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

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

Problem Statement
Current computational research on rhetoric in social media predominantly focuses on identifying rhetorical strategies and fallacies within the explicit text of posts and comments. This approach overlooks the "universal audience"—the majority of users who consume content silently without leaving explicit feedback. Consequently, existing methods fail to capture how rhetorical devices, specifically ethos (appeals to credibility/character) and pathos (appeals to emotion), are processed, transformed, or omitted during the internal interpretation of a message. This study addresses the gap between the sender's rhetorical intent and the silent audience's cognitive reception, investigating whether and how these appeals resonate in reader interpretations.

Methodology
The study utilizes the OrigamIM dataset, which contains 2,018 English sentences from the Reddit subreddit r/ChangeMyView paired with approximately 9,851 human-written interpretations (reformulations) and attitude scores (1–5 Likert scale) toward the original author.

1. Rhetorical Classification (Silver Standard):
   Due to the scale of the dataset (~10,000 text spans), the authors employed a "silver standard" annotation pipeline rather than manual labeling for the entire corpus. They compared two approaches:

   • Fine-tuned Classifiers: RoBERTa models (large for ethos, base for pathos) trained on the PolarIs dataset, which contains expert-annotated ethos and pathos labels.
   • Large Language Models (LLMs): Gemini 2.5 used for zero-shot, few-shot, and chain-of-thought prompting to classify ethos and pathos.
   • Label Scheme: Both ethos and pathos were mapped to a ternary scheme: {+, −, 0} (positive/support, negative/attack, neutral).

2. Validation:

   • Automatic Evaluation: Models were tested on held-out sets of PolarIs and the EU DisinfoTest dataset. Results showed RoBERTa achieved high F1 scores (0.88–0.91) on in-domain data but dropped in performance on out-of-domain data due to differences in annotation guidelines (e.g., self-referential vs. other-directed ethos).
   • Manual Evaluation: A subset of 100 samples from OrigamIM was manually annotated by experts. Gemini 2.5 outperformed RoBERTa in this specific context (F1 0.73 vs. 0.64 for ethos), likely due to better coverage of credibility cues.
   • Final Strategy: A majority voting scheme combining RoBERTa and Gemini decisions was used for downstream analysis.

3. Analysis:
   The study analyzed the alignment between the rhetorical labels of the original sentence ($s$) and its audience interpretations ($i$). It employed statistical tests (Chi-square) and Ordinary Least Squares (OLS) regression to determine:

   • The degree of divergence between source and interpretation labels.
   • Whether specific rhetorical labels (e.g., positive pathos) predict higher variability in interpretations.
   • Whether sentence-level ethos/pathos predicts reader attitudes toward the author.
   • Qualitative factors (beyond ethos/pathos) driving interpretive differences.

Key Results

• Divergence in Interpretation: Rhetorical appeals are not consistently preserved. Interpretations diverge from the original sentence in 30% of cases. Full alignment (where all interpretations match the original ethos and pathos) occurs in only 15.9% of sentences.
• Impact of Rhetorical Charge:
  • Neutrality: Sentences with neutral ethos and pathos show significantly higher alignment with interpretations ($p < 0.001$), suggesting neutrality fosters consensus.
  • Variability: Rhetorically charged content elicits greater variability. Sentences with positive pathos and positive/negative ethos show the highest interpretive divergence. OLS regression confirmed that both positive and negative ethos ($\beta = 0.46$ and $0.32$) and positive pathos ($\beta = 0.30$) significantly increase interpretive variability compared to neutral baselines.
• Predicting Attitudes: Original sentence ethos and pathos are significant predictors of reader attitudes toward the author. Positive appeals correlate with more positive attitudes, while negative appeals correlate with more negative attitudes. However, these appeals do not significantly predict the variability of attitudes within a single interpretation set.
• Drivers of Variability: Beyond ethos and pathos, the study identified eight factors contributing to interpretive divergence, categorized as:
  • Interpreter-related: Value systems, political partisanship, social identity, personal experience.
  • Content/Form-based: Author opinion framing, rhetorical questions, sarcasm.
  • Contextual: Out-of-context effects leading to misinterpretation of referents or targets.

Key Contributions

1. Perspectivist Rhetorical Analysis: The paper introduces a computational perspective that shifts focus from the text itself to the distribution of audience interpretations, capturing the "silent" reception of rhetoric.
2. Empirical Evidence of Transfer: It provides large-scale evidence that ethos and pathos are not static properties of text but are subject to transformation (preservation, distortion, or omission) during the interpretation process.
3. Dataset Enrichment: The authors enriched the existing OrigamIM dataset with new ethos and pathos annotation layers, making them publicly available for further research.
4. Identification of Predictive Factors: The study quantifies how specific rhetorical appeals (particularly non-neutral ones) drive interpretive instability and identifies the specific cognitive and contextual factors that mediate this variation.

Significance and Claims
The paper argues that the assumption of textual content serving as a direct proxy for audience reception is flawed. The findings suggest that "ground truth" in communication data is inherently unstable, particularly regarding persuasive appeals.

• Theoretical Implication: The study challenges the field to move from modeling "what the text says" to modeling "the distribution of what the text evokes." It proposes that "interpretive instability" (divergence in meaning construction) is a distinct dimension from toxicity that warrants investigation.
• Practical Implication: For Natural Language Processing (NLP), optimizing models solely on semantic similarity metrics ignores a massive chasm in human communication. To effectively model social phenomena like polarization, models must account for the receiver-centric pragmatic distribution of meaning.
• Modesty: The authors acknowledge limitations, including the reliance on a single-language (English) dataset from a specific subreddit, the small size of the interpretation set per sentence (limiting the capture of the full "universal audience"), and the inherent noise in silver-standard classification. They frame their work as a baseline for modeling rhetorical interpretation within the silent audience, opening avenues for future work to disentangle rhetorical appeals from confounding variables (e.g., ideological alignment) and to bridge the gap between latent cognitive states and overt platform behaviors (e.g., upvotes, shares).