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This paper compares RoBERTa-based sentiment analysis with an LLM-based multi-dimensional framing analysis on political news articles, finding that traditional SA suffers from 'neutral collapse' and that LLM-based approaches better capture bias, sensationalism, and framing for social science research.
This paper explores methods for adapting large language models to cultural contexts in political discourse, aiming to improve cross-cultural understanding and reduce bias.
The article outlines a left-wing perspective supporting AI by highlighting its benefits for disability access and chronic illness management, while critiquing the political alignment of anti-AI sentiment.
University of Minnesota Duluth team used DeBERTa-V3-base augmented with synthetic data from Gemini 3 and Claude Sonnet 4.5 to classify political question evasions, achieving 8th place at SemEval-2026 Task 6.