Predicting Political Orientation in News with Latent Discourse Structure to Improve Bias Understanding

Nicolas Devatine, Philippe Muller, Chloé Braud


Abstract
With the growing number of information sources, the problem of media bias becomes worrying for a democratic society. This paper explores the task of predicting the political orientation of news articles, with a goal of analyzing how bias is expressed. We demonstrate that integrating rhetorical dimensions via latent structures over sub-sentential discourse units allows for large improvements, with a +7.4 points difference between the base LSTM model and its discourse-based version, and +3 points improvement over the previous BERT-based state-of-the-art model. We also argue that this gives a new relevant handle for analyzing political bias in news articles.
Anthology ID:
2022.codi-1.10
Volume:
Proceedings of the 3rd Workshop on Computational Approaches to Discourse
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea and Online
Venue:
CODI
SIG:
Publisher:
International Conference on Computational Linguistics
Note:
Pages:
77–85
Language:
URL:
https://aclanthology.org/2022.codi-1.10
DOI:
Bibkey:
Cite (ACL):
Nicolas Devatine, Philippe Muller, and Chloé Braud. 2022. Predicting Political Orientation in News with Latent Discourse Structure to Improve Bias Understanding. In Proceedings of the 3rd Workshop on Computational Approaches to Discourse, pages 77–85, Gyeongju, Republic of Korea and Online. International Conference on Computational Linguistics.
Cite (Informal):
Predicting Political Orientation in News with Latent Discourse Structure to Improve Bias Understanding (Devatine et al., CODI 2022)
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PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2022.codi-1.10.pdf
Code
 neops9/news_political_bias