To Bias or Not to Bias: Detecting bias in News with bias-detector
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.
682c46c70f622b7afc2e6f8f
Himel Ghosh
extracted_confirmed
2025-05-21T09:42:53.022Z
67d5c51817d572b960f6982a
/avatars/2c22663b27326b2300444bcc84ebbdd7.svg
Himel Ghosh
user
himel7
682c46c70f622b7afc2e6f90
Ahmed Mosharafa
admin_assigned
2025-05-21T10:24:54.616Z
62d0859afb896639b29c45c8
/avatars/5309851f11c875f15f80a7415a4ae7f3.svg
Ahmed Mosharafa
user
amosharafa
682c46c70f622b7afc2e6f91
Georg Groh
admin_assigned
2025-05-21T10:25:00.667Z
662a6560925d2af5b0e13a34
/avatars/d45a057e6bbde14f54811be34ae6bc64.svg
Georg Groh
user
grohg
To Bias or Not to Bias: Detecting bias in News with bias-detector
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.
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.
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