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Uncovering gender bias in newspaper coverage of Irish politicians using machine learning
Author(s)
Date Issued
2018-06-09
Date Available
2019-03-14T08:54:28Z
Abstract
This article presents a text-analytic approach to analysing media content for evidence of gender bias. Irish newspaper content is examined using machine learning and natural language processing techniques. Systematic differences in the coverage of male and female politicians are uncovered, and these differences are analysed for evidence of gender bias. A corpus of newspaper coverage of politicians over a 15-year period was created. Features of the text were extracted and patterns differentiating coverage of male and female politicians were identified using machine learning. Discriminative features were then analysed for evidence of gender bias. Findings showed evidence of gender bias in how female politicians were portrayed, the policies they were associated with, and how they were evaluated. This research also sets out a methodology whereby natural language processing and machine learning can be used to identify gender bias in media coverage of politicians.
Type of Material
Journal Article
Publisher
Oxford University Press
Journal
Digital Scholarship in the Humanities
Volume
34
Issue
1
Start Page
48
End Page
63
Copyright (Published Version)
2018 the Authors
Language
English
Status of Item
Peer reviewed
ISSN
2055-7671
This item is made available under a Creative Commons License
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