The Impact of Social Media on Sexual Self-Concept: A Qualitative Analysis of Instagram in Mexico

This study explores the complexities of women's self-representation on social media, examining its potential detrimental effects and consequences. This study examines image exposure through the lens of Machine Learning, within the framework of Instagram's visual culture. By examining digital content and its impact on users, this study aims to shed light on the detrimental effects of objectification and harmful stereotypes on individuals and society. In this study, we explore women's self-representation through the portrayal of their bodies. Leveraging Google Vision API, we investigate the feasibility of automating the identification of imagery among women users located in Mexico. Our findings reveal that Google Vision API is a useful tool for detecting racy and adult content. We identify a quantitative evidence between clothing and body parts in these images, suggesting that women often share photos in revealing attire. Contrasting conventional survey and manual labeling methods, our results highlight the potential use of machine learning techniques to analyze this sort of imagery. The analysis reveals that over 90% of the examined images contain some degree of nudity or provocative poses. These findings provide valuable insights into the self-representation of women on social media, highlighting prevailing visual trends and their potential sociocultural implications.