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This paper presents a computational model for analyzing coping styles in digital crisis discourse, specifically applied to the 2023 Turkiye earthquake.
AudienceCue analyzes YouTube comments to generate cited insights and reports.
Researchers developed the Construction Safety Attitude Framework (CSAF) and validated an LLM-based classifier to measure construction workers' safety attitudes from Reddit discourse, achieving high agreement (κ=0.90) with human expert coders across 10,000+ posts.
Researchers from University of Technology Sydney compare fine-tuned transformers (DistilBERT, RoBERTa) against zero-shot LLMs (Llama variants, Claude, Gemini) for classifying misinformation responses on Reddit, finding that fine-tuned RoBERTa achieves 0.62 macro-F1 versus 0.50 for the best zero-shot model. The study shows that task-specific fine-tuning outperforms larger generalist models, particularly for detecting belief propagation, and that safety-alignment artifacts in frontier models can degrade performance.
This paper proposes a validation framework for using Large Language Models to extract causal relations from social media posts during disasters. It evaluates the effectiveness of LLMs in identifying cause-effect relationships and compares them against expert-grounded reference graphs to assess reliability and risks.
This paper presents an empirical evaluation of LLM-guided semi-supervised learning for classifying social media crisis data. It demonstrates that LG-CoTrain outperforms classical baselines in low-resource settings and highlights the potential of transferring knowledge from LLMs to smaller, deployable models for disaster response.