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Female librarians and male computer programmers? Gender bias in occupational images on digital media platforms

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  • Vivek K. Singh
  • Mary Chayko
  • Raj Inamdar
  • Diana Floegel

Abstract

Media platforms, technological systems, and search engines act as conduits and gatekeepers for all kinds of information. They often influence, reflect, and reinforce gender stereotypes, including those that represent occupations. This study examines the prevalence of gender stereotypes on digital media platforms and considers how human efforts to create and curate messages directly may impact these stereotypes. While gender stereotyping in social media and algorithms has received some examination in the recent literature, its prevalence in different types of platforms (for example, wiki vs. news vs. social network) and under differing conditions (for example, degrees of human‐ and machine‐led content creation and curation) has yet to be studied. This research explores the extent to which stereotypes of certain strongly gendered professions (librarian, nurse, computer programmer, civil engineer) persist and may vary across digital platforms (Twitter, the New York Times online, Wikipedia, and Shutterstock). The results suggest that gender stereotypes are most likely to be challenged when human beings act directly to create and curate content in digital platforms, and that highly algorithmic approaches for curation showed little inclination towards breaking stereotypes. Implications for the more inclusive design and use of digital media platforms, particularly with regard to mediated occupational messaging, are discussed.

Suggested Citation

  • Vivek K. Singh & Mary Chayko & Raj Inamdar & Diana Floegel, 2020. "Female librarians and male computer programmers? Gender bias in occupational images on digital media platforms," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(11), pages 1281-1294, November.
    • Handle: RePEc:bla:jinfst:v:71:y:2020:i:11:p:1281-1294
    DOI: 10.1002/asi.24335
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    References listed on IDEAS

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    4. Amaç Herdağdelen & Marco Baroni, 2011. "Stereotypical gender actions can be extracted from web text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(9), pages 1741-1749, September.
    5. Amaç Herdağdelen & Marco Baroni, 2011. "Stereotypical gender actions can be extracted from web text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(9), pages 1741-1749, September.
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    Cited by:

    1. Sudeshna Das & Jiaul H. Paik, 2023. "Gender tagging of named entities using retrieval‐assisted multi‐context aggregation: An unsupervised approach," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(4), pages 461-475, April.

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