Investigating Subjective Factors of Argument Strength: Storytelling, Emotions, and Hedging

Carlotta Quensel; Gabriella Lapesa; Neele Falk

REVIEW 4 major objections 4 minor 60 references

Investigating Subjective Factors of Argument Strength: Storytelling, Emotions, and Hedging

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Storytelling and hedging boost subjective persuasion but suppress objective argument quality in regression tests on two datasets.

desk verdict A genuinely new joint regression with a plausible but unsecured cross-domain annotation pipeline; worth refereeing with validation requested. read the letter →

arxiv 2507.17409 v1 pith:JPPEJTRT submitted 2025-07-23 cs.CL

Carlotta Quensel , Neele Falk , Gabriella Lapesa This is my paper · ORCID

classification cs.CL

keywords argumentstrengthpersuasionqualitystorytellingemotionshedgingregressionanalysisautomatedannotation

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

This paper investigates whether three subjective rhetorical devices—personal storytelling, emotional language, and hedging (uncertainty markers like 'probably' or 'I think')—strengthen or weaken arguments, and whether their impact depends on how argument strength is defined. Using regression on two standard datasets, IBM ARGQ for objective argument quality and Cornell CMV for subjective persuasion, the authors find that storytelling and hedging significantly help persuasion but significantly hurt objective quality. Emotion effects are mostly stable across the two datasets: guilt/shame and disgust reduce argument strength, while fear and sadness increase it, and the authors interpret this as evidence that the rhetoric use of emotions matters more than the domain. The paper also contributes automated annotation methods for the three features, since no existing dataset labels all of them.

What carries the argument

The central object is a pair of parallel regressions on two datasets that operationalize objective and subjective argument strength: OLS linear regression on IBM ARGQ's continuous quality score and logistic regression on Cornell CMV's binary persuasion label. The independent variables come from automated annotation layers: a 10-fold RoBERTa ensemble for storytelling trained on mixed domains, masked emotion classifiers per emotion trained on CROWD-ENVENT event descriptions, and a lexicon-based hedge detector with syntactic disambiguation rules. The design's power comes from the contrast between the datasets, which differ in collection, length, and annotation procedure, so any feature whose coefficient sign is consistent across both is attributed to the feature itself, while a sign flip is attributed to the difference between objective and subjective strength.

What would settle it

A manual annotation of a random sample of 200–300 arguments from each dataset by human judges for storytelling, the ten emotions, and hedging, followed by re-running the regressions with the human labels, would settle whether the automated-label coefficients reflect real effects or measurement error; if the signs or significances change, the paper's central contrast is an artifact of the annotation pipeline.

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Extended reading notes

Core claim

The central claim is that the value of a subjective rhetorical feature is not intrinsic; it flips sign with the facet of argument strength being measured. On IBM ARGQ, where short impersonal arguments are scored by averaged crowd judgments of adequacy, storytelling has a significant negative coefficient ($\beta = -0.182$) and hedge count is significantly negative ($\beta = -0.011$). On Cornell CMV, where a delta means one reader changed their mind, storytelling raises the odds of persuasion by a factor of 1.148 and hedge count by 1.030. Emotions behave differently: disgust and guilt/shame significantly lower argument strength in both datasets, while fear and sadness significantly raise it, which the authors trace to the difference between emotional attacks aimed at a participant and emotional appeals to universal concerns. The regression models themselves explain only a few percent of variance, so the contribution is about the direction and significance of the effects, not their size.

Load-bearing premise

The automated annotations of storytelling, emotions, and hedges are accurate enough on the target corpora to serve as independent variables, but they are only validated on held-out training-domain data, not on IBM ARGQ or Cornell CMV text.

Editorial extensions

If this is right

Argument-quality models built for objective settings such as essays or debates should treat personal stories and hedges as likely weaknesses, not strengths.
Persuasion-oriented systems, in contrast, can treat hedges and personal narratives as positive signals of credibility-building.
Emotion-aware argument models can expect the same directional effects across domains: disgust and guilt/shame lower strength, fear and sadness raise it.
Because explained variance is low (about 4% for quality and 1% for persuasion), these features complement rather than replace contextual and demographic predictors.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A direct test of the paper's interpretation would be to re-run the analysis with a third dataset that contains both objective and subjective strength labels for the same arguments; the framework predicts that feature coefficients align with the label definition, not with the topic or platform.
The emotion results suggest that classifying emotional attacks versus emotional appeals, rather than raw emotion categories, could be a stronger predictor of argument strength than any single emotion label.
The automated annotation transfer assumption is fragile: because classifiers are validated only on training-domain held-out data, a manual validation sample from the target corpora would reveal whether the sign flips are genuine or measurement artifacts.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

First thing to know: this paper has a real new result—the first joint regression of storytelling, emotion, and hedging against both objective quality and subjective persuasion—but the headline contrast is only as solid as the automatically predicted features, and those are not validated on the target corpora. I’d send it to review, but with a request for validation before acceptance.

What’s genuinely new: the joint modeling. Prior work studied these features in isolation, so the opposite-signed effects for storytelling and hedging across IBM ARGQ and Cornell CMV are a useful empirical contribution. The released annotated datasets will be useful to the community. The emotion findings—mostly stable directions, with guilt/shame and disgust negative and fear/sadness positive—are plausible and align with earlier isolated results.

The soft spots are real and concentrate in the annotation pipeline. The classifiers are evaluated only on held-out training data, not on the actual target texts. That matters because the training domains are far from the targets: CROWD-ENVENT event descriptions for emotions, and mixed narrative/forum texts for storytelling. The IBM ARGQ storytelling cell is tiny—about 42 predicted positives out of 5.3k—so the negative coefficient is identified by very few out-of-domain predictions. If the classifier fires differently on short impersonal debate arguments versus long Reddit posts, the opposite signs could be an artifact of differential misclassification. The stress-test note is right. The paper’s own Limitations paragraph explicitly relies on heldout performance, which is not enough.

There are also smaller issues: stepwise model selection with no multiple-comparison correction, very low explained variance (3.96% r2 on IBM ARGQ, 1.36% pseudo-r2 on Cornell), and the abstract states the 'rhetoric utilization' interpretation as a finding when it’s only a hypothesis from a few examples. The self-citation to Falk and Lapesa is minor and appropriate—they’re replicating that setup.

Bottom line: the central contrast is plausible and consistent with prior isolated findings, but it is not secured. A reviewer should ask for a sample of target-domain annotations (even a few hundred per corpus), an overlap check between the storytelling training set and Cornell CMV, and a sensitivity analysis that re-runs the regressions on the subset of instances with high-confidence predictions.

For a workshop paper, this is the right kind of ambition. It deserves serious refereeing, but conditional on the validation. I’d bring it to reading group mostly to debate how much weight to put on cross-domain annotation.

Referee Report

4 major / 4 minor

Summary. This paper studies the joint impact of three subjective features—storytelling, emotions, and hedging—on two argument-strength corpora. The authors automatically annotate IBM ARGQ (5.3k short debate arguments, objective quality scores) and Cornell CMV (11.5k Reddit comments, binary persuasion labels) using a RoBERTa ensemble for storytelling and ten emotion classifiers trained on CROWD-ENVENT, plus lexicon-based hedge detection. They then run univariate and stepwise regressions with interactions. They report that storytelling and hedging have negative coefficients on IBM ARGQ but positive effects on Cornell CMV persuasiveness, while discrete emotions have largely consistent effects across corpora, with disgust and guilt/shame negative and fear/sadness positive. The paper also contributes the annotated datasets and code.

Significance. If the empirical results hold, the main contrast—subjective features helping persuasion but hurting objective quality—would be a valuable contribution to computational argumentation, and the released annotated datasets would support further research. The paper has genuine strengths: it compares multiple annotation strategies (e.g., masked versus unmasked emotion training, mixed-domain ensembles), it reproduces prior storytelling results, and it is unusually explicit about the limitations of its cross-domain annotation. The data and code are shared. However, the significance of the empirical findings is currently limited by the absence of target-domain validation of the automatically predicted features and by inferential choices discussed below.

major comments (4)

[§4.1, §4.2, Table 2, Limitations] The independent variables for storytelling and emotions are machine predictions with no target-domain validation. For IBM ARGQ, the storytelling classifier predicts only 0.8% positives (45 of 5.3k instances in Table 2), and all reported held-out F1 scores in Table 5 come from training-domain test sets (Falk and Lapesa 2022; CROWD-ENVENT), not from IBM ARGQ or Cornell CMV. The Limitations section explicitly justifies the emotion annotations via good results on the heldout training data. Measurement error in binary predictors is non-classical and can bias regression coefficients in either direction, especially with rare positives. Since the headline storytelling/hedging contrast is identified by these predicted labels, the authors should supply target-domain validation (for example, manual evaluation of a random sample, particularly the 45 IBM ARGQ storytelling positives), run a sensitivity analysis under misclassification assumptions, or substantially soften the contrast claims.
[§3, Discussion] The central contrast between objective argument quality and individualized persuasion is confounded by domain differences. IBM ARGQ and Cornell CMV differ simultaneously in text length, register, collection procedure, annotation process, and topic, as the authors acknowledge in §3 when they state that the number of differences disallows a comparison without confounding factors. The opposite signs for storytelling and hedging may therefore reflect domain, length, or register effects rather than the objective/subjective distinction. The paper should frame the result as a comparison of two datasets rather than of two argument-strength constructs, or add within-domain controls such as length, topic, or matched sub-samples.
[§5, Table 9] The stepwise regression procedure selects features and interactions by AIC and significance tests on the same data, and the resulting p-values are reported without correction for this selection. In addition, Table 3 tests multiple emotion categories plus storytelling and hedging variants against two corpora, and some Cornell CMV effects are only marginal (for example storytelling p=0.015, pride p=0.042, relief p=0.007) with no multiple-comparison correction. With final explained variance of 3.96% for IBM ARGQ and 1.36% for Cornell CMV, the Limitations statement that high significance nonetheless shows importance overstates the evidence, because post-selection p-values are not valid and the effect sizes are very small. The authors should hold out a validation set for model selection, correct for multiple testing, and report effect sizes with confidence intervals.
[§5 Discussion, Conclusion] The abstract claim that the influence of emotions depends on their rhetoric utilization rather than the domain is not supported by the regression design. The models measure emotion categories such as anger, fear, and disgust, but not rhetoric utilization such as emotional attack versus appeal; the attack/appeal distinction is introduced post hoc from a few examples in Table 4 and a qualitative reading. To support this claim, the authors would need to annotate or automatically measure attack/appeal usage and include it in the regression, or explicitly label the claim as an untested hypothesis.

minor comments (4)

[§5] The sentence listing the features as 1 storytelling plus 9 emotions excluding boredom and surprise plus 6 hedging variants does not match Table 3, which includes boredom and reports only one hedge count; please harmonize the feature count and the table.
[§5] The expression beta-story = 0.138, beta-hedge) = 0.030 contains a misplaced parenthesis; please clarify whether the Cornell CMV estimates are log-odds or odds ratios.
[Table 5] The header standard deviance should be standard deviation; the current wording is ambiguous and nonstandard.
[§4.1] The storytelling training data includes a subset of r/ChangeMyView, so the Cornell CMV predictions are not fully cross-domain; please state this explicitly when discussing cross-domain robustness.

Circularity Check

0 steps flagged · score 2.0 of 10

Empirical regression with automated annotations; no equation-level circularity; the only same-author citation is not load-bearing for the central contrast.

full rationale

The paper's derivation chain is an empirical regression: Section 4 produces automated annotations via classifiers trained on external or prior corpora, and Section 5 regresses those annotations on the target variables. No independent variable is defined in terms of the dependent variable, and no fitted parameter is renamed as a prediction. The central claim—storytelling and hedging have opposite-signed effects on objective argument quality versus subjective persuasion—is a regression outcome, not an identity or a restatement of the annotation models' outputs. The one place where same-author prior work appears is the storytelling annotation setup, which follows Falk and Lapesa (2022) for training data and ensemble design; however, the paper re-trains the classifiers and reports its own held-out F1 = 0.82, and the prior work is not used to forbid alternatives or to supply the regression result. Thus the self-citation is not load-bearing. The Limitations paragraph's reliance on held-out training-domain performance for the emotion classifiers is an explicit cross-domain validity limitation, not a circularity: it concerns measurement error, not a reduction of the conclusion to the input. No circular step can be exhibited by quoting an equation or construction, so no specific circular step is reported.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claims rest on the validity of predicted annotations and on the mapping of IBM ARGQ to objective quality and Cornell CMV to subjective persuasion. No new physical or conceptual entities are postulated; the emotional attack versus appeal distinction is an interpretive hypothesis, not a tested variable.

assumptions (4)

ad hoc to paper The automatically predicted feature annotations (storytelling, emotions, hedging) are valid proxies for the true features in both target corpora.
The paper validates classifiers only on held-out training domains and assumes transfer to IBM ARGQ and Cornell CMV; see the Limitations paragraph.
domain assumption IBM ARGQ crowd-averaged scores operationalize objective argument quality, and Cornell CMV deltas operationalize individualized subjective persuasion.
Section 3 assigns these conceptualizations while acknowledging that the datasets differ in many confounded ways.
standard math OLS and logistic regression are appropriate functional forms, with independent errors and no serious multicollinearity.
Section 5 uses these models but reports no residual or multicollinearity diagnostics.
domain assumption Hedge lexicons and disambiguation rules transfer to both corpora, including Reddit abbreviation usage.
Section 4.3 says the lexicons are adapted from similar semi-formal domains, but no target-domain evaluation is provided.

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Pith. "Pith review of Investigating Subjective Factors of Argument Strength: Storytelling, Emotions, and Hedging." pith.science (2026). https://pith.science/paper/JPPEJTRT

@misc{pith2026250717409,
  author       = {Pith},
  title        = {Pith review of: Investigating Subjective Factors of Argument Strength: Storytelling, Emotions, and Hedging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JPPEJTRT}},
  note         = {Machine review of arXiv:2507.17409}
}

abstract

In assessing argument strength, the notions of what makes a good argument are manifold. With the broader trend towards treating subjectivity as an asset and not a problem in NLP, new dimensions of argument quality are studied. Although studies on individual subjective features like personal stories exist, there is a lack of large-scale analyses of the relation between these features and argument strength. To address this gap, we conduct regression analysis to quantify the impact of subjective factors $-$ emotions, storytelling, and hedging $-$ on two standard datasets annotated for objective argument quality and subjective persuasion. As such, our contribution is twofold: at the level of contributed resources, as there are no datasets annotated with all studied dimensions, this work compares and evaluates automated annotation methods for each subjective feature. At the level of novel insights, our regression analysis uncovers different patterns of impact of subjective features on the two facets of argument strength encoded in the datasets. Our results show that storytelling and hedging have contrasting effects on objective and subjective argument quality, while the influence of emotions depends on their rhetoric utilization rather than the domain.

Figures

Figures reproduced from arXiv: 2507.17409 by the authors.

**Figure 2.** Interaction between fear (x-axis) and sadness (standard deviations shown through hue and dashing) on IBM ARGQ with confidence intervals. of argument strength. To that end, we first determined the feasibility of large-scale automated annotation of our subjective features, to then systematically reveal correlations through a regression analysis. We could reveal a significant effect of almost all observed features on… view at source ↗

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