Definitional Sensitivity in Media Bias Detection: A Multi-Definition Dataset and Benchmark

arXiv:2608.23095 · cs.CL · Submitted 2026-08-24 · Read on arXiv

cs.CL

Submitted: 2026-08-24

Updated: 2026-09-07

Comments: To appear in Findings of the Association for Computational Linguistics: EMNLP 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 5× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Annotator Agreement — 3× in this paper · explained in 1 episode
• Actor — 2× in this paper · explained in 3 episodes
• Scaffolding — 2× in this paper · explained in 3 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes

Sources

• Automated Annotation with Generative AI Requires Validation
• The Llama 3 Herd of Models
• Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis
• Best Practices for Text Annotation with Large Language Models
• The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias

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