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.

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Cited by in corpus (6)

• Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection
• A Domain-adaptive Pre-training Approach for Language Bias Detection in News
• ChatGPT v.s. Media Bias: A Comparative Study of GPT-3.5 and Fine-tuned Language Models
• Analyzing Multi-Task Learning for Abstractive Text Summarization
• Unraveling Media Perspectives: A Comprehensive Methodology Combining Large Language Models, Topic Modeling, Sentiment Analysis, and Ontology Learning to Analyse Media Bias
• DocNet: Semantic Structure in Inductive Bias Detection Models