A Domain-Adaptive Pre-Training Approach for Language Bias Detection in News. Krieger, J., Spinde, T., Ruas, T., Kulshrestha, J., & Gipp, B. In Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries, of JCDL '22, New York, NY, USA, June, 2022. Association for Computing Machinery. Number of pages: 7 Place: Cologne, Germany tex.articleno: 3![A Domain-Adaptive Pre-Training Approach for Language Bias Detection in News [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@inproceedings{krieger_domain-adaptive_2022,
address = {New York, NY, USA},
series = {{JCDL} '22},
title = {A {Domain}-{Adaptive} {Pre}-{Training} {Approach} for {Language} {Bias} {Detection} in {News}},
isbn = {978-1-4503-9345-4},
url = {https://doi.org/10.1145/3529372.3530932},
doi = {10.1145/3529372.3530932},
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.},
booktitle = {Proceedings of the 22nd {ACM}/{IEEE} {Joint} {Conference} on {Digital} {Libraries}},
publisher = {Association for Computing Machinery},
author = {Krieger, Jan-David and Spinde, Timo and Ruas, Terry and Kulshrestha, Juhi and Gipp, Bela},
month = jun,
year = {2022},
note = {Number of pages: 7
Place: Cologne, Germany
tex.articleno: 3},
keywords = {domain adaptive, media bias, neural classification, news slant, text analysis},
}
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