Social media platforms and other online spaces form a large part of today’s (everyday) culture. Therefore, scholars across various fields are increasingly concerned with the entanglement of media practices, self-presentation, and body images on social media (Aparicio-Martinez et al., 2019; Chua & Chang, 2016; Cruz-Sáez et al., 2020; Mahon & Hevey, 2021). What is still missing from the literature, however, is the intersection between body images, algorithmic systems, and power relations in the context of educational research.

This symposium thus explores ways in which algorithmic systems can shape body images by taking data-informed discrimination (Chun, 2021a), network gaps (Chun, 2021b) and AI/ML systems (Crawford & Paglen, 2019) into account. Our assumption is that algorithmic systems with their recommendations (Seaver, 2022) of whom to follow and what to see next are producing a mostly affirming and normalizing social stream that might have a significant impact on what body images are circulating. So rather, it can be assumed that there is a lack of diversity due to algorithmic influence. And this is where educational science should respond.

With the theme ‘Normalizing the body’, we focus on the role of algorithms in processes of constructing body images that fit societal - often ‘Western‘ - norms and expectations. We critically question whether the initially widespread promise that digitalization and, specifically, the internet and social media platforms, will make participation and representation more diverse can be fulfilled when algorithmic systems are based on discriminatory data. 

As such, this symposium addresses the potential biases in and limitations of algorithmic systems, and how these may impact the re-presentation and portrayal of diverse bodies. We do this through three papers: The first, Designing the ‘normal’ body, critically reflects on the normalization of menstruating bodies in the context of self-tracking apps and socio-technical feedback loops. With media educational and biopolitical considerations in mind, the paper argues that algorithmic recommendations within menstrual cycle tracker apps have a disciplinary effect since they (re-)produce norms and normalities of (menstruating) bodies. The second paper, Damn Data!, explores the practices and complex entanglements of AI in creative articulative processes as part of media education. By doing so, on the one hand, the paper highlights the explorative potential of AI/ML Systems in the creative play on re-presenting bodies, on the other hand, it reflects the inherent contingency of digital media practices. The third and final paper, Beauty and the biased, explores diversity on TikTok, focusing on issues of content regulation and biased data used in recommendation systems. To respond to this from an educational perspective, the paper looks at how media education can help us recognize when actions and policies take different forms than intended when it takes into account the power relations inherent in content regulation and media practices.

All contributions start from an educational perspective by also including such approaches that consider the interdisciplinarity of the subject. By doing so, they pursue the same pressing question: How can and must we think and research diversity in educational science, taking into account increasing algorithmic influences? 

Aparicio-Martinez, Perea-Moreno, Martinez-Jimenez, Redel-Macías, Pagliari, & Vaquero-Abellan. (2019). Social Media, Thin-Ideal, Body Dissatisfaction and Disordered Eating Attitudes: An Exploratory Analysis. International Journal of Environmental Research and Public Health, 16(21), 4177. https://doi.org/10.3390/ijerph16214177 Chua, T. H. H., & Chang, L. (2016). Follow me and like my beautiful selfies: Singapore teenage girls’ engagement in self-presentation and peer comparison on social media. Computers in Human Behavior, 55, 190–197. https://doi.org/10.1016/j.chb.2015.09.011 Chun, W. H. K. (2021a). Discriminating data: Correlation, neighborhoods, and the new politics of recognition. The MIT Press. Chun, W. H. K. (2021b). The Space between Us: Network Gaps, Racism, and the Possibilities of Living in/Difference. Catalyst: Feminism, Theory, Technoscience, 7(2). https://doi.org/10.28968/cftt.v7i2.34903 Crawford, K. & Paglen, T. (2019). “Excavating AI: The Politics of Training Sets for Machine Learning (September 19, 2019) https://excavating.ai (last access: 14.12.2022) Cruz-Sáez, S., Pascual, A., Wlodarczyk, A., & Echeburúa, E. (2020). The effect of body dissatisfaction on disordered eating: The mediating role of self-esteem and negative affect in male and female adolescents. Journal of Health Psychology, 25(8), 1098–1108. https://doi.org/10.1177/1359105317748734 Mahon, C., & Hevey, D. (2021). Processing Body Image on Social Media: Gender Differences in Adolescent Boys’ and Girls’ Agency and Active Coping. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.626763 Seaver, N. (2022). Computing taste: Algorithms and the makers of music recommendation. University of Chicago Press.