Analyses of labeled social media sentences and interpretations show 30% divergence in ethos and pathos, greater variability for charged content, and predictive power for audience attitudes toward the author.
Exploring the Potential of Large Language Models in Computational Argumentation
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CL 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
CAF-Gen uses an iterative multi-agent creator-reviewer process to enrich shallow argument mining outputs into structurally richer CAF-compliant models with claimed improvements over single-pass generation.
Proposes a symbiotic human-AI deliberation framework with graded metrics, provenance pipelines, and human primacy to scale collective intelligence accountably.
citing papers explorer
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How Ethos and Pathos Appeals Resonate in Reader Interpretations of Social Media Messages
Analyses of labeled social media sentences and interpretations show 30% divergence in ethos and pathos, greater variability for charged content, and predictive power for audience attitudes toward the author.
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CAF-Gen: A Multi-Agent System for Enriching Argumentation Structures
CAF-Gen uses an iterative multi-agent creator-reviewer process to enrich shallow argument mining outputs into structurally richer CAF-compliant models with claimed improvements over single-pass generation.
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Accountable Human-AI Deliberation with LLMs: Scaling Collective Intelligence through Symbiotic Scaffolding
Proposes a symbiotic human-AI deliberation framework with graded metrics, provenance pipelines, and human primacy to scale collective intelligence accountably.