Detecting and understanding moral biases in news
Usman Shahid, Barbara Di Eugenio, Andrew Rojecki, Elena Zheleva
Abstract
We describe work in progress on detecting and understanding the moral biases of news sources by combining framing theory with natural language processing. First we draw connections between issue-specific frames and moral frames that apply to all issues. Then we analyze the connection between moral frame presence and news source political leaning. We develop and test a simple classification model for detecting the presence of a moral frame, highlighting the need for more sophisticated models. We also discuss some of the annotation and frame detection challenges that can inform future research in this area.- Anthology ID:
- 2020.nuse-1.15
- Volume:
- Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Claire Bonial, Tommaso Caselli, Snigdha Chaturvedi, Elizabeth Clark, Ruihong Huang, Mohit Iyyer, Alejandro Jaimes, Heng Ji, Lara J. Martin, Ben Miller, Teruko Mitamura, Nanyun Peng, Joel Tetreault
- Venues:
- NUSE | WNU
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 120–125
- Language:
- URL:
- https://aclanthology.org/2020.nuse-1.15
- DOI:
- 10.18653/v1/2020.nuse-1.15
- Cite (ACL):
- Usman Shahid, Barbara Di Eugenio, Andrew Rojecki, and Elena Zheleva. 2020. Detecting and understanding moral biases in news. In Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events, pages 120–125, Online. Association for Computational Linguistics.
- Cite (Informal):
- Detecting and understanding moral biases in news (Shahid et al., NUSE-WNU 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.nuse-1.15.pdf