Unveiling digital mirrors: Decoding gendered body poses in instagram imagery

Social media platforms have had a significant impact on people's everyday lives worldwide and imagery on social media has become a vital means of practicing views of the self and communicating them to others. Albeit a significant proportion of people frequently upload self-representations on social media, this source of information on individuals, groups, and societies remains under-examined within the social sciences and neighboring disciplines. In our study, we focus on gender-stereotypical body-posing in self-portraits on social media. While sociology has examined gender stereotypes for decades, research lacks empirical evidence on representations in digital contexts as well as novel forms of stereotyping. We present a scalable and transferable methodology for analyzing body poses in images by combining neural network pose detection with an unsupervised learning approach and applying this methodology to data from the social media platform Instagram. Based on a clustering algorithm applied to gender-annotated imagery, we identify 150 body posing clusters. Our results reveal significant gender differences in 20 percent of clusters, many of which represent gender-stereotypical body poses addressed in sociological literature. Moreover, we can identify new stereotypical poses related to smartphone technology and social media trends. This study represents a novel approach to utilizing large-scale image data for social science research and contributes to a better understanding of the consolidation and reproduction of gender stereotypes in digital realms.