Gender bias and stereotypes corresponding to certain occupations are prevalent on digital and social media platforms like Twitter, say researchers, including those of Indian origin.
The researchers noted that online images of men and women in four professions -- librarian, nurse, computer programmer, and civil engineer -- tend to represent and reinforce existing gender stereotypes.
The study, published in the Journal of the Association for Information Science and Technology, analysed search results for images of people in each of the four occupations on four digital media platforms: Twitter, NYTimes.com, Wikipedia, and Shutterstock.
The researchers, including Vivek Singh and Raj Inamdar from Rutgers University in the US, also compared the search results to the gender representation of each occupation as per the US Bureau of Labor Statistics.
The results showed gender stereotypes and biases to be prevalent.
Women were over-represented as librarians and nurses, and under-represented as computer programmers and civil engineers, especially when the collection and curation of content is largely automated by an algorithm, such as on Twitter.
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However, on platforms where individuals can generate and curate content more directly, such as the NYTimes.com and Shutterstock, stereotypes were more likely to be challenged, the researchers said.
Search results of NYTimes.com, for example, produced images of civil engineers who are women, and nurses who are men, more often than would be expected given their representation in the Labor Statistics, they said.
"More direct content curation will help counter gender stereotypes," said Singh.
While women generally tend to be underrepresented in male-dominated professions on digital media platforms, Singh noted some progress towards equity in the gendered presentation of images from 2018 to 2019.
For instance, more women were shown in images for male-dominated professions on Twitter in 2019 than in 2018, the researchers said.
"Gender bias limits the ability of people to select careers that may suit them and impedes fair practices, pay equity and equality," said Mary Chayko from Rutgers.
"Understanding the prevalence and patterns of bias and stereotypes in online images is essential, and can help us challenge, and hopefully someday break, these stereotypes," Chayko said.
The researchers said that the study could help prevent biases from being designed into digital media platforms, algorithms, and artificial intelligence (AI) software.
The study's results may help content creators and platform designers identify whether algorithm-heavy or human-heavy curation may be better suited to a task, they said.