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arxiv: 2606.22689 · v1 · pith:LL3VMAV4new · submitted 2026-06-21 · 💻 cs.CY

Is This AI? Longitudinal Analysis of Strategies Used for AI Detection on Two Subreddits

Christina Yeung , Galen Weld , Jaron Mink , Franziska Roesner This is my paper

Pith reviewed 2026-06-26 09:21 UTC · model grok-4.3

classification 💻 cs.CY
keywords AI detection strategiesReddit communitiesgenerative AIlongitudinal analysismental modelsAI-generated contentcrowdsourced identificationonline trends
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The pith

Users in two Reddit communities develop and adapt twelve strategies to detect AI-generated media as models improve.

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

The paper examines how people attempt to identify AI-generated content by studying discussions in two dedicated Reddit communities over more than two years. Through mixed-method analysis of over 13,000 posts and 222,000 comments, it identifies twelve specific strategies such as inspecting fine physical details and spotting patterns typical of AI output. These strategies and the assumptions users make about AI capabilities change in response to advancements in generative models and external social trends. A sympathetic reader would care because the work maps real human approaches to a growing online problem and provides a foundation for practical guidance.

Core claim

Through mixed-method analysis of posts and comments in r/isthisAI and r/RealOrAI, the paper catalogs twelve AI-detection strategies including examining fine-grained physical details, recognizing trends in AI-created content, and assumptions about model capabilities, and demonstrates that these strategies and associated mental models change over time in line with evolving AI capabilities and social trends.

What carries the argument

Catalog of twelve AI-detection strategies extracted from longitudinal analysis of community discussions and their temporal shifts.

If this is right

  • The catalog of strategies can serve as a basis for user-facing guidance on identifying AI content.
  • The documented shifts indicate that detection approaches must adapt as generative AI capabilities advance.
  • Assumptions users hold about model limitations influence which strategies gain popularity.
  • Crowdsourced analysis in these communities reveals collective mental models for distinguishing real and generated media.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar analysis on other platforms could reveal whether the twelve strategies generalize or vary by community type.
  • The observed evolution suggests that static AI detectors risk becoming less effective without updates tied to current model trends.
  • This catalog could support development of AI literacy tools that teach people the most reliable current strategies.

Load-bearing premise

The discussions and strategies observed in these two specific subreddits represent the broader strategies and mental models that people use to detect AI-generated media outside these communities.

What would settle it

A study of detection discussions on other platforms or through direct surveys that reveals substantially different strategies or no comparable temporal shifts would challenge the findings.

Figures

Figures reproduced from arXiv: 2606.22689 by Christina Yeung, Franziska Roesner, Galen Weld, Jaron Mink.

Figure 1
Figure 1. Figure 1: AI Strategies over time: The proportion of each sub-code across our observed time periods. [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Posts and comments in our dataset from r/RealOrAI and r/isthisAI, from July 7, 2023 to March 31, 2026. [PITH_FULL_IMAGE:figures/full_fig_p019_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Centered average (over 90 days) of comments in the AI [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The relative difference in strategies in comments discussing political-related posts, versus all other posts [PITH_FULL_IMAGE:figures/full_fig_p021_4.png] view at source ↗
read the original abstract

As AI-generated content (e.g., "slop") becomes more prevalent online, people are developing strategies to attempt to identify it (or, conversely, to gain confidence that something is not AI-generated). What strategies are people using, and how are they changing over time as generative AI models themselves change? In this work, we catalog and analyze 2 years and 8 months of the AI detection strategies discussed by users of two popular Reddit communities (r/isthisAI and r/RealOrAI) that use the wisdom of crowds to identify AI-generated media. Through a mixed-method analysis of 13,098 posts and 222,060 comments within these communities, we catalog and analyze the prevalence of 12 AI-detection strategies, including examining fine-grained physical details, recognizing trends in AI-created content, and the assumptions people make about what models are capable of producing. Furthermore, we find that these strategies and mental models shift over time in accordance with changing AI capabilities and in response to online social trends. By systematically cataloging users' AI detection strategies, we lay the groundwork for user-facing guidance and future research.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The manuscript reports a mixed-method longitudinal study of AI detection strategies in two Reddit subreddits (r/isthisAI and r/RealOrAI) spanning 2 years and 8 months. Analyzing 13,098 posts and 222,060 comments, the authors catalog 12 strategies (e.g., examining physical details, recognizing AI trends, assumptions about model capabilities), quantify their prevalence, and document temporal shifts aligned with AI advancements and social trends. The work positions itself as providing groundwork for user-facing guidance and future research.

Significance. If the reported findings are robustly supported by the underlying data and methods, the paper offers a substantial empirical contribution by systematically documenting user strategies for identifying AI-generated content in active online communities. The scale of the dataset and the longitudinal design are notable strengths that enable observation of evolving mental models. This could inform the development of detection tools and educational resources, though the scope is limited to these specific communities.

major comments (3)
  1. [Methods] The mixed-method analysis is described at a high level, but the Methods section provides no details on the coding scheme for identifying the 12 strategies, inter-rater reliability metrics, the sampling frame for selecting the 13,098 posts and 222,060 comments, or the statistical tests used to establish temporal shifts. These omissions prevent verification that the data support the prevalence and change claims.
  2. [Results] Claims of strategy prevalence and shifts over time (in accordance with AI capabilities and social trends) are central to the abstract and results, yet no tables, figures, or quantitative measures (e.g., percentages with confidence intervals or tests for trend significance) are referenced to ground these observations.
  3. [Discussion] The Discussion extends the catalog to 'user-facing guidance' and broader mental models, but this rests on the untested assumption that patterns from these two self-selected subreddits generalize; no cross-platform sampling, demographic controls, or comparison to other populations is provided.
minor comments (1)
  1. Figure captions and table headings should explicitly state the time periods and subreddit breakdowns to allow readers to assess the longitudinal claims independently.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments. We agree that the Methods and Results sections require substantial expansion with specific details and quantitative support, and we will revise the manuscript accordingly. For the Discussion, we will clarify scope and limitations without overclaiming generalizability.

read point-by-point responses

  1. Referee: [Methods] The mixed-method analysis is described at a high level, but the Methods section provides no details on the coding scheme for identifying the 12 strategies, inter-rater reliability metrics, the sampling frame for selecting the 13,098 posts and 222,060 comments, or the statistical tests used to establish temporal shifts. These omissions prevent verification that the data support the prevalence and change claims.

    Authors: We agree that these details are missing from the current Methods section. In revision we will add: (1) a full description of the iterative coding scheme development and how the 12 strategies were derived from the data; (2) inter-rater reliability statistics (e.g., Cohen’s kappa or percentage agreement) from the coding process; (3) explicit sampling details (the dataset comprises the complete set of posts and comments from the two subreddits over the 2-year-8-month window, with inclusion criteria); and (4) the specific statistical procedures (trend tests, time-series comparisons) used to evaluate temporal shifts. revision: yes

  2. Referee: [Results] Claims of strategy prevalence and shifts over time (in accordance with AI capabilities and social trends) are central to the abstract and results, yet no tables, figures, or quantitative measures (e.g., percentages with confidence intervals or tests for trend significance) are referenced to ground these observations.

    Authors: This observation is correct. Although the text describes prevalence and shifts, supporting numbers, tables, and figures are absent. We will insert new tables reporting strategy frequencies (percentages) overall and by time period, figures showing temporal trends, and the results of statistical tests (including p-values or confidence intervals) that underpin the claims of change aligned with AI capability shifts. revision: yes

  3. Referee: [Discussion] The Discussion extends the catalog to 'user-facing guidance' and broader mental models, but this rests on the untested assumption that patterns from these two self-selected subreddits generalize; no cross-platform sampling, demographic controls, or comparison to other populations is provided.

    Authors: We accept the critique on scope. The study is deliberately limited to these two communities; we do not possess cross-platform or demographic data. In revision we will explicitly state this limitation, remove or qualify any language implying broader generalizability, and reframe the contribution as groundwork specific to these active subreddits while recommending future comparative work. revision: partial

Circularity Check

0 steps flagged

Empirical content analysis of Reddit data exhibits no circularity

full rationale

The paper conducts a mixed-method analysis of 13,098 posts and 222,060 comments from two subreddits to catalog 12 AI-detection strategies and their temporal shifts. No equations, fitted parameters, predictions, or derivations are present that could reduce to inputs by construction. Claims rest directly on observed comment patterns rather than self-citation chains, ansatzes, or renamed known results. The work is self-contained as an empirical cataloging exercise with no load-bearing steps that equate to their own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that the two chosen subreddits supply representative data on detection strategies; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Discussions in r/isthisAI and r/RealOrAI reflect authentic user strategies for detecting AI-generated media rather than performative or community-specific biases.
    The paper frames these communities as using 'wisdom of crowds' to identify AI media and treats their posts/comments as the basis for cataloging general strategies.

pith-pipeline@v0.9.1-grok · 5736 in / 1478 out tokens · 39061 ms · 2026-06-26T09:21:43.390893+00:00 · methodology

discussion (0)

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    In terms of adverse impacts, we recognize that AI companies or AI-generated content creators who are acting in bad faith (or simply want to improve the “quality” of their content by some metrics) could use our findings to make AI-generated content harder to detect. However, this is an ongoing arms race that is likely to be minimally impacted by our paper....

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    Read the comment carefully

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    Assign one or more category labels from the taxonomy above

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    Quote the specific phrase(s) from the comment that support each label

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    A quote may be reused across labels if necessary

    Each label must have at least one supporting quote. A quote may be reused across labels if necessary

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    If the label is IRRELEVANT, return an empty evidence array

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    labels": [

    Return only valid JSON, as formatted in ##OUTPUT FORMAT. Do not include any explanation, preamble, or markdown formatting outside of the JSON object. ## OUTPUT FORMAT Return your answer as a JSON object using this exact structure: { "labels": ["LABEL 1", ‘‘LABEL 2’’], "evidence": [ { "label": "LABEL 1", "quote": "exact quote from the comment" }, { "label"...

    2023