title: To Bias or Not to Bias: Detecting bias in News with bias-detector
summary: Media bias detection is a critical task in ensuring fair and balanced
  information dissemination, yet it remains challenging due to the subjectivity
  of bias and the scarcity of high-quality annotated data. In this work, we
  perform sentence-level bias classification by fine-tuning a RoBERTa-based model
  on the expert-annotated BABE dataset. Using McNemar's test and the 5x2
  cross-validation paired t-test, we show statistically significant improvements
  in performance when comparing our model to a domain-adaptively pre-trained
  DA-RoBERTa baseline. Furthermore, attention-based analysis shows that our model
  avoids common pitfalls like oversensitivity to politically charged terms and
  instead attends more meaningfully to contextually relevant tokens. For a
  comprehensive examination of media bias, we present a pipeline that combines
  our model with an already-existing bias-type classifier. Our method exhibits
  good generalization and interpretability, despite being constrained by
  sentence-level analysis and dataset size because of a lack of larger and more
  advanced bias corpora. We talk about context-aware modeling, bias
  neutralization, and advanced bias type classification as potential future
  directions. Our findings contribute to building more robust, explainable, and
  socially responsible NLP systems for media bias detection.
paper_authors._id: 682c46c70f622b7afc2e6f8f
paper_authors.name: Himel Ghosh
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-21T09:42:53.022Z
paper_authors._id: 67d5c51817d572b960f6982a
paper_authors.avatarUrl: /avatars/2c22663b27326b2300444bcc84ebbdd7.svg
paper_authors.fullname: Himel Ghosh
paper_authors.type: user
paper_authors.user: himel7
paper_authors._id: 682c46c70f622b7afc2e6f90
paper_authors.name: Ahmed Mosharafa
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-21T10:24:54.616Z
paper_authors._id: 62d0859afb896639b29c45c8
paper_authors.avatarUrl: /avatars/5309851f11c875f15f80a7415a4ae7f3.svg
paper_authors.fullname: Ahmed Mosharafa
paper_authors.type: user
paper_authors.user: amosharafa
paper_authors._id: 682c46c70f622b7afc2e6f91
paper_authors.name: Georg Groh
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-21T10:25:00.667Z
paper_authors._id: 662a6560925d2af5b0e13a34
paper_authors.avatarUrl: /avatars/d45a057e6bbde14f54811be34ae6bc64.svg
paper_authors.fullname: Georg Groh
paper_authors.type: user
paper_authors.user: grohg
paper_title: To Bias or Not to Bias: Detecting bias in News with bias-detector
paper_summary: Media bias detection is a critical task in ensuring fair and balanced
  information dissemination, yet it remains challenging due to the subjectivity
  of bias and the scarcity of high-quality annotated data. In this work, we
  perform sentence-level bias classification by fine-tuning a RoBERTa-based model
  on the expert-annotated BABE dataset. Using McNemar's test and the 5x2
  cross-validation paired t-test, we show statistically significant improvements
  in performance when comparing our model to a domain-adaptively pre-trained
  DA-RoBERTa baseline. Furthermore, attention-based analysis shows that our model
  avoids common pitfalls like oversensitivity to politically charged terms and
  instead attends more meaningfully to contextually relevant tokens. For a
  comprehensive examination of media bias, we present a pipeline that combines
  our model with an already-existing bias-type classifier. Our method exhibits
  good generalization and interpretability, despite being constrained by
  sentence-level analysis and dataset size because of a lack of larger and more
  advanced bias corpora. We talk about context-aware modeling, bias
  neutralization, and advanced bias type classification as potential future
  directions. Our findings contribute to building more robust, explainable, and
  socially responsible NLP systems for media bias detection.
paper_ai_summary: A RoBERTa-based model fine-tuned on the BABE dataset shows improved performance in sentence-level media bias detection compared to a domain-adaptively pre-trained DA-RoBERTa baseline, with improved attention to contextually relevant tokens.
paper_ai_keywords: RoBERTa BABE dataset McNemar's test 5x2 cross-validation paired t-test DA-RoBERTa attention-based analysis context-aware modeling bias neutralization bias type classification