paper classifies AAAs into Mono-Topic (one format or subject), Poly-Topic (varied trends), and Hybrid (synthetic and regular imagery with AI-generated narratives). The other key mechanism is the manual annotation codebook for detecting synthetic AI imagery, applied by two independent coders to avoid confirmation bias. Together, the taxonomy and the codebook carry the argument: the taxonomy explains who produces the AI content, and the codebook provides the evidence that the content is synthetic.

What would settle it

A replication study using a diverse panel of real user accounts with varied browsing histories and devices, on both the web and app versions of TikTok, would settle the claim. If the share of AI content in the top 30 search results across the same hashtags and countries falls well below 25% when using such accounts, the prevalence claim would be refuted.

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Extended reading notes

Core claim

On TikTok, approximately 25% of the top 30 search results for common hashtags such as #health, #history, and #trump contain synthetic AI imagery, with similar shares across Spain, Germany, and Poland. On Instagram, the share is about 4.9%. More than 80% of the AI imagery on TikTok and Instagram is photorealistic, and over 80% of AI content on TikTok is posted by what the authors call Agentic AI Accounts, defined as accounts whose recent posts consist exclusively of synthetic AI imagery produced through partial or fully automated pipelines. Only about half of AI content on TikTok is labelled as AI, and on Instagram only 3 of 13 AI posts were labelled; labels are often hidden behind clicks or absent on the web version. The paper concludes that current AI labelling practices are insufficient and that Agentic AI Accounts represent a new form of automated content production that platforms are not adequately addressing.

Load-bearing premise

The prevalence numbers assume that search results collected from newly created, logged-in accounts behind residential proxies in each country approximate the top results ordinary users see; if platform search results vary by account history, device, or IP reputation, the 25% figure could be an artifact of the collection setup.

Editorial extensions

If this is right

If the 25% prevalence is representative, TikTok search results are a significant vector for AI-generated imagery on ordinary topics, not just niche AI tags.
The dominance of Agentic AI Accounts implies that a relatively small number of automated producers shape the visible AI content in search results, making moderation more tractable if platforms target such accounts.
The low labelling rate (roughly half on TikTok, 23% on Instagram) suggests current platform policies and self-disclosure are not effective, meaning DSA obligations for prominent marking are not being met in practice.
The finding that AI content is shared 1.15 times more often than non-AI content on TikTok suggests synthetic content may have a viral advantage, increasing the incentive for automated production.
Because Instagram's web version shows no AI labels, users on desktop are systematically deprived of AI disclosure information.

Reading between the lines

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

The prevalence numbers are likely time-sensitive: as generative video tools become widespread and watermarking remains restricted, the share of AI content in search results could grow beyond 25% in the near term.
The taxonomy of Agentic AI Accounts could be extended into a longitudinal detection method: monitoring posting rates and content homogeneity might allow platforms or researchers to flag AAAs before content goes viral, but this would require operationalizing the 'recent 10 posts exclusively synthetic' criterion into an automated classifier.
A testable extension: if platform algorithms truly favor engagement over provenance, then disabling sharing or demoting AI-posted content would disproportionately reduce the reach of Agentic AI Accounts; the paper's sharing-rate finding suggests a natural experiment for platform-level interventions.

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Signed reviews

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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 paper is worth a serious look. It is one of the first cross-country, cross-platform measurements of AI-generated content prevalence in search results, and it introduces a sensible taxonomy of what it calls Agentic AI Accounts. The headline finding—about 25% of top TikTok search results for ordinary hashtags contain synthetic AI imagery—is new data, and it is presented with an uncommon level of methodological honesty: two coders, a published codebook, a reported ~4% disagreement rate, and an explicit statement that datasets and code are attached. That is real evidence and it earns credit.

What it does well is scope itself. It does not claim to measure feeds or personalized recommendations; it measures search results for a small set of hashtags on three dates in three countries. The manual annotation is careful, and the qualitative taxonomy of slop types and AAA subtypes is a genuine conceptual contribution beyond the existing media reporting.

The soft spots are real but not fatal. The sample is narrow: 13 hashtags, top 30 results, three countries, two TikTok snapshots, one Instagram snapshot. Percentages are reported without confidence intervals, and the “growing” language in the conclusion is not supported by two time points. On Instagram the AI-content count is so small (13 posts) that any percentage is fragile.

The stress-test concern about fresh proxy-created accounts is legitimate and the paper does not fully address it. New accounts have no watch history, and TikTok search ranking is personalized and known to have cold-start behavior. If unpersonalized accounts are served more engagement-bait or AI-specialist content, the 25% figure could overstate what a typical logged-in user sees. This does not sink the paper, but it should be discussed explicitly and ideally tested by comparing a few queries from established accounts with different histories. The paper acknowledges web/app differences but not account-age effects, and it does not report how many accounts were created or whether the same account ran all queries in a country. That is the main methodological gap.

The AAA definition (last 10 posts exclusively AI) is a clean operationalization, and the claim that over 80% of AI content comes from such accounts is not circular in a harmful way—it is a structural finding about the supply of this content. The circularity burden is low.

Bottom line: this is a useful descriptive study for platform governance and DSA enforcement discussions. It deserves peer review, not a desk reject. A good referee should push for more detail on account creation, robustness to cold-start, and a more careful hedged conclusion about growth. I would bring it to a reading group and would cite it if I worked on AI content moderation or platform transparency.

Referee Report

3 major / 5 minor

Summary. This manuscript reports a cross-platform, cross-country measurement of AI-generated visual content in TikTok and Instagram search results. Over two collection dates in June 2025, the authors annotated the top 30 results for 13 hashtags (politics, health, history, pope-related) in Spain, Germany, and Poland, coding each item for synthetic AI imagery (full/partial, photorealistic) and for the presence of platform or user AI labels. They find that roughly 25% of TikTok search results contain synthetic AI imagery, with lower prevalence on Instagram, and that more than 80% of TikTok AI-tagged content was posted by accounts whose last 10 posts were exclusively AI, which they term Agentic AI Accounts. They propose a taxonomy of such accounts, discuss examples of AI slop, and argue that platform labeling is insufficient and often hidden. The paper includes a detailed codebook, qualitative examples, and regulatory implications.

Significance. If the reported prevalence is accurate, the finding that one in four top TikTok search results contains synthetic AI imagery is important and policy-relevant, and the cross-national design is a strength. The manual annotation methodology is systematic, with dual coding and reported disagreement rates, and the public availability of the codebook and dataset supports reproducibility. However, the main quantitative claims rest on a convenience sample collected through newly created accounts, and the 'Agentic AI Account' category is defined by a threshold that makes the 80% share partly tautological. The paper is valuable as a descriptive snapshot and a taxonomy, but the headline numbers should be treated as provisional until the account-selection issue is addressed.

major comments (3)

[Methodology – Data collection] The headline prevalence figures (Table 8 and Executive Summary) are measured from search results obtained with newly created accounts behind residential proxies. TikTok search ranking is personalized, and fresh accounts have no watch, like, or follow history, so the set of top results may differ systematically from what established logged-in users see. The paper acknowledges web/app differences (Methodology) but does not address account-age or personalization effects, nor does it report how many accounts were created per country or whether all queries for a country used the same account. Please provide a robustness analysis (e.g., compare against a small number of long-standing accounts, or against non-logged-in top-6 results) or explicitly restrict the claim to 'search results as seen by a newly created account.'
[Taxonomy of Agentic AI Accounts] The operational definition of an Agentic AI Account as an account whose most recent 10 posts consist exclusively of synthetic AI imagery means that the statement 'over 80% of synthetic AI content on TikTok was posted by Agentic AI Accounts' is in large part a restatement of the classification rule rather than an empirical discovery about automation. The paper further asserts that these accounts automate content creation and posting through pipelines, but no direct evidence (posting frequency, timing patterns, API signatures, or otherwise) is presented to distinguish automated from human-run content farms. Please provide such evidence, or rename the category to a more neutral term (e.g., 'AI-specialist accounts') and mark the automation inference as a hypothesis.
[Generative AI content on TikTok] The paragraph following Figure 13 reports that synthetic AI content is 'statistically significantly' shared approximately 1.15 times more often than other content, but it does not report the test used, the effect size, a p-value, or a confidence interval, and no multiple-comparison correction is mentioned. Because this is the only inferential statistic in the manuscript, please provide the full test details or remove the significance claim.

minor comments (5)

[AI Label on TikTok and Instagram] The text refers to 'Table 9' when reporting labeling percentages, but Table 9 is an earlier examples table; the labeling percentages actually appear in Table 16.
[Table 16] The entries in Table 16 are inconsistent in formatting (e.g., '22.45' without a percent sign, '6.45 %' with a space); please standardize the notation.
[Generative AI content on TikTok] The text reporting the highest single-hashtag prevalence for #pope in Poland as 'over 53%' contradicts Table 15, which reports 35.71% and 37.84% for the aggregate #pope/translation in Poland; please reconcile the numbers.
[Results of our research] The prevalence estimates in Table 8 are point percentages without confidence intervals; given the small number of queries, countries, and collection dates, the authors should state the uncertainty, for example by treating each hashtag-country-date combination as a cluster.
[Executive summary] The term 'Agentic AI Accounts' is used in the executive summary before its operational definition appears later in the Methodology and Taxonomy sections; a forward reference would help readers who skip the executive summary.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the prevalence and Agentic AI Account concentration figures are empirical measurements, not derived from fitted inputs or self-citations.

full rationale

The paper's central quantitative claims, such as roughly 25% of TikTok top-30 search results containing synthetic AI imagery and over 80% of that content being posted by Agentic AI Accounts, are empirical measurements derived from manual annotation of collected search results. There is no mathematical derivation chain in which an output is equivalent to an input by construction. The Agentic AI Account definition uses a stated threshold (the most recent 10 posts consisting exclusively of synthetic AI imagery), but the reported concentration percentages are not logically forced by that definition; they could have been low if AI content had been spread across ordinary accounts. The threshold is a classification rule, not a fitted parameter, and the paper does not rename the threshold as a prediction. The only self-referential element is the statement that the codebook builds on AI Forensics' previous report, but the codebook itself is included and the manual coding is validated by inter-coder agreement, so the central findings do not reduce to that citation. Concerns about freshly created accounts and residential proxies affecting search rankings are external-validity limitations, not circularity. The paper is self-contained as an observational measurement study.

Assumptions & free parameters 2 free parameters · 4 assumptions · 1 invented entities

The central claims depend on hand-chosen sampling thresholds (top 30 results, 10-post AAA definition), on the assumption that proxy-based account search results reflect typical user search results, and on the assumption that manual visual coding is accurate. The AAA category is an invented construct with no independent validation.

free parameters (2)

top_results_per_query = 30
The analysis only annotated the top 30 search results per query. This cutoff is hand-selected and directly determines prevalence percentages; different cutoffs could change the numbers.
aaa_recent_post_threshold = 10
Accounts were considered 'agentic' if their most recent 10 posts consisted exclusively of synthetic AI imagery. This hand-picked threshold defines the AAA category and drives the claim that over 80% of AI content comes from such accounts.

assumptions (4)

domain assumption The top 30 search results collected from freshly created accounts routed through residential proxies approximate the search results that typical users in Spain, Germany, and Poland would see.
The paper used logged-in accounts with residential IPs to collect results from TikTok web and an unofficial Instagram API, then manually checked labels via an Android phone. It acknowledges possible differences between web and app versions (Methodology, Data collection). The representativeness of this setup is assumed, not validated against actual user search results.
domain assumption Manual visual inspection by trained coders using the provided codebook is a reliable ground truth for detecting synthetic AI imagery in low-quality social media video.
The paper states 'there are no reliable tools to automatically detect the variety of synthetic AI content' and relied on manual coding by two coders with a ~4% disagreement rate after reconciliation. This assumes the coders' judgments, guided by the codebook and reverse-image searches, are accurate. No external validation against known ground truth is provided.
ad hoc to paper An account whose most recent 10 posts consist exclusively of synthetic AI imagery is an 'Agentic AI Account' that uses automated pipelines.
The taxonomy's operational definition (Taxonomy of Agentic AI Accounts) equates posting exclusively AI content for 10 posts with automation/agentic behavior, even though the paper notes it does not verify whether posting is actually automated. This is an ad hoc criterion chosen by the authors.
domain assumption The selected 13 hashtags and their translations represent broader topic areas and allow cross-country comparison.
The hashtags were chosen in May 2025 based on media coverage, and the paper acknowledges that selection and translation issues (e.g., Spanish #papa returning father-related content) may have impacted results (Methodology, Data collection; Results).

invented entities (1)

Agentic AI Accounts (AAA)
purpose: A category of social media accounts that post predominantly or exclusively synthetic AI content, supposedly through automated pipelines, used to describe the majority of AI content producers in the dataset.
The paper introduces this category and its subtypes (Mono-Topic, Poly-Topic, Hybrid) based on observed posting behavior. It provides no falsifiable handle outside the paper: any account posting 10 consecutive AI images is classified as AAA, so the category is defined by the paper's own threshold rather than an external measure of automation. The paper itself only 'predicts' full automation in the future.

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Cite this review

Pith. "Pith review of AI-Generated Algorithmic Virality." pith.science (2026). https://pith.science/paper/TQPNXMGY

@misc{pith2026250801042,
  author       = {Pith},
  title        = {Pith review of: AI-Generated Algorithmic Virality},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TQPNXMGY}},
  note         = {Machine review of arXiv:2508.01042}
}

read the original abstract

There is a growing discussion about social media feeds being increasingly filled with AI-generated content. Due to its visual plausibility, low cost, and fast production speed, AI-generated content is said to be highly effective in "gaming the algorithm" and going viral. Popularly referred to as "AI slop," this phenomenon arguably leads to the presence of sloppy and potentially deceptive content at a scale unseen before. This investigation offers a systematic analysis of AI-generated content and its labelling in TikTok's and Instagram's search results across 13 hashtags (see Appendix) in three European countries (Spain, Germany, and Poland) over the course of June 2025. We manually annotated and analyzed the 30 top search results on political (#trump, #zelensky, #pope) and broader topics (e.g.,#health, #history) to understand the relation between synthetic (content that is partially or entirely made using generative AI) and non-synthetic content across languages and countries. We then explored the emerging phenomenon of accounts producing generative AI content at scale by analyzing 153 accounts and proposing a new categorization schema of what we termed Agentic AI Accounts. Our main findings are:

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

9 extracted references · 9 canonical work pages

[1]

Mono-Topic Agentic AI Accounts

work page
[2]

Partial GenAI - content includes both synthetic and non-synthetic imagery, for example, synthetic AI images intertwined with stock images. 2.1 photorealistic - content which is labelled as Partial GenAI and consists of a representation of stylistic realism; it imitates the stylistic exactness of a photograph or ﬁlm capture (unlike, e.g., a cartoon)

work page
[3]

Unclear - no deﬁnitive conclusion on the nature of the content can be drawn from the information at hand

work page
[4]

AIF Guidebook: A Human Guide to Detecting Synthetic AI Imagery

Not GenAI - the content is deﬁnitely not made using generative AI tools. 4 “AIF Guidebook: A Human Guide to Detecting Synthetic AI Imagery” AI-Generated Algorithmic Virality | 19 Results of our research Based on our research, synthetic AI content is present to a similar extent across Spain, Germany, and Poland in the search results on TikTok and Instagram...

work page 2025
[5]

Poly-Topic Agentic AI Accounts

work page
[6]

waitlist only

Hybrid Agentic AI Accounts specialize in one convention, usually both in subject matter and formal qualities attempt various formal and subject matter conventions, often following memetic-like trends use both synthetic, stock, and found imagery with AI-generated audio voiceover ﬁg. 16A [link ] ﬁg. 16B [link ] ﬁg. 16C [link ] ﬁg. 16D [link ] ﬁg. 16E [link ...

work page
[7]

GenAI - content consists of generative AI imagery, that is, synthetic visuals (moving and/or still images). 1.1 photorealistic - content which is labelled as GenAI and consists of a representation of stylistic realism; it imitates the stylistic exactness of a photographic or ﬁlm capture (unlike, e.g., a cartoon)

work page
[8]

Partial GenAI - content includes both synthetic and non-synthetic imagery, for example, generative AI images intertwined with stock images. 2.1 photorealistic - content which is labelled as Partial GenAI and consists of a representation of stylistic realism; it imitates the stylistic exactness of a photographic or ﬁlm capture (unlike, e.g., a cartoon)

work page

Show all 9 references

[9]

AIF Guidebook: A Human Guide to Detecting Synthetic AI Imagery

Unclear - no deﬁnitive conclusion on the nature of the content can be drawn from the information at hand. 4. Not GenAI - the content is deﬁnitely not made using generative AI tools. This codebook accounts for a spectrum of synthetic content: moving images, still images, and de...

2022

Pith tools

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

[1] [1]

Mono-Topic Agentic AI Accounts

work page

[2] [2]

Partial GenAI - content includes both synthetic and non-synthetic imagery, for example, synthetic AI images intertwined with stock images. 2.1 photorealistic - content which is labelled as Partial GenAI and consists of a representation of stylistic realism; it imitates the stylistic exactness of a photograph or ﬁlm capture (unlike, e.g., a cartoon)

work page

[3] [3]

Unclear - no deﬁnitive conclusion on the nature of the content can be drawn from the information at hand

work page

[4] [4]

AIF Guidebook: A Human Guide to Detecting Synthetic AI Imagery

Not GenAI - the content is deﬁnitely not made using generative AI tools. 4 “AIF Guidebook: A Human Guide to Detecting Synthetic AI Imagery” AI-Generated Algorithmic Virality | 19 Results of our research Based on our research, synthetic AI content is present to a similar extent across Spain, Germany, and Poland in the search results on TikTok and Instagram...

work page 2025

[5] [5]

Poly-Topic Agentic AI Accounts

work page

[6] [6]

waitlist only

Hybrid Agentic AI Accounts specialize in one convention, usually both in subject matter and formal qualities attempt various formal and subject matter conventions, often following memetic-like trends use both synthetic, stock, and found imagery with AI-generated audio voiceover ﬁg. 16A [link ] ﬁg. 16B [link ] ﬁg. 16C [link ] ﬁg. 16D [link ] ﬁg. 16E [link ...

work page

[7] [7]

GenAI - content consists of generative AI imagery, that is, synthetic visuals (moving and/or still images). 1.1 photorealistic - content which is labelled as GenAI and consists of a representation of stylistic realism; it imitates the stylistic exactness of a photographic or ﬁlm capture (unlike, e.g., a cartoon)

work page

[8] [8]

Partial GenAI - content includes both synthetic and non-synthetic imagery, for example, generative AI images intertwined with stock images. 2.1 photorealistic - content which is labelled as Partial GenAI and consists of a representation of stylistic realism; it imitates the stylistic exactness of a photographic or ﬁlm capture (unlike, e.g., a cartoon)

work page