Uncovering gender bias in newspaper coverage of Irish politicians using machine learning.
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...
| Publicado en: | Digital Scholarship in the Humanities Vol. 34; no. 1; pp. 48 - 64 |
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| Formato: | Artículo |
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Oxford University Press / USA
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=135432211&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 135432211 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Apr2019 vid: 34 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 135432211 10.1093/llc/fqy005 ppf: 48 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P size: 246KB tig: atl: Uncovering gender bias in newspaper coverage of Irish politicians using machine learning. aug: au: Leavy, Susan affil: University College Dublin, Ireland su: Sex discrimination Machine learning Politicians Journalistic reporting Research methodology Natural language processing Ireland sug: subj: Ireland Sex discrimination Machine learning Politicians Journalistic reporting Research methodology Natural language processing ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2019 holdings: @attributes: islocal: N |
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