To Bias or Not to Bias: Detecting bias in News with bias-detector
Authors: name: Himel Ghosh, name: Ahmed Mosharafa, name: Georg Groh

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.