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

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.

Gendered Sounds in Household Devices: Results from an Online Search Case Study

“…Overall, there were more male‐sounding devices (155) than female‐sounding devices (90). The overrepresentation of male‐associated search results on the internet is consistent with past studies (Singh et al, 2020). However, the results did not match the gender stereotype expected in five of the six categories.…”

“…The first potential source of bias includes the search engine, where the ratio of search engine results may be biased. There is an overrepresentation of maleness on the internet (Singh et al, 2020). Next, the dataset is relatively small, and the labels were assigned manually by a single human annotator.…”

“…These systems then influence and reflect stereotypes, including those in the household and gender domains (Herdağdelen, 2011;Noble 2018Noble , 2013. There is well documented evidence of marginalization based on gender in print and electronic media as well as digitally mediated platforms (Singh, Chayko, Inamdar, & Floegel, 2020;Arslan & Koca, 2007;Bligh, Schlehofer, Casad, & Gaffney, 2011;Sink & Mastro 2017). These media reinforce "traditional" stereotypes but can also serve as useful examples for counter-stereotypical imagery in certain situations (Singh et al, 2020).…”

“…There is well documented evidence of marginalization based on gender in print and electronic media as well as digitally mediated platforms (Singh, Chayko, Inamdar, & Floegel, 2020;Arslan & Koca, 2007;Bligh, Schlehofer, Casad, & Gaffney, 2011;Sink & Mastro 2017). These media reinforce "traditional" stereotypes but can also serve as useful examples for counter-stereotypical imagery in certain situations (Singh et al, 2020). Specialized smart devices with computerized voices, which are becoming ubiquitous in households, are currently understudied with respect to potential gender bias (Anderson & Rainie, 2018).…”

Digital news on public libraries: Media mapping and thematic and consumption analysis based on Facebook interactions

“…-Yu and Chen (2016) analyze the link between libraries and digital news with the aim of increasing their visibility and promoting the consumption of information about libraries. -Singh et al (2020) focus on the gender bias of the library profession in digital media, including in the electronic version of The New York Times.…”