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#discourse-analysis News & Analysis

5 articles tagged with #discourse-analysis. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles
AIBearisharXiv – CS AI · May 17/10
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When Roles Fail: Epistemic Constraints on Advocate Role Fidelity in LLM-Based Political Statement Analysis

Researchers systematically tested whether large language models can maintain assigned adversarial roles when analyzing political statements, discovering that models frequently fail to sustain their epistemic stance due to training knowledge overriding role instructions. The study identifies "Epistemic Role Override" as the mechanism behind role failures, with significant performance variance between models (Mistral Large achieving 67% role fidelity versus Claude Sonnet's 39%), raising critical concerns about the reliability of multi-agent LLM systems designed to provide balanced political discourse analysis.

🏢 Perplexity🧠 Claude
AINeutralarXiv – CS AI · Jun 115/10
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Causal Emotion Recognition in Conversation: Context Saturation and Discourse-Marker Evidence

Researchers conducted a systematic study on emotion recognition in conversation using the IEMOCAP dataset, identifying that conversational context dominates performance but saturates within 10-30 preceding turns. The study reveals that hierarchical sentence representations and external affective lexicons provide minimal additional benefit, while discourse-marker analysis shows sadness correlates with reduced left-periphery markers, suggesting emotional states vary in context-dependency.

AINeutralarXiv – CS AI · Jun 26/10
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VET: A Framework for Analyzing AI Discourse

Researchers introduce the VET Framework, a structured method for categorizing AI discourse across three dimensions—valence, effectiveness, and trajectory—to combat polarized narratives in public AI discussions. The framework identifies and critiques four prevalent stances (AI Hype, AI Doom, AI Denial, and AI Normalcy) as tools for improving AI literacy among the general public.

AINeutralarXiv – CS AI · May 286/10
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Evaluating the Realism of LLM-powered Social Agents: A Case Study of Reactions to Spanish Online News

Researchers evaluated whether large language models can realistically simulate human behavior in online discourse by comparing LLM-generated reactions to Spanish news articles against real audience responses across hate speech, sentiment, and semantic alignment metrics. The study found that off-the-shelf models significantly underreproduce hate speech and introduce model-specific biases, while fine-tuning improves fidelity unevenly depending on the model.