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Measuring Gender Bias in News Images

Published: 18 May 2015 Publication History

Abstract

Analysing the representation of gender in news media has a long history within the fields of journalism, media and communication. Typically this can be performed by measuring how often people of each gender are mentioned within the textual content of news articles. In this paper, we adopt a different approach, classifying the faces in images of news articles into their respective gender. We present a study on $885{,}573$ news articles gathered from the web, covering a period of four months between 19th October 2014 and 19th January 2015 from $882$ news outlets. Findings show that gender bias differs by topic, with Fashion and the Arts showing the least bias. Comparisons of gender bias by outlet suggest that tabloid-style news outlets may be less gender-biased than broadsheet-style ones, supporting previous results from textual content analysis of news articles.

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Cited By

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  • (2024)A broken mirror? From representation to presentation of gender in Scandinavian news mediaNordic Journal of Media Studies10.2478/njms-2024-00056:1(81-109)Online publication date: 2-Sep-2024
  • (2024)Author mentions in science news reveal widespread disparities across name-inferred ethnicitiesQuantitative Science Studies10.1162/qss_a_00297(1-15)Online publication date: 15-Apr-2024
  • (2022)Untraining Ethnocentric Biases about Gender Roles: A Preliminary Empirical Study Presenting Art as StimulusProceedings of Mensch und Computer 202210.1145/3543758.3547543(376-381)Online publication date: 4-Sep-2022
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cover image ACM Other conferences
WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web
May 2015
1602 pages
ISBN:9781450334730
DOI:10.1145/2740908

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  • IW3C2: International World Wide Web Conference Committee

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2015

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Author Tags

  1. gender bias
  2. image classification
  3. news analysis

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WWW '15
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  • IW3C2

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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View all
  • (2024)A broken mirror? From representation to presentation of gender in Scandinavian news mediaNordic Journal of Media Studies10.2478/njms-2024-00056:1(81-109)Online publication date: 2-Sep-2024
  • (2024)Author mentions in science news reveal widespread disparities across name-inferred ethnicitiesQuantitative Science Studies10.1162/qss_a_00297(1-15)Online publication date: 15-Apr-2024
  • (2022)Untraining Ethnocentric Biases about Gender Roles: A Preliminary Empirical Study Presenting Art as StimulusProceedings of Mensch und Computer 202210.1145/3543758.3547543(376-381)Online publication date: 4-Sep-2022
  • (2021)Does Gender Matter in the News? Detecting and Examining Gender Bias in News ArticlesCompanion Proceedings of the Web Conference 202110.1145/3442442.3452325(385-392)Online publication date: 19-Apr-2021
  • (2021)Dynamics of gender bias in computingCommunications of the ACM10.1145/341751764:6(76-83)Online publication date: 24-May-2021
  • (2021)E-MIMIC: Empowering Multilingual Inclusive Communication2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671868(4227-4234)Online publication date: 15-Dec-2021
  • (2020)Diagnosing Gender Bias in Image Recognition SystemsSocius: Sociological Research for a Dynamic World10.1177/23780231209671716Online publication date: 11-Nov-2020
  • (2016)Gender Classification by Deep Learning on Millions of Weakly Labelled Images2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2016.0072(462-467)Online publication date: Dec-2016

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