Discovering Biased News Articles Leveraging Multiple Human Annotations

Konstantina Lazaridou, Alexander Löser, Maria Mestre, Felix Naumann


Abstract
Unbiased and fair reporting is an integral part of ethical journalism. Yet, political propaganda and one-sided views can be found in the news and can cause distrust in media. Both accidental and deliberate political bias affect the readers and shape their views. We contribute to a trustworthy media ecosystem by automatically identifying politically biased news articles. We introduce novel corpora annotated by two communities, i.e., domain experts and crowd workers, and we also consider automatic article labels inferred by the newspapers’ ideologies. Our goal is to compare domain experts to crowd workers and also to prove that media bias can be detected automatically. We classify news articles with a neural network and we also improve our performance in a self-supervised manner.
Anthology ID:
2020.lrec-1.159
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1268–1277
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.159
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.159.pdf