@inproceedings{shahid-etal-2020-detecting,
title = "Detecting and understanding moral biases in news",
author = "Shahid, Usman and
Di Eugenio, Barbara and
Rojecki, Andrew and
Zheleva, Elena",
editor = "Bonial, Claire and
Caselli, Tommaso and
Chaturvedi, Snigdha and
Clark, Elizabeth and
Huang, Ruihong and
Iyyer, Mohit and
Jaimes, Alejandro and
Ji, Heng and
Martin, Lara J. and
Miller, Ben and
Mitamura, Teruko and
Peng, Nanyun and
Tetreault, Joel",
booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nuse-1.15",
doi = "10.18653/v1/2020.nuse-1.15",
pages = "120--125",
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.",
}
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%0 Conference Proceedings
%T Detecting and understanding moral biases in news
%A Shahid, Usman
%A Di Eugenio, Barbara
%A Rojecki, Andrew
%A Zheleva, Elena
%Y Bonial, Claire
%Y Caselli, Tommaso
%Y Chaturvedi, Snigdha
%Y Clark, Elizabeth
%Y Huang, Ruihong
%Y Iyyer, Mohit
%Y Jaimes, Alejandro
%Y Ji, Heng
%Y Martin, Lara J.
%Y Miller, Ben
%Y Mitamura, Teruko
%Y Peng, Nanyun
%Y Tetreault, Joel
%S Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F shahid-etal-2020-detecting
%X 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.
%R 10.18653/v1/2020.nuse-1.15
%U https://aclanthology.org/2020.nuse-1.15
%U https://doi.org/10.18653/v1/2020.nuse-1.15
%P 120-125
Markdown (Informal)
[Detecting and understanding moral biases in news](https://aclanthology.org/2020.nuse-1.15) (Shahid et al., NUSE-WNU 2020)
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.