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.
Original language | English |
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Title of host publication | JCDL 2022 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2022 |
Publisher | ACM |
ISBN (Electronic) | 9781450393454 |
DOIs | |
Publication status | Published - Jun 2022 |
MoE publication type | A4 Article in a conference publication |
Event | ACM/IEEE Joint Conference on Digital Libraries - Cologne, Germany Duration: 20 Jun 2022 → 24 Jun 2022 Conference number: 22 |
Conference
Conference | ACM/IEEE Joint Conference on Digital Libraries |
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Abbreviated title | JCDL |
Country/Territory | Germany |
City | Cologne |
Period | 20/06/2022 → 24/06/2022 |
Keywords
- Domain adaptive
- Media bias
- Neural classification
- News slant
- Text analysis