Pith. sign in

REVIEW 3 major objections 1 minor 17 references

Change My View? The Dynamics of Persuasion and Polarization in Online Discourse

T0 review · 3 major / 1 minor · reviewed 2026-05-12 · grok-4.3

Challenge this review Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Concession and empathy in online debates substantially increase the prospect of belief change while frontal refutation and attacks diminish it.

desk verdict The paper links concession and empathy to higher belief-change rates on CMV via LLM baseline and strategy coding, but the measurements lack any visible validation. read the letter →

arxiv 2605.08383 v1 submitted 2026-05-08 cs.CL

David Freeborn , Malihe Alikani , Anthony Sicilia This is my paper · ORCID
classification cs.CL
keywords persuasionbeliefchangerhetoricalstrategiesonlinediscourseconcessionempathymyviewreddit
checked against Cost.FunctionalEquationbranch_selection
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

The paper uses large language models to forecast belief change in r/ChangeMyView debates and codes replies for rhetorical strategies. Adding these features to the baseline predictions markedly improves accuracy. Concessions and empathetic alignment raise the odds of view change, but direct challenges, credibility attacks, and deflection lower them. This pattern shows that relational aspects of arguing matter alongside the evidence for persuasion success.

What carries the argument

Hybrid machine-assisted coding of ten rhetorical strategies, including concession, empathy, logical challenge, and credibility appeals, to enhance LLM baseline predictions of belief revision.

What would settle it

New debate data where adding the rhetorical strategy codes fails to improve prediction of belief change over the LLM baseline alone, or where the direction of effects for concession and refutation does not match the observed pattern.

Watch

Extended reading notes

Core claim

Large language models forecast whether belief revision will occur in ChangeMyView threads, and coding each reply for ten rhetorical strategies reveals that concession and empathetic alignment substantially increase the prospect of belief change, whereas frontal refutation, credibility attacks, and topic deflection diminish it. The findings indicate that effective public reasoning depends as much on relational framing as on evidential content.

Load-bearing premise

The LLM's probabilistic estimates halfway through each discussion provide an unbiased conversational baseline and that the hybrid machine-assisted coding of the ten rhetorical strategies is reliable and free of systematic bias.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 1 minor

Summary. The paper analyzes a corpus of debates from Reddit's r/ChangeMyView using large language models. LLMs generate probabilistic forecasts halfway through each thread to serve as a baseline for whether a public belief change (delta) will occur. Each reply is then labeled via a hybrid machine-assisted procedure for ten rhetorical strategies. The central claim is that incorporating these strategy features substantially improves predictive power over the baseline and reveals a consistent directional pattern: concession and empathetic alignment increase the likelihood of belief change, while frontal refutation, credibility attacks, and topic deflection decrease it.

Significance. If the empirical results hold after validation, the work would offer a scalable, data-driven contribution to computational argumentation and social science by demonstrating that relational framing matters as much as evidential content in online persuasion. The use of public, verifiable belief-change signals from a large real-world corpus is a methodological strength that enables falsifiable tests of normative dialogue theories. The LLM-assisted approach could be extended to other platforms, though its current impact is constrained by the absence of reported quantitative metrics and validation.

major comments (3)
  1. [Abstract] Abstract: the claim that adding the coded features 'markedly improves predictive power' is presented without any sample sizes, performance metrics (e.g., AUC, accuracy delta, or log-likelihood improvement), confidence intervals, or statistical tests, rendering the central empirical result impossible to evaluate.
  2. [Methods / Coding procedure] Hybrid coding procedure (described after the baseline forecast): the ten rhetorical strategies are assigned via LLM with no reported inter-annotator agreement against human coders, prompt details, few-shot examples, or systematic bias audit. Because the headline directional effects (concession/empathy positive; refutation/credibility attacks negative) rest entirely on these labels, the absence of validation is load-bearing for the pattern reported.
  3. [Baseline forecast] Baseline forecast section: the LLM's halfway-through probabilistic estimates are used as the conversational baseline without any human validation, calibration check against actual delta rates, or analysis of whether the LLM systematically over- or under-predicts in threads that later receive deltas. This assumption directly affects the claimed incremental lift from the strategy features.
minor comments (1)
  1. [Abstract] The abstract lists example strategies but does not enumerate all ten; adding the full list would improve readability without altering the claims.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thoughtful and constructive review. We agree that the manuscript would benefit from greater transparency in quantitative metrics, validation procedures, and calibration checks. We will incorporate these elements in a major revision. Our responses to each major comment are provided below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that adding the coded features 'markedly improves predictive power' is presented without any sample sizes, performance metrics (e.g., AUC, accuracy delta, or log-likelihood improvement), confidence intervals, or statistical tests, rendering the central empirical result impossible to evaluate.

    Authors: We agree that the abstract should include key quantitative details to allow proper evaluation of the central claim. In the revised version, we will expand the abstract to report the sample size (number of threads), the baseline model's performance metric (e.g., AUC), the improvement from adding the rhetorical strategy features (with delta and statistical significance), and associated confidence intervals. These values are computed in the full results but will be summarized concisely in the abstract. revision: yes

  2. Referee: [Methods / Coding procedure] Hybrid coding procedure (described after the baseline forecast): the ten rhetorical strategies are assigned via LLM with no reported inter-annotator agreement against human coders, prompt details, few-shot examples, or systematic bias audit. Because the headline directional effects (concession/empathy positive; refutation/credibility attacks negative) rest entirely on these labels, the absence of validation is load-bearing for the pattern reported.

    Authors: We acknowledge that the validation of the hybrid coding procedure requires more explicit reporting. In the revision, we will add the exact LLM prompts, few-shot examples, inter-annotator agreement statistics from a human-annotated subset (including Cohen's kappa per strategy), and results from a bias audit comparing LLM labels to human judgments. This will directly support the reliability of the reported directional effects for concession, empathy, refutation, and credibility attacks. revision: yes

  3. Referee: [Baseline forecast] Baseline forecast section: the LLM's halfway-through probabilistic estimates are used as the conversational baseline without any human validation, calibration check against actual delta rates, or analysis of whether the LLM systematically over- or under-predicts in threads that later receive deltas. This assumption directly affects the claimed incremental lift from the strategy features.

    Authors: We agree that the baseline requires explicit validation and calibration analysis. We will add to the revised manuscript a dedicated subsection with calibration metrics, including the correlation between LLM-predicted probabilities and observed delta rates, Brier scores, and an analysis of systematic over- or under-prediction (with particular attention to threads that later receive deltas). This will clarify the incremental contribution of the strategy features over the baseline. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical prediction of external belief-change labels from independent LLM baseline and text-derived strategy codes

full rationale

The paper's central result is an empirical regression showing that coded rhetorical strategies improve prediction of actual observed belief changes (OP-awarded deltas on r/ChangeMyView) beyond an LLM-generated halfway forecast baseline. The target variable is real-world public data independent of the model. The baseline is an external probabilistic forecast, and strategy codes are derived from reply text via hybrid machine-assisted labeling; neither is defined in terms of the outcome nor fitted to it by construction. No self-citation chain, uniqueness theorem, or ansatz is invoked to force the result. The analysis is therefore self-contained against the external benchmark of observed deltas.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The work is empirical and rests on domain assumptions about LLM forecasting accuracy and reliable strategy coding rather than mathematical axioms or free parameters.

assumptions (2)
  • domain assumption LLMs can produce useful probabilistic forecasts of belief revision from partial conversation transcripts
    Used as the conversational baseline before adding strategy features.
  • domain assumption The ten rhetorical strategies can be coded reliably through a hybrid machine-assisted procedure
    Central to the improved predictive power and the reported pattern.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Change My View? The Dynamics of Persuasion and Polarization in Online Discourse." pith.science (2026). https://pith.science/paper/2605.08383

@misc{pith2026260508383,
  author       = {Pith},
  title        = {Pith review of: Change My View? The Dynamics of Persuasion and Polarization in Online Discourse},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2605.08383}},
  note         = {Machine review of arXiv:2605.08383}
}
read the original abstract

Philosophical accounts of persuasion often assume that shared evidence and rational argumentation should lead to a convergence of views between peers, yet everyday discourse often suggests otherwise. In this study, we use large language models to analyze a corpus of debates on Reddit's r/ChangeMyView, where belief revision is publicly signaled. Large language models were asked, halfway through each discussion, to forecast whether such an acknowledgement would arise; their probabilistic estimates serve as a conversational baseline. Each reply was then coded, through a hybrid machine-assisted procedure, for ten familiar rhetorical strategies -- concession, empathy, logical challenge, credibility appeals, and so forth. Adding these strategic features markedly improves predictive power and yields a consistent pattern: moves that express concession or empathetic alignment substantially increase the prospect of belief change, whereas frontal refutation, credibility attacks, and topic deflection diminish it. The findings indicate that effective public reasoning depends as much on relational framing as on evidential content, and they invite a refinement of normative accounts of rational dialogue.

Discussion (0). Continue with ORCID to comment.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    Large language models were asked, halfway through each discussion, to forecast whether such an acknowledgement would arise; their probabilistic estimates serve as a conversational baseline. Each reply was then coded, through a hybrid machine-assisted procedure, for ten familiar rhetorical strategies

  • IndisputableMonolith/Foundation/BranchSelection.lean branch_selection unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    replies that begin with concessionary language or that make a visible effort to establish rapport are markedly more likely to earn a delta than replies that lead with formal refutation

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages

  1. [1]

    The Annals of Statistics , volume=

    Agreeing to Disagree , author=. The Annals of Statistics , volume=

    work page

  2. [2]

    The Annals of Mathematical Statistics , volume=

    Merging of Opinions with Increasing Information , author=. The Annals of Mathematical Statistics , volume=

    work page

  3. [3]

    Perspectives on Socially Shared Cognition , editor=

    Grounding in Communication , author=. Perspectives on Socially Shared Cognition , editor=. 1991 , publisher=

    work page 1991

  4. [4]

    The Philosophical Review , volume=

    Epistemology of Disagreement: The Good News , author=. The Philosophical Review , volume=

    work page

  5. [5]

    Reflection and Disagreement , author=. No\^

    work page

  6. [6]

    Psychological Review , volume=

    Belief Polarization Is Not Always Irrational , author=. Psychological Review , volume=

    work page

  7. [7]

    2004 , publisher=

    A Systematic Theory of Argumentation: The Pragma-Dialectical Approach , author=. 2004 , publisher=

    work page 2004

  8. [8]

    1995 , publisher=

    A Pragmatic Theory of Fallacy , author=. 1995 , publisher=

    work page 1995

Show all 17 references
  1. [9]

    2023 , school=

    Polarization and Factionalization for Agents with Multiple, Related Beliefs , author=. 2023 , school=

    2023

  2. [10]

    Synthese , volume=

    Rational Factionalization for Agents with Probabilistically Related Beliefs , author=. Synthese , volume=

  3. [11]

    The Journal of Philosophy , volume=

    Disagreement, Dogmatism, and Belief Polarization , author=. The Journal of Philosophy , volume=

  4. [12]

    Why Do Humans Reason?

    Mercier, Hugo and Sperber, Dan , journal=. Why Do Humans Reason?

  5. [13]

    2018 , howpublished=

    2018

  6. [14]

    Deal, or No Deal (or Who Knows)?

    Sicilia, Anthony and Kim, Hyunwoo and Chandu, Khyathi Raghavi and Alikhani, Malihe and Hessel, Jack , booktitle=. Deal, or No Deal (or Who Knows)?. 2024 , publisher=

    2024

  7. [15]

    Mind & Language , volume=

    Epistemic Vigilance , author=. Mind & Language , volume=

  8. [16]

    Linguistics and Philosophy , volume=

    Common Ground , author=. Linguistics and Philosophy , volume=

  9. [17]

    Proceedings of the 25th International Conference on World Wide Web , pages=

    Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-Faith Online Discussions , author=. Proceedings of the 25th International Conference on World Wide Web , pages=

Pith tools

Reviewed May 12, 2026 · model on record in the stance chip above.