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

• URL: http://arxiv.org/abs/2205.10773v1
• Date: Sun, 22 May 2022 08:18:19 GMT
• Title: A Domain-adaptive Pre-training Approach for Language Bias Detection in News
• Authors: Jan-David Krieger, Timo Spinde, Terry Ruas, Juhi Kulshrestha, Bela Gipp
• Abstract summary: We present DA-RoBERTa, a new state-of-the-art transformer-based model adapted to the media bias domain. We also train, DA-BERT and DA-BART, two more transformer models adapted to the bias domain. Our proposed domain-adapted models outperform prior bias detection approaches on the same data.
• Score: 3.7238620986236373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Media bias is a multi-faceted construct influencing individual behavior and collective decision-making. Slanted news reporting is the result of one-sided and polarized writing which can occur in various forms. In this work, we focus on an important form of media bias, i.e. bias by word choice. Detecting biased word choices is a challenging task due to its linguistic complexity and the lack of representative gold-standard corpora. We present DA-RoBERTa, a new state-of-the-art transformer-based model adapted to the media bias domain which identifies sentence-level bias with an F1 score of 0.814. In addition, we also train, DA-BERT and DA-BART, two more transformer models adapted to the bias domain. Our proposed domain-adapted models outperform prior bias detection approaches on the same data.

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