How Ethos and Pathos Appeals Resonate in Reader Interpretations of Social Media Messages

Abstract: Rhetorical strategies and their influence on audiences are often studied through social media posts and comments. However, this focus overlooks the universal audience, which is the majority of readers who remain silent and do not explicitly express how a message affects them. This study investigates how two classical modes of persuasion, ethos and pathos, resonate in the silent audience's interpretations of meaning. Using a dataset of social media sentences paired with human-written interpretations, we label both sources for ethos and pathos and assess whether these rhetorical appeals are preserved. Our analyses show that interpretations diverge from the original sentences in 30% of cases, with rhetorically charged content eliciting greater variability than neutral content. We further find that ethos and pathos in original sentences can predict audience attitudes toward the author, underscoring the subtle ways rhetoric shapes perception beyond visible engagement.

• The paper demonstrates that only 74.2% of interpretations match the original message’s ethos and 70.4% match pathos, highlighting significant interpretive divergence.
• It employs a combined automated (RoBERTa and Gemini-2.5) and manual annotation methodology to quantify rhetorical alignment on a large-scale Reddit dataset.
• Findings indicate that rhetorical charge, particularly positive appeals, drives higher variability in audience attitudes, urging a shift toward receiver-centric modeling.

Rhetorical Appeals and Interpretive Variability in Social Media: A Computational Analysis

Introduction

The study "How Ethos and Pathos Appeals Resonate in Reader Interpretations of Social Media Messages" (2607.00873) examines the transmission and transformation of Aristotelian rhetorical appeals—ethos (credibility) and pathos (emotion)—as social media messages are interpreted by a silent audience. Rather than focusing solely on explicit audience engagement such as comments or likes, the paper operationalizes the concept of the "universal audience," that is, the vast majority of readers who consume content without leaving publicly visible reactions. The central concern is whether, and how, ethos and pathos appeals, expressed in original posts, are preserved, transformed, or omitted when internalized by readers, and how these rhetorical cues influence attitudes toward content authors.

This work is situated at the intersection of computational rhetoric, perspectivist approaches to meaning and annotation, and the analysis of social communication dynamics on digital platforms. By leveraging both automatic and manual labeling pipelines on a large-scale interpretation dataset, the study provides empirical evidence on the variability of rhetorical resonance and its implications for modeling online persuasion, attitude formation, and interpretive divergence.

Methodology

The authors use the OrigamIM dataset, which consists of 2,018 sentences sourced from Reddit’s r/ChangeMyView and paired with approximately five human-written interpretations each, resulting in 9,851 interpretations. Each interpretation carries both reformulation of the source sentence's meaning and an explicit attitude score toward the author, based on a five-point Likert scale. This setup captures fine-grained differences in meaning construction and reception at the level of silent audience cognition.

Annotation of rhetorical appeals is achieved via a silver-standard pipeline, combining finetuned RoBERTa models (using the PolarIs dataset, annotated for ethos and pathos [gajewska2024ethos]) with prompt-based Gemini-2.5 LLM classification. The labels are unified into ternary {-1, 0, +1} schemes for both ethos (attack, neutral, support) and pathos (negative, neutral, positive). Validation includes both automatic evaluation on held-out data and gold-standard manual annotation by experts.

The analytical approach centers on quantifying the alignment (or divergence) of ethos and pathos between original sentences and their interpretations, assessing which sentence-level cues are predictive of interpretive variation, and modeling the impact of rhetorical devices on audience attitudes.

Key Results and Numerical Findings

Labeling Reliability

• RoBERTa finetuned on PolarIs achieves substantial F1 scores for 3-class ethos and pathos detection (F1 ≈ 0.91 and 0.88, respectively) within-domain, with expected degradation in domain transfer and especially for subjective, emotionally laden content.
• On a held-out, manually annotated subset from OrigamIM, Gemini-2.5 marginally outperforms RoBERTa (F1 = 0.73 vs. 0.64 for ethos), suggesting strength in LLM generalization for subtle credibility cues.

Preservation of Rhetorical Appeals

• Only 74.2% of interpretations match the original sentence’s ethos; only 70.4% match pathos. That is, nearly 30% of interpretations diverge in rhetorical charge.
• Full alignment (all interpretations for a sentence reproduce the original ethos and pathos) is rare (15.9% of cases); complete divergence is extremely uncommon.
• Neutral sentences show dramatically higher rhetorical consistency in interpretations versus non-neutral (charged) sentences (e.g., for ethos: 85.6% alignment for neutral, only 51.0% for non-neutral; pathos: 72.6% vs. 62.7%).

Effect of Rhetorical Charge

• OLS regression reveals that both positive and negative ethos/pathos significantly increase interpretive variability; positive ethos is particularly associated with higher divergence (β = 0.46, p < 0.001). Pathos has a similar effect but of smaller magnitude.
• Ethos and pathos signals in the sentence are statistically significant predictors of the attitude assigned by readers toward authors: positive pathos increases positive attitudes (β = 0.37), negative pathos decreases them (β = -0.12); similarly for ethos (β = 0.18 and -0.10 for positive and negative, respectively). All coefficients highly significant (p < 0.001).

Determinants of Interpretation Variability

Manual analysis identifies that, in addition to sentence-level ethos/pathos, interpretive divergence is modulated by:

• Value system differences and political partisanship
• Social identity/positioning
• Out-of-context effects, rhetorical questions, and the use of sarcasm
• Personal experience framing

These factors operate at the intersection of cognitive, social, linguistic, and contextual dimensions, amplifying variability particularly for rhetorically charged topics.

Theoretical and Practical Implications

The findings directly challenge assumptions that meaning and interpretive effect can be linearly inferred from text surface forms in computational social science. The observed 30% rhetorical divergence, concentrated in non-neutral, high-stakes rhetorical scenarios, exposes inherent limits of ground-truth centric evaluation, especially for models of persuasion, toxicity, or ideology. Instead, modeling the distribution and drivers of interpretive outcomes—rather than the modal interpretation—becomes essential when analyzing influence and polarization dynamics.

Practically, this work motivates:

• A paradigm shift from sender-centric/text-centric to receiver-centric/pragmatic modeling for applications such as stance detection, misinformation classification, affect detection, and opinion mining.
• Rethinking evaluation metrics: accuracy on a single label is insufficient for phenomena characterized by interpretive pluralism.
• The need for dataset curation and method development that foregrounds interpretee diversity, social context, and subjectivity as core components, not annotation "noise."
• Theoretical extension of computational models of argumentation, suggestibility, and online polarization to jointly account for rhetorical style and recipient heterogeneity.

Future Directions

Several open research directions are suggested:

• Extending the analysis to larger, more diverse universal audience samples and across more social platforms, to test universality of observed effects.
• Decomposing the interplay between rhetorical appeals and dimensions such as ideology, topic, text complexity, and platform incentives.
• Integrating signals from explicit engagement (e.g., voting behavior, resharing, commenting) with silent audience interpretation data, thereby triangulating cognitive and behavioral measures of influence.

Such work will inform the design of pragmatic, socially aware AI systems able to emulate or predict not just what text says, but how meaning is refracted through the heterogeneous lenses of digital audiences.

Conclusion

This study provides robust quantitative evidence that rhetorical appeals—ethos and pathos—are subject to substantial interpretive divergence in social media audiences, especially in controversial or affect-rich contexts. Rhetorical neutrality tends to foster consensus, while rhetorical charge, whether positive or negative, is a key driver of variation in both meaning assignment and social evaluation of speakers. The analysis reveals that social media interpretation is shaped by a complex interaction of textual, cognitive, and socio-political factors. For computational linguistics and social computing, these findings mandate receiver-centric models that foreground interpretive plurality and context in understanding digital persuasion and opinion dynamics.