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arxiv: 2605.20457 · v1 · pith:NEGBETHZnew · submitted 2026-05-19 · 💻 cs.SI

The Structure and Dynamics of the Online MAHA-sphere

Sabit Ahmed , Subigya Nepal , Henry Kautz This is my paper

Pith reviewed 2026-05-21 06:16 UTC · model grok-4.3

classification 💻 cs.SI
keywords MAHA movementonline communitiesReddit analysisvaccine skepticismopinion dynamicsstance classificationnetwork structurepublic health
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The pith

MAHA-aligned users bundle health and anti-science themes into coherent networks, while anti-MAHA users show no bundling beyond chance.

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

The paper examines six years of Reddit posts across twelve MAHA-adjacent themes that range from mainstream topics like exercise and whole foods to contentious ones such as vaccines, masks, fluoride, and GMOs. It uses a tree-based few-shot LLM pipeline to assign pro, anti, or neutral stances to users and then tracks how those stances interact across themes and shift over time. MAHA supporters display tight cross-theme connections and structured network patterns, whereas opponents do not exceed random chance in their bundling. The analysis also shows that users posting against fluoride and masks during the pandemic later moved into anti-vaccination content and then broader anti-science narratives.

Core claim

MAHA-aligned users exhibit strong cross-theme bundling and coherent network structure, whereas anti-MAHA users do not bundle beyond chance. During the pandemic, anti-fluoride and anti-mask posters transitioned into anti-vaccination posts, and later moved to broader anti-science narratives, suggesting that vaccine skepticism may serve as an entry point into wider anti-science engagement. Pro- and anti-MAHA communities also exhibit distinct psycholinguistic profiles, reflecting deeper ideological and rhetorical divides.

What carries the argument

A tree-based few-shot LLM pipeline that classifies user stances (pro, anti, neutral) across twelve MAHA-adjacent themes, followed by user-level opinion scores that reveal cross-theme interactions and temporal shifts.

If this is right

  • MAHA users cluster in a few mainstream subreddits yet post across a wide ecosystem of related communities.
  • Vaccine skepticism can function as an entry point that leads users toward wider anti-science narratives.
  • Pro- and anti-MAHA groups maintain distinct psycholinguistic profiles that mark deeper rhetorical divides.
  • Opinion shifts unfold over time, with specific health concerns evolving into broader skepticism during the pandemic.

Where Pith is reading between the lines

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

  • Public health efforts might target early skepticism points such as fluoride or mask concerns to interrupt later transitions into vaccine and science distrust.
  • The coherent network structure among MAHA users could make those communities more resistant to outside messaging than the less bundled anti-MAHA side.
  • Similar cross-theme bundling patterns may appear in other online ideological movements and could be tested with the same stance-classification approach.

Load-bearing premise

The tree-based few-shot LLM pipeline accurately classifies user stances across the twelve themes without substantial bias or misclassification that would distort the bundling and transition findings.

What would settle it

Re-annotating a random sample of posts with multiple human coders and finding low agreement with the LLM stance labels, particularly on the identified transitions from anti-fluoride or anti-mask to anti-vaccine content.

Figures

Figures reproduced from arXiv: 2605.20457 by Henry Kautz, Sabit Ahmed, Subigya Nepal.

Figure 1
Figure 1. Figure 1: Cross-theme co-occurrence among MAHA (M) and anti-MAHA (AM) users. Both heatmaps show the MAHA theme [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distance-based layout (Kamada–Kawai) of the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Hierarchical clustering (UPGMA) of MAHA themes. The log-scale x-axis shows the merge distance (de￾rived from Eq. 5) at which each pair or cluster joins; smaller values indicate stronger user overlap. Node size is propor￾tional to the number of MAHA users in each theme. ber of subreddits host thousands of MAHA users each, while the majority host only a few. While users themselves are clustered on a few subr… view at source ↗
Figure 4
Figure 4. Figure 4: (A) Cumulative share of MAHA users captured by top-ranked subreddits, where subreddits are ranked by MAHA [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Cumulative share of MAHA posts captured by top [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Cohen’s d effect sizes for 18 selected LIWC-22 psycholinguistic dimensions comparing propensity-matched MAHA and anti-MAHA users, with themes as columns and LIWC dimensions as rows. Positive values (red) indicate higher scores among MAHA users; negative values (blue) indicate higher scores among anti-MAHA users. ∗p < 0.05, ∗∗p < 10−5 , ∗∗∗p < 10−20 , † p < 10−100 (BH-adjusted). tinct ways of writing within… view at source ↗
read the original abstract

The "Make America Healthy Again" (MAHA) movement has created a complex ideological ecosystem within online communities, where advocacy for healthier lifestyles and whole-food diets coexists with vaccine skepticism and anti-science attitudes. Understanding how these interconnected beliefs interact, overlap, and evolve is critical for public health communication and intervention. We uncover the functional overlaps, network structures, engagement patterns, opinion dynamics, and linguistic differences across the full spectrum of MAHA ideologies. Using large-scale Reddit data spanning six years, we identified 12 MAHA-adjacent themes, including mainstream topics such as exercise, whole food, and screen use, as well as contentious topics such as vaccines, masks, GMOs, fluoride, and others. We developed a tree-based few-shot LLM pipeline to classify stances (pro, anti, neutral) across all themes, then computed user-level opinion scores to examine cross-theme interactions and opinion shifts over time. We find that MAHA-aligned users exhibit strong cross-theme bundling and coherent network structure, whereas anti-MAHA users do not bundle beyond chance. MAHA users cluster in a few mainstream subreddits, but post in a wide ecosystem of MAHA-related communities. During the pandemic, anti-fluoride and anti-mask posters transitioned into anti-vaccination posts, and later moved to broader anti-science narratives, suggesting that vaccine skepticism may serve as an entry point into wider anti-science engagement. Pro- and anti-MAHA communities also exhibit distinct psycholinguistic profiles, reflecting deeper ideological and rhetorical divides.

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

1 major / 2 minor

Summary. This manuscript analyzes the structure and dynamics of the online 'Make America Healthy Again' (MAHA) movement using six years of Reddit data. The authors identify 12 themes, employ a tree-based few-shot LLM pipeline to assign stance labels (pro, anti, neutral) to posts, compute user-level opinion scores, and investigate cross-theme bundling, network coherence, engagement patterns, and temporal transitions. They report that MAHA-aligned users show strong bundling and structured networks unlike anti-MAHA users, MAHA users cluster in mainstream subreddits but engage broadly, and there are specific transitions during the pandemic from anti-fluoride and anti-mask to anti-vaccination and then anti-science narratives, alongside distinct linguistic profiles.

Significance. If the stance classifications prove reliable, the work would advance understanding of ideological bundling and gateway effects in online health and science-skeptic communities, with potential value for public health monitoring and communication strategies. The multi-theme, longitudinal Reddit analysis offers a broad empirical map of how mainstream wellness topics intersect with contentious ones.

major comments (1)
  1. [Methods] Methods (as described): The tree-based few-shot LLM pipeline for classifying post stances across the 12 themes reports no validation details, human-annotated test set, accuracy/precision/recall metrics, or error analysis. All headline results on cross-theme bundling, network structure, and pandemic-era transitions (anti-fluoride/anti-mask to anti-vax to anti-science) are direct aggregates of these labels; without quantitative checks on classifier performance or bias, systematic mislabeling could artifactually produce the reported MAHA vs. anti-MAHA differences.
minor comments (2)
  1. [Abstract] Abstract: The data collection period is described only as 'six years' without exact dates, total post or user counts, or subreddit sampling criteria, which would help readers assess scale and potential biases.
  2. [Methods] The description of user-level opinion scores and bundling metrics would benefit from explicit formulas or pseudocode showing how per-post labels are aggregated and compared to a null model of chance co-occurrence.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript examining the structure and dynamics of the online MAHA movement. The primary methodological concern raised is the lack of reported validation for the stance classification pipeline. We address this point directly below and commit to revisions that enhance transparency without altering the core findings or interpretations.

read point-by-point responses

  1. Referee: [Methods] Methods (as described): The tree-based few-shot LLM pipeline for classifying post stances across the 12 themes reports no validation details, human-annotated test set, accuracy/precision/recall metrics, or error analysis. All headline results on cross-theme bundling, network structure, and pandemic-era transitions (anti-fluoride/anti-mask to anti-vax to anti-science) are direct aggregates of these labels; without quantitative checks on classifier performance or bias, systematic mislabeling could artifactually produce the reported MAHA vs. anti-MAHA differences.

    Authors: We agree that the current manuscript does not include explicit quantitative validation details, a human-annotated test set, performance metrics, or error analysis for the tree-based few-shot LLM pipeline. This is a fair and substantive observation, as such information is necessary to evaluate the reliability of the stance labels underlying the reported patterns of cross-theme bundling, network coherence, and temporal transitions. In the revised version we will add a dedicated Methods subsection that describes the validation process in full. This will include: (1) construction of a human-annotated gold-standard test set sampled across all 12 themes and stance categories; (2) annotation protocol and inter-annotator agreement statistics; (3) classifier performance metrics (accuracy, precision, recall, and F1 scores) broken down by theme and stance; and (4) a qualitative error analysis identifying common failure modes together with steps taken during prompt design and few-shot example selection to reduce systematic bias. While the large data volume and the fact that anti-MAHA users show no bundling beyond chance provide indirect support that any mislabeling is not uniformly directional, we recognize that these arguments are insufficient without the requested quantitative checks. We will therefore incorporate the validation results and make the annotated test set available to readers. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical pipeline on external data yields independent aggregates

full rationale

The paper collects six years of Reddit posts, applies a tree-based few-shot LLM classifier to assign per-post stance labels (pro/anti/neutral) across 12 themes, then aggregates those labels into user-level opinion scores for bundling, network, and transition analyses. No equations, fitted parameters, or self-citations are invoked that reduce the reported cross-theme bundling, coherent networks, or pandemic-era transitions back to the classifier outputs by construction; the findings remain direct statistical summaries of the labeled external corpus rather than tautological restatements of inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical social media study; no explicit free parameters, mathematical axioms, or newly invented entities are stated in the provided abstract.

pith-pipeline@v0.9.0 · 5805 in / 1082 out tokens · 27850 ms · 2026-05-21T06:16:55.689111+00:00 · methodology

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