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

• URL: http://arxiv.org/abs/2211.03491v1
• Date: Mon, 7 Nov 2022 12:22:31 GMT
• Title: Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articles
• Authors: Timo Spinde, Jan-David Krieger, Terry Ruas, Jelena Mitrovi\'c, Franz G\"otz-Hahn, Akiko Aizawa, and Bela Gipp
• Abstract summary: We propose a Transformer-based deep learning architecture trained via Multi-Task Learning to detect media bias. Our best-performing implementation achieves a macro $F_1$ of 0.776, a performance boost of 3% compared to our baseline, outperforming existing methods.
• Score: 21.960154864540282
• License: http://creativecommons.org/licenses/by/4.0/
• 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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