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

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.

References in corpus (4)

• An Overview of Multi-Task Learning in Deep Neural Networks
• SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation
• Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias
• Identification of Biased Terms in News Articles by Comparison of Outlet-specific Word Embeddings

Cited by in corpus (1)

• A Domain-adaptive Pre-training Approach for Language Bias Detection in News