@inproceedings{lazaridou-etal-2020-discovering,
title = "Discovering Biased News Articles Leveraging Multiple Human Annotations",
author = {Lazaridou, Konstantina and
L{\"o}ser, Alexander and
Mestre, Maria and
Naumann, Felix},
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.159",
pages = "1268--1277",
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.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<namePart type="given">Sara</namePart>
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<namePart type="given">Hitoshi</namePart>
<namePart type="family">Isahara</namePart>
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<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.</abstract>
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%0 Conference Proceedings
%T Discovering Biased News Articles Leveraging Multiple Human Annotations
%A Lazaridou, Konstantina
%A Löser, Alexander
%A Mestre, Maria
%A Naumann, Felix
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F lazaridou-etal-2020-discovering
%X 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.
%U https://aclanthology.org/2020.lrec-1.159
%P 1268-1277
Markdown (Informal)
[Discovering Biased News Articles Leveraging Multiple Human Annotations](https://aclanthology.org/2020.lrec-1.159) (Lazaridou et al., LREC 2020)
ACL