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arXiv cs.CL
arXiv cs.CL
6/26/2026
HierBias: Context-Conditioned Hierarchical Media Bias Detection with Multi-Task Type Classification

HierBias: Context-Conditioned Hierarchical Media Bias Detection with Multi-Task Type Classification

Short summary

HierBias uses hierarchical Transformers to detect media bias by modeling document context, combining binary detection with fine-grained type classification. It achieves state-of-the-art results on BABE and BASIL benchmarks (F1: 0.853, MCC: 0.723) with theoretical proof that context-conditioned classification reduces error.

  • •Hierarchical approach leverages document context for bias detection, not just isolated sentences
  • •Achieves +2.6% F1 and +4.3% MCC over prior SOTA with statistical significance
  • •Combines binary bias detection with four-class type classification via multi-task learning

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