To Bias or Not to Bias: Detecting bias in News with bias-detector

TL;DR

This paper introduces a RoBERTa-based model for sentence-level media bias detection, demonstrating statistically significant improvements and interpretability, contributing to more robust and explainable bias detection systems.

Contribution

The work presents a fine-tuned RoBERTa model with attention analysis and a bias detection pipeline, advancing media bias detection with improved performance and interpretability.

Findings

Significant performance improvements over baseline models.

Attention analysis reveals meaningful focus on contextually relevant tokens.

The model generalizes well despite dataset limitations.

Abstract

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.