Target-Aware Contextual Political Bias Detection in News

TL;DR

This paper introduces a target-aware, bias-sensitive data augmentation method for sentence-level political bias detection in news, significantly improving performance by reducing noise and leveraging contextual information.

Contribution

It presents a novel target-aware augmentation technique that enhances bias detection accuracy by better capturing context and reducing over-generalization issues.

Findings

Achieved state-of-the-art F1-score of 58.15 on BASIL dataset.

Outperformed previous methods significantly.

Effective when combined with pre-trained models like BERT.

Abstract

Media bias detection requires comprehensive integration of information derived from multiple news sources. Sentence-level political bias detection in news is no exception, and has proven to be a challenging task that requires an understanding of bias in consideration of the context. Inspired by the fact that humans exhibit varying degrees of writing styles, resulting in a diverse range of statements with different local and global contexts, previous work in media bias detection has proposed augmentation techniques to exploit this fact. Despite their success, we observe that these techniques introduce noise by over-generalizing bias context boundaries, which hinders performance. To alleviate this issue, we propose techniques to more carefully search for context using a bias-sensitive, target-aware approach for data augmentation. Comprehensive experiments on the well-known BASIL dataset show that when combined with pre-trained models such as BERT, our augmentation techniques lead to state-of-the-art results. Our approach outperforms previous methods significantly, obtaining an F1-score of 58.15 over state-of-the-art bias detection task.