Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity
Wei-Fan Chen, Khalid Al Khatib, Henning Wachsmuth, Benno Stein
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.- Anthology ID:
- 2020.nlpcss-1.16
- Volume:
- Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- NLP+CSS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 149–154
- Language:
- URL:
- https://aclanthology.org/2020.nlpcss-1.16
- DOI:
- 10.18653/v1/2020.nlpcss-1.16
- Cite (ACL):
- Wei-Fan Chen, Khalid Al Khatib, Henning Wachsmuth, and Benno Stein. 2020. Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity. In Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pages 149–154, Online. Association for Computational Linguistics.
- Cite (Informal):
- Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity (Chen et al., NLP+CSS 2020)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2020.nlpcss-1.16.pdf
- Code
- webis-de/NLPCSS-20