{"slug": "hierbias-context-conditioned-hierarchical-media-bias-detection-with-multi-task", "title": "HierBias: Context-Conditioned Hierarchical Media Bias Detection with Multi-Task Type Classification", "summary": "Researchers introduced HierBias, a hierarchical context-conditioned media bias detector that models document context to improve sentence-level bias classification. The system achieved 0.853 F1 and 0.723 MCC on BABE and BASIL benchmarks, surpassing the state-of-the-art by 2.6% F1 and 4.3% MCC. The approach includes a multi-task framework for binary detection and fine-grained bias type classification, with theoretical guarantees on error reduction.", "body_md": "arXiv:2606.26100v1 Announce Type: new\nAbstract: Media bias detection is a critical task for ensuring fair and balanced information dissemination, yet existing sentence-level approaches classify each sentence independently, ignoring inter-sentence contextual signals that human annotators naturally exploit. We present \\textbf{HierBias}, a hierarchical context-conditioned media bias detector that formally models document context in bias prediction. We introduce the \\emph{context-conditioned bias probability} and prove theoretically that leveraging document context strictly reduces the Bayes error of sentence-level classification when inter-sentence mutual information is non-zero. A multi-task generalization bound further establishes that jointly training binary bias detection and fine-grained bias type classification improves sample efficiency on small annotated corpora. Architecturally, HierBias pairs a sentence-level RoBERTa encoder with a cross-sentence Transformer aggregator and dual output heads for binary detection and four-class type classification. Evaluated on BABE and BASIL, HierBias achieves 0.853 F1 and 0.723 MCC, surpassing the state-of-the-art bias-detector by $+2.6\\%$ F1 and $+4.3\\%$ MCC (McNemar's test, $p < 0.05$). Ablation experiments confirm that each theoretical component contributes independently and consistently.", "url": "https://wpnews.pro/news/hierbias-context-conditioned-hierarchical-media-bias-detection-with-multi-task", "canonical_source": "https://arxiv.org/abs/2606.26100", "published_at": "2026-06-26 04:00:00+00:00", "updated_at": "2026-06-26 04:04:13.382189+00:00", "lang": "en", "topics": ["natural-language-processing", "machine-learning", "ai-research"], "entities": ["HierBias", "RoBERTa", "BABE", "BASIL"], "alternates": {"html": "https://wpnews.pro/news/hierbias-context-conditioned-hierarchical-media-bias-detection-with-multi-task", "markdown": "https://wpnews.pro/news/hierbias-context-conditioned-hierarchical-media-bias-detection-with-multi-task.md", "text": "https://wpnews.pro/news/hierbias-context-conditioned-hierarchical-media-bias-detection-with-multi-task.txt", "jsonld": "https://wpnews.pro/news/hierbias-context-conditioned-hierarchical-media-bias-detection-with-multi-task.jsonld"}}