Sentence-level Media Bias Analysis Informed by Discourse Structures

Yuanyuan Lei, Ruihong Huang, Lu Wang, Nick Beauchamp


Abstract
As polarization continues to rise among both the public and the news media, increasing attention has been devoted to detecting media bias. Most recent work in the NLP community, however, identify bias at the level of individual articles. However, each article itself comprises multiple sentences, which vary in their ideological bias. In this paper, we aim to identify sentences within an article that can illuminate and explain the overall bias of the entire article. We show that understanding the discourse role of a sentence in telling a news story, as well as its relation with nearby sentences, can reveal the ideological leanings of an author even when the sentence itself appears merely neutral. In particular, we consider using a functional news discourse structure and PDTB discourse relations to inform bias sentence identification, and distill the auxiliary knowledge from the two types of discourse structure into our bias sentence identification system. Experimental results on benchmark datasets show that incorporating both the global functional discourse structure and local rhetorical discourse relations can effectively increase the recall of bias sentence identification by 8.27% - 8.62%, as well as increase the precision by 2.82% - 3.48%.
Anthology ID:
2022.emnlp-main.682
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10040–10050
Language:
URL:
https://aclanthology.org/2022.emnlp-main.682
DOI:
10.18653/v1/2022.emnlp-main.682
Bibkey:
Cite (ACL):
Yuanyuan Lei, Ruihong Huang, Lu Wang, and Nick Beauchamp. 2022. Sentence-level Media Bias Analysis Informed by Discourse Structures. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10040–10050, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Sentence-level Media Bias Analysis Informed by Discourse Structures (Lei et al., EMNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.emnlp-main.682.pdf