@inproceedings{chen-etal-2020-analyzing,
title = "Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity",
author = "Chen, Wei-Fan and
Al Khatib, Khalid and
Wachsmuth, Henning and
Stein, Benno",
editor = "Bamman, David and
Hovy, Dirk and
Jurgens, David and
O'Connor, Brendan and
Volkova, Svitlana",
booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcss-1.16",
doi = "10.18653/v1/2020.nlpcss-1.16",
pages = "149--154",
abstract = "Media is an indispensable source of information and opinion, shaping the beliefs and attitudes of our society. Obviously, media portals can also provide overly biased content, e.g., by reporting on political events in a selective or incomplete manner. A relevant question hence is whether and how such a form of unfair news coverage can be exposed. This paper addresses the automatic detection of bias, but it goes one step further in that it explores how political bias and unfairness are manifested linguistically. We utilize a new corpus of 6964 news articles with labels derived from adfontesmedia.com to develop a neural model for bias assessment. Analyzing the model on article excerpts, we find insightful bias patterns at different levels of text granularity, from single words to the whole article discourse.",
}
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<abstract>Media is an indispensable source of information and opinion, shaping the beliefs and attitudes of our society. Obviously, media portals can also provide overly biased content, e.g., by reporting on political events in a selective or incomplete manner. A relevant question hence is whether and how such a form of unfair news coverage can be exposed. This paper addresses the automatic detection of bias, but it goes one step further in that it explores how political bias and unfairness are manifested linguistically. We utilize a new corpus of 6964 news articles with labels derived from adfontesmedia.com to develop a neural model for bias assessment. Analyzing the model on article excerpts, we find insightful bias patterns at different levels of text granularity, from single words to the whole article discourse.</abstract>
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%0 Conference Proceedings
%T Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity
%A Chen, Wei-Fan
%A Al Khatib, Khalid
%A Wachsmuth, Henning
%A Stein, Benno
%Y Bamman, David
%Y Hovy, Dirk
%Y Jurgens, David
%Y O’Connor, Brendan
%Y Volkova, Svitlana
%S Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F chen-etal-2020-analyzing
%X Media is an indispensable source of information and opinion, shaping the beliefs and attitudes of our society. Obviously, media portals can also provide overly biased content, e.g., by reporting on political events in a selective or incomplete manner. A relevant question hence is whether and how such a form of unfair news coverage can be exposed. This paper addresses the automatic detection of bias, but it goes one step further in that it explores how political bias and unfairness are manifested linguistically. We utilize a new corpus of 6964 news articles with labels derived from adfontesmedia.com to develop a neural model for bias assessment. Analyzing the model on article excerpts, we find insightful bias patterns at different levels of text granularity, from single words to the whole article discourse.
%R 10.18653/v1/2020.nlpcss-1.16
%U https://aclanthology.org/2020.nlpcss-1.16
%U https://doi.org/10.18653/v1/2020.nlpcss-1.16
%P 149-154
Markdown (Informal)
[Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity](https://aclanthology.org/2020.nlpcss-1.16) (Chen et al., NLP+CSS 2020)
ACL