Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articles

TL;DR

This paper introduces a Transformer-based multi-task learning approach to detect media bias in news articles, focusing on subtle bias indicators like word choice, and demonstrates improved performance over existing methods.

Contribution

It presents a novel Transformer-based multi-task learning architecture trained on multiple bias datasets, achieving state-of-the-art results in media bias detection.

Findings

Achieved macro F1 score of 0.776, outperforming baseline by 3%.

Multi-Task Learning enhances bias detection accuracy.

Outperforms existing methods in identifying subtle media bias.

Abstract

Media has a substantial impact on the public perception of events. A one-sided or polarizing perspective on any topic is usually described as media bias. One of the ways how bias in news articles can be introduced is by altering word choice. Biased word choices are not always obvious, nor do they exhibit high context-dependency. Hence, detecting bias is often difficult. We propose a Transformer-based deep learning architecture trained via Multi-Task Learning using six bias-related data sets to tackle the media bias detection problem. Our best-performing implementation achieves a macro $F_{1}$ of 0.776, a performance boost of 3\% compared to our baseline, outperforming existing methods. Our results indicate Multi-Task Learning as a promising alternative to improve existing baseline models in identifying slanted reporting.