Sentence-level Media Bias Analysis with Event Relation Graph

Yuanyuan Lei, Ruihong Huang


Abstract
Media outlets are becoming more partisan and polarized nowadays. In this paper, we identify media bias at the sentence level, and pinpoint bias sentences that intend to sway readers’ opinions. As bias sentences are often expressed in a neutral and factual way, considering broader context outside a sentence can help reveal the bias. In particular, we observe that events in a bias sentence need to be understood in associations with other events in the document. Therefore, we propose to construct an event relation graph to explicitly reason about event-event relations for sentence-level bias identification. The designed event relation graph consists of events as nodes and four common types of event relations: coreference, temporal, causal, and subevent relations. Then, we incorporate event relation graph for bias sentences identification in two steps: an event-aware language model is built to inject the events and event relations knowledge into the basic language model via soft labels; further, a relation-aware graph attention network is designed to update sentence embedding with events and event relations information based on hard labels. Experiments on two benchmark datasets demonstrate that our approach with the aid of event relation graph improves both precision and recall of bias sentence identification.
Anthology ID:
2024.naacl-long.292
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5225–5238
Language:
URL:
https://aclanthology.org/2024.naacl-long.292
DOI:
Bibkey:
Cite (ACL):
Yuanyuan Lei and Ruihong Huang. 2024. Sentence-level Media Bias Analysis with Event Relation Graph. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 5225–5238, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Sentence-level Media Bias Analysis with Event Relation Graph (Lei & Huang, NAACL 2024)
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https://aclanthology.org/2024.naacl-long.292.pdf
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