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arxiv: 2606.29682 · v1 · pith:AVBECS7Gnew · submitted 2026-06-29 · 💻 cs.CY

The Body as Status: Muscularity, Engagement, and Body Image Risk on #GymTok

Magdalayna Curry , Minh Duc Chu , Changhao Yan , Stuart B. Murray , Kristina Lerman , Lindsay E. Young This is my paper

Pith reviewed 2026-06-30 04:37 UTC · model grok-4.3

classification 💻 cs.CY
keywords TikTok#GymTokmuscularitybody imagecontent analysissocial media engagementalgorithmic amplificationfitness culture
0
0 comments X

The pith

TikTok's #GymTok videos with more muscular bodies and higher expert-rated harm to body image receive greater views, likes, shares, and comments.

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

The study performs a content analysis of 2210 #GymTok videos, coding them for themes such as self-objectification, rigid dieting, excessive exercise, and supplement or steroid use, while clinical experts rate each video's perceived harm to viewers' body image and coders assess the muscularity of depicted bodies. Engagement metrics rise in tandem with both muscularity level and harm rating, and masculinity-focused videos draw the highest interaction even when less common. These patterns indicate that the platform's recommendation systems increase the visibility of muscular ideals and potentially risky behaviors. The authors conclude that this dynamic can intensify social comparison and position the muscular body as a marker of status and worth, raising body image risks for boys and young men.

Core claim

Content analysis of 2210 #GymTok videos shows that perceived harm to body image varies by theme, with supplement- and steroid-related videos rated highest, while engagement metrics including views, likes, shares, and comments increase with both higher muscularity of depicted bodies and higher harm ratings; masculinity-focused content produces the strongest engagement overall, implying that recommendation algorithms amplify exposure to muscular ideals and harmful practices.

What carries the argument

Expert-rated perceived harm to body image combined with coded muscularity level of depicted bodies, which together predict higher engagement across views, likes, shares, and comments.

If this is right

  • Videos depicting more muscular bodies receive greater views, likes, shares, and comments.
  • Supplement and steroid content is rated most harmful yet still drives engagement.
  • Masculinity-focused content generates the highest engagement despite lower prevalence.
  • Recommendation systems increase visibility of high-muscularity and high-harm videos, intensifying social comparison processes.

Where Pith is reading between the lines

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

  • Reducing algorithmic promotion of high-harm fitness content could lower aggregate exposure to muscular ideals without changing user search behavior.
  • The same engagement-harm correlation may appear on other short-video platforms that prioritize visual body displays.
  • Viewer-level experiments measuring actual supplement uptake or body dissatisfaction after controlled exposure would test whether the observed amplification translates to measurable risk.

Load-bearing premise

Expert ratings of perceived harm to body image accurately predict real-world viewer risk and observed engagement differences stem from algorithmic amplification rather than unmeasured factors like video quality.

What would settle it

A longitudinal study that tracks changes in body image concerns or supplement use among users whose feeds are experimentally varied in the proportion of high-muscularity or high-harm #GymTok videos.

Figures

Figures reproduced from arXiv: 2606.29682 by Changhao Yan, Kristina Lerman, Lindsay E. Young, Magdalayna Curry, Minh Duc Chu, Stuart B. Murray.

Figure 1
Figure 1. Figure 1: Body Type Classification. Visual Body Scale for Men is a muscularity figural [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of videos classified by Primary Categories and Subcategories [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of Body Types by Primary Content Category [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of perceived harm ratings across content categories. Ratings reflect [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Distribution of perceived harm ratings by depicted body type. Body types were [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Engagement Metrics by Primary Content Category. To account for the highly [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Engagement Metrics by Body Type. To account for highly right-skewed distri [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Engagement Metrics by Perceived Harm Severity Category. Videos were catego [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
read the original abstract

Body image concerns among boys and young men are increasingly oriented toward muscularity, with social media serving as a central context for communicating and evaluating these ideals. While prior research has focused on the thin-ideal, less is known about how the muscular-ideal is represented and reinforced on visual social media platforms. This study examines (1) dominant content themes, (2) perceived harm to body image, and (3) engagement patterns across #GymTok, a muscularity-oriented fitness subculture on TikTok. We conducted a content analysis of 2,210 #GymTok videos annotated by clinical experts across themes like self-objectification, rigid dieting, excessive exercise, supplement and steroid use, and masculinity. Annotators also rated the perceived harm of videos to the viewers' body image, and depicted bodies were coded according to muscularity level. Perceived harm varied across content themes, with supplement- and steroid-related content rated as most harmful. Engagement was positively associated with both muscularity and perceived harm: videos depicting more muscular bodies and those rated as more harmful received greater views, likes, shares, and comments. Although less prevalent, masculinity-focused content generated the highest engagement. These findings suggest that TikTok may not only expose users to muscular ideals and potentially harmful behaviors, but also algorithmically amplify them. By increasing the visibility of highly muscular and harmful content, recommendation systems may intensify social comparison processes, while objectification elevates the muscular body into a marker of status, masculinity, and social worth. Together, these dynamics may contribute to body image risk among boys and young men.

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

4 major / 2 minor

Summary. The paper reports a content analysis of 2,210 #GymTok videos on TikTok. Clinical experts annotated videos for themes (self-objectification, rigid dieting, excessive exercise, supplement/steroid use, masculinity), rated perceived harm to body image, and coded depicted muscularity. Perceived harm was highest for supplement/steroid content; engagement metrics (views, likes, shares, comments) were positively associated with higher muscularity and higher harm ratings, with masculinity-focused content showing highest engagement. The authors conclude that TikTok's algorithms may amplify exposure to muscular ideals and harmful content, intensifying body image risks among boys and young men.

Significance. If the associations survive proper controls and the sampling/measurement details are clarified, the work would add to the literature on muscular-ideal representations on visual platforms and provide platform-specific evidence on engagement patterns. The expert annotation of harm is a methodological strength relative to purely automated approaches, but the observational design limits stronger causal claims about algorithmic amplification.

major comments (4)
  1. [Abstract/Methods] Abstract and Methods: No sampling frame, selection criteria, or collection dates are described for the 2,210 videos. Without this information it is impossible to assess whether the sample is representative of #GymTok or biased toward high-engagement videos, directly affecting the validity of the engagement associations.
  2. [Methods] Methods: No inter-rater reliability statistics (e.g., Cohen's kappa or ICC) are reported for the expert annotations of themes or perceived-harm ratings. This omission undermines confidence in the harm-by-theme and harm-by-engagement findings.
  3. [Results] Results: The reported positive associations between muscularity/perceived harm and engagement metrics are presented without statistical controls for creator follower count, video length, posting time, or hashtag effects. These unmeasured factors could explain the correlations and prevent inference that the algorithm preferentially amplifies harmful content.
  4. [Discussion] Discussion: The claim that 'recommendation systems may intensify social comparison processes' and 'algorithmically amplify' harmful content is not supported by the cross-sectional data. Engagement differences are equally consistent with user-driven selection or production quality differences; temporal or A/B-test evidence would be required to support the causal language.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'algorithmically amplify them' should be qualified as an interpretation rather than a direct finding, to avoid overstating the observational results.
  2. [Results] The manuscript would benefit from a table summarizing theme prevalence, mean harm ratings, and mean engagement by theme to make the patterns easier to evaluate.

Simulated Author's Rebuttal

4 responses · 0 unresolved

Thank you for the opportunity to respond to the referee's comments on our manuscript. We appreciate the constructive feedback and address each major comment below, indicating where revisions will be incorporated.

read point-by-point responses

  1. Referee: [Abstract/Methods] Abstract and Methods: No sampling frame, selection criteria, or collection dates are described for the 2,210 videos. Without this information it is impossible to assess whether the sample is representative of #GymTok or biased toward high-engagement videos, directly affecting the validity of the engagement associations.

    Authors: We agree that these methodological details are necessary for evaluating sample representativeness. In the revised manuscript, we will add a dedicated subsection in Methods describing the sampling frame, including collection dates, the TikTok API or scraping procedure used, inclusion/exclusion criteria for #GymTok videos, and any stratification steps taken to mitigate high-engagement bias. revision: yes

  2. Referee: [Methods] Methods: No inter-rater reliability statistics (e.g., Cohen's kappa or ICC) are reported for the expert annotations of themes or perceived-harm ratings. This omission undermines confidence in the harm-by-theme and harm-by-engagement findings.

    Authors: We thank the referee for noting this omission. Inter-rater reliability was computed during annotation. We will report the statistics (Cohen's kappa for thematic codes and ICC for harm ratings) in the revised Methods section, along with details on how disagreements were resolved. revision: yes

  3. Referee: [Results] Results: The reported positive associations between muscularity/perceived harm and engagement metrics are presented without statistical controls for creator follower count, video length, posting time, or hashtag effects. These unmeasured factors could explain the correlations and prevent inference that the algorithm preferentially amplifies harmful content.

    Authors: We acknowledge that unmeasured variables could confound the observed associations. Our dataset contains partial metadata on video length and hashtags but lacks complete follower counts and posting times. In revision we will add regression models controlling for available variables and will expand the Discussion to explicitly address these limitations and their implications for causal inference. revision: partial

  4. Referee: [Discussion] Discussion: The claim that 'recommendation systems may intensify social comparison processes' and 'algorithmically amplify' harmful content is not supported by the cross-sectional data. Engagement differences are equally consistent with user-driven selection or production quality differences; temporal or A/B-test evidence would be required to support the causal language.

    Authors: We agree that the cross-sectional design precludes strong causal claims regarding algorithmic amplification. We will revise the language throughout the Abstract, Results, and Discussion to describe observed associations and plausible mechanisms while clearly stating the limitations of observational data and removing any definitive causal assertions about recommendation systems. revision: yes

Circularity Check

0 steps flagged

Empirical content analysis with no derivations or self-referential reductions

full rationale

The paper is a cross-sectional content analysis of 2,210 TikTok videos, with expert annotations for themes, muscularity levels, perceived harm ratings, and measured engagement metrics (views, likes, etc.). All reported findings are direct statistical associations from the coded data; the inference that recommendation systems 'algorithmically amplify' harmful content is an interpretive claim from those correlations, not a mathematical derivation, fitted parameter renamed as prediction, or reduction to self-citations. No equations, ansatzes, uniqueness theorems, or load-bearing prior-author citations appear in the text. The study is therefore self-contained against its own observational inputs without circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on two untested domain assumptions about measurement validity and causal interpretation of engagement data; no free parameters or invented entities are introduced.

axioms (2)
  • domain assumption Clinical experts can reliably rate the perceived harm of videos to viewers' body image
    This underpins the harm variation findings and is invoked when linking content themes to body image risk.
  • domain assumption Differences in engagement metrics indicate algorithmic amplification of content
    This interpretation is used to conclude that recommendation systems intensify exposure to harmful muscular content.

pith-pipeline@v0.9.1-grok · 5838 in / 1412 out tokens · 57068 ms · 2026-06-30T04:37:30.564857+00:00 · methodology

discussion (0)

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