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arxiv: 2605.20050 · v1 · pith:H3UXMDRQnew · submitted 2026-05-19 · 💻 cs.CL

Language Mutations Sustain the Persistences of Conspiracy Theories on Social Media

Calvin Yixiang Cheng , Dorian Quelle , Scott A. Hale This is my paper

Pith reviewed 2026-05-20 05:42 UTC · model grok-4.3

classification 💻 cs.CL
keywords conspiracy theoriessocial medialanguage mutationssemantic changesurvival analysispsycholinguistic featurescontent moderationpersistence
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The pith

Conspiracy claims with greater semantic mutations have substantially longer lifespans on social media.

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

This paper examines how language changes influence the staying power of conspiracy theories on platforms such as X. Drawing on a three-year collection of related posts and combining linguistic analysis with survival models, the authors establish that posts showing more semantic mutations tend to remain active for longer periods. Mutations affecting pronouns, social reference words, cognitive process terms, and risk- or health-related vocabulary are tied to these extended durations, as are shifts in descriptions of actors, actions, and targets. Qualitative review points to simplification and assimilation as the main ways these mutations occur. The work implies that efforts to moderate content should address the stable core of a claim rather than every possible rewording.

Core claim

Conspiracy claims with greater semantic mutations have substantially longer lifespans. Mutations in psycholinguistic properties, including pronouns, social reference words, cognitive process terms, risk- and health-related vocabularies, are associated with extended lifespans. Mutations in actor, action and target categories are associated with longer lifespans as well. Qualitative analysis identifies two predominant mutation patterns: simplification and assimilation, at both linguistic and AAT structural levels.

What carries the argument

Semantic mutations in conspiracy posts, measured by shifts in psycholinguistic categories and actor-action-target structures, tracked through computational linguistic analysis and survival modelling on a three-year X dataset.

If this is right

  • Conspiracy claims that change in pronouns, social references, and cognitive terms persist longer than stable versions.
  • Mutations in how actors, actions, and targets are framed also extend the active period of a claim.
  • Content moderation should target core claims to limit the persistence of their variations.

Where Pith is reading between the lines

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

  • The same mutation-driven persistence may apply to other categories of online claims such as health misinformation.
  • Detection systems could monitor ongoing linguistic adaptation as a signal for early intervention.
  • Experiments that limit visible mutations, for instance through targeted replies, could test whether lifespan shortens.

Load-bearing premise

The three-year X dataset and survival modelling accurately capture post lifespans and semantic mutations without major biases from platform algorithms, user engagement patterns, or incomplete sampling of conspiracy discussions.

What would settle it

A controlled collection of conspiracy posts where higher rates of semantic mutation show no increase in measured lifespan compared with lower-mutation versions.

Figures

Figures reproduced from arXiv: 2605.20050 by Calvin Yixiang Cheng, Dorian Quelle, Scott A. Hale.

Figure 1
Figure 1. Figure 1: The workflow of data collection. 4.2. Conspiracy Claim Matching We examine the persistence at the claim level of conspir￾acy theories. A claim refer to discrete statements or asser￾tions that convey one conspiratorial idea. Different posts may refer to the same underlying conspiracy claim. Following prior research on fact-checking, we conceptualize this as a claim-matching task, in which semantically simil… view at source ↗
Figure 2
Figure 2. Figure 2: Plot a shows average cosine similarity of indirectly connected conspiracy posts over a hundred days. Time measures the number of days between the dates of two clustered posts not directly connected. Shading shows the standard error. Panel b, c show the relationship between claim lifespan and their average cosine similarity and standard deviation given the preset threshold of 0.88. The standard deviation (S… view at source ↗
Figure 3
Figure 3. Figure 3: The time ratios (TR) of semantic drift in Weibull Accelerated Failure Time (AFT) model. The x-axis represents the TR with 95% confidence intervals, y-axis corresponds to covariates in the Weibull AFT model. Asterisk represents the significance of variables (* 𝑝 < 0.05, ** 𝑝 < 0.01, *** 𝑝 < 0.001). The horizontal reference line indicates no effect. 𝑇 𝑅 > 1 represents longer survival times (decelerated failu… view at source ↗
Figure 4
Figure 4. Figure 4: Kaplan-Meier survival curves for psycholinguistic properties. The x-axis is days, and the y-axis represents the probability a claim is still transmitting after 𝑡 days. The vertical dash lines indicate the 20% survival time for both groups. The log-rank tests shows the statistic difference between two curves in each plot [PITH_FULL_IMAGE:figures/full_fig_p021_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The time ratios (TR) of psycholinguistic properties in Weibull Accelerated Failure Time (AFT) model. Plot a shows the binary changes, and Plot b illustrates the standard deviation of percentage change in conspiracy claims. The x-axis represents the TR with 95% confidence intervals, y￾axis corresponds to covariates in the Weibull AFT model. Asterisk represents the significance of variables (* 𝑝 < 0.05, ** 𝑝… view at source ↗
Figure 6
Figure 6. Figure 6 [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative analysis of significant psycholinguistic elements change within conspiracy relevant claims. Cheng, Quelle, and Hale: Preprint submitted to Elsevier Page 22 of 19 [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative analysis of significant actor-action-target elements change within conspiracy relevant claims. Cheng, Quelle, and Hale: Preprint submitted to Elsevier Page 23 of 19 [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Inter- and Intra Cluster Distances. Highlighted bars show the minimized intra-cluster distance and the approxi￾mately maximized inter-cluster distance. Cheng, Quelle, and Hale: Preprint submitted to Elsevier Page 24 of 19 [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Kaplan-Meier survival curves for psycholinguistic properties with 0.25 threshold. The x-axis is days, and the y-axis represents the probability a claim is still transmitting after 𝑡 days. The vertical dash lines indicate the 20% survival time for both groups. The log-rank tests shows the statistic difference between two curves in each plot [PITH_FULL_IMAGE:figures/full_fig_p025_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Kaplan-Meier survival curves for psycholinguistic properties with 0.75 threshold. The x-axis is days, and the y-axis represents the probability a claim is still transmitting after 𝑡 days. The vertical dash lines indicate the 20% survival time for both groups. The log-rank tests shows the statistic difference between two curves in each plot. Cheng, Quelle, and Hale: Preprint submitted to Elsevier Page 25 o… view at source ↗
Figure 12
Figure 12. Figure 12: The time ratios (TR) of psycholinguistic properties in Weibull Accelerated Failure Time (AFT) model with 0.25 threshold. Plot a shows the binary changes, and Plot b illustrates the standard deviation of percentage change in conspiracy claims. The x-axis represents the TR with 95% confidence intervals, y-axis corresponds to covariates in the Weibull AFT model. Asterisk represents the significance of variab… view at source ↗
Figure 14
Figure 14. Figure 14: The inter-cluster variance and the Silhouette score against the number of clusters for actors, actions and targets [PITH_FULL_IMAGE:figures/full_fig_p026_14.png] view at source ↗
Figure 13
Figure 13. Figure 13: The time ratios (TR) of psycholinguistic properties in Weibull Accelerated Failure Time (AFT) model with 0.75 threshold. Plot a shows the binary changes, and Plot b illustrates the standard deviation of percentage change in conspiracy claims. The x-axis represents the TR with 95% confidence intervals, y-axis corresponds to covariates in the Weibull AFT model. Asterisk represents the significance of variab… view at source ↗
Figure 16
Figure 16. Figure 16: The relationship between conspiracy claims’ lifespan and their average cosine similarity, standard deviation, and entropy given different cosine similarity thresholds. Cheng, Quelle, and Hale: Preprint submitted to Elsevier Page 27 of 19 [PITH_FULL_IMAGE:figures/full_fig_p027_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Average cosine similarity of indirectly connected conspiracy posts over time in the first year. Time measures the number of days between the dates of two clustered posts not directly connected. Shading shows the standard error [PITH_FULL_IMAGE:figures/full_fig_p028_17.png] view at source ↗
Figure 19
Figure 19. Figure 19: The time ratios (TR) of semantic drift in Weibull Accelerated Failure Time (AFT) model. The x-axis represents the TR with 95% confidence intervals, y-axis corresponds to covariates in the Weibull AFT model. Asterisk represents the significance of variables (* 𝑝 < 0.05, ** 𝑝 < 0.01, *** 𝑝 < 0.001). The horizontal reference line indicates no effect. 𝑇 𝑅 > 1 represents longer survival times (decelerated fail… view at source ↗
Figure 18
Figure 18. Figure 18: Number of pairs by time. The y axis represent the number of pairs at certain time point within the cluster at a certain threshold [PITH_FULL_IMAGE:figures/full_fig_p028_18.png] view at source ↗
read the original abstract

This study investigates how language mutations affect the persistent diffusion of conspiracy theories on social media. Drawing on a three-year dataset of conspiracy-related posts from X, and applying computational linguistic analysis alongside survival modelling, we find that conspiracy claims with greater semantic mutations have substantially longer lifespans. Mutations in psycholinguistic properties, including pronouns, social reference words, cognitive process terms, risk- and health- related vocabularies, are associated with extended lifespans. Mutations in actor, action and target (AAT) categories are associated with longer lifespans as well. Qualitative analysis identifies two predominant mutation patterns: simplification and assimilation, at both linguistic and AAT structural levels. Taken together, the results advance our understanding of how language mutations contribute to conspiracy persistence online and shed lights on longitudinal content moderation strategies. We argue that content moderation should consider the mutability of conspiracy claims and focus on the core claims that can address their potential variations.

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

2 major / 3 minor

Summary. This paper investigates how language mutations affect the persistent diffusion of conspiracy theories on social media. Drawing on a three-year dataset of conspiracy-related posts from X and applying computational linguistic analysis alongside survival modelling, it finds that conspiracy claims with greater semantic mutations have substantially longer lifespans. Mutations in psycholinguistic properties (pronouns, social reference words, cognitive process terms, risk- and health-related vocabularies) and in actor-action-target (AAT) categories are associated with extended lifespans. Qualitative analysis identifies simplification and assimilation as predominant mutation patterns at both linguistic and structural levels, with implications for longitudinal content moderation that targets core claims rather than variants.

Significance. If the central associations hold after addressing potential confounds, the work provides a data-driven link between linguistic adaptability and online conspiracy persistence, combining survival analysis with psycholinguistic feature tracking and qualitative pattern identification. This could inform more effective moderation strategies and extends computational social science approaches to misinformation dynamics.

major comments (2)
  1. [Survival modelling and dataset construction] § on survival modelling and dataset construction: the models do not include explicit robustness checks such as inverse-probability weighting by initial engagement, stratification by follower count, or sensitivity analyses for right-censoring and API sampling limits. Because the central claim equates observed post lifespans with intrinsic mutation-driven persistence, unaddressed platform algorithm biases (which may correlate with the studied psycholinguistic markers) constitute a load-bearing threat to causal interpretation.
  2. [Methods section] Methods section: the manuscript provides no details on sample size, sampling strategy for identifying conspiracy posts, exact quantification of semantic mutations (e.g., distance metrics or thresholds), statistical controls, or error handling. These omissions prevent verification that the reported positive associations between mutation rates and lifespans are statistically supported rather than artifacts of data construction.
minor comments (3)
  1. [Title] Title: 'Persistences' is grammatically incorrect and should read 'Persistence'.
  2. [Abstract] Abstract: 'shed lights on' should be corrected to 'shed light on'.
  3. [Throughout] Notation and terminology: ensure consistent distinction between 'semantic mutations' and 'language mutations' throughout; define AAT categories explicitly on first use.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their constructive feedback, which helps improve the clarity and robustness of our analysis. We respond to each major comment in turn.

read point-by-point responses

  1. Referee: [Survival modelling and dataset construction] § on survival modelling and dataset construction: the models do not include explicit robustness checks such as inverse-probability weighting by initial engagement, stratification by follower count, or sensitivity analyses for right-censoring and API sampling limits. Because the central claim equates observed post lifespans with intrinsic mutation-driven persistence, unaddressed platform algorithm biases (which may correlate with the studied psycholinguistic markers) constitute a load-bearing threat to causal interpretation.

    Authors: We concur that robustness checks are essential to mitigate concerns about platform biases and to support the interpretation of mutation-driven persistence. Accordingly, we will add sensitivity analyses for right-censoring and API sampling limits in the revised manuscript. Regrettably, follower counts and initial engagement metrics are not available in the dataset, precluding inverse-probability weighting and stratification by follower count. We will revise the manuscript to emphasize that the findings represent associations and to discuss these data limitations explicitly. revision: partial

  2. Referee: [Methods section] Methods section: the manuscript provides no details on sample size, sampling strategy for identifying conspiracy posts, exact quantification of semantic mutations (e.g., distance metrics or thresholds), statistical controls, or error handling. These omissions prevent verification that the reported positive associations between mutation rates and lifespans are statistically supported rather than artifacts of data construction.

    Authors: We apologize for the insufficient detail provided in the methods section of the submitted manuscript. In the revision, we will include the sample size, describe the sampling strategy for conspiracy posts, specify the distance metrics and thresholds used for semantic mutation quantification, detail the statistical controls, and outline the error handling procedures to allow for full verification of the results. revision: yes

standing simulated objections not resolved
  • Data limitations prevent conducting inverse-probability weighting by initial engagement and stratification by follower count.

Circularity Check

0 steps flagged

No circularity: empirical associations derived from external dataset observations

full rationale

The paper conducts a data-driven empirical study on a three-year X dataset using computational linguistic analysis (psycholinguistic properties and AAT categories) paired with survival modeling. The central claims are associations between observed semantic mutations and measured post lifespans, with no first-principles derivation chain, no parameters fitted to a subset then re-presented as predictions, and no load-bearing self-citations or uniqueness theorems. All reported results rest on external data observations and standard statistical methods rather than reducing to the paper's own inputs by construction. This is a normal, self-contained empirical finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review yields minimal ledger entries; no explicit free parameters, invented entities, or ad-hoc axioms are stated.

axioms (1)
  • domain assumption The collected X posts form a representative sample of conspiracy theory diffusion without significant platform-induced selection effects.
    Required for survival modelling results to generalize to online persistence.

pith-pipeline@v0.9.0 · 5688 in / 1104 out tokens · 34217 ms · 2026-05-20T05:42:08.607684+00:00 · methodology

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