Young, lower-class, and algorithmically persuaded: exploring personalized advertising and its impact on social inequality

Abstract

Algorithmically personalized advertising is a defining feature of the digital ecosystem, yet its potential to reinforce social inequalities remains underexplored. This study examines how socioeconomic status and gender intersect to influence exposure to personalized advertisements on platforms like Instagram and TikTok. Using online survey data from 1,200 participants aged 16 to 25, regression analyses reveal that young people from lower socioeconomic backgrounds are disproportionately targeted with ads promising easy income, quick earnings, and social mobility. They are also more likely to encounter ads related to gambling, online games, and financial services such as quick loans. These patterns are particularly pronounced among young men. Our findings highlight how social media platforms leverage massive data collection to infer sensitive attributes such as socioeconomic status, which includes complex information like education level, employment status, income, or migration background. This profiling exacerbates inequalities, as algorithmic advertising perpetuates both class-based barriers and gender stereotypes. Women are predominantly targeted with ads related to beauty, parenting, and education, while men are more exposed to ads for sports, alcohol, and politics. By applying an intersectional lens, this study underscores how algorithmic systems exploit vulnerabilities tied to overlapping identities, trapping the most vulnerable youth in a cycle of digital inequality. These findings emphasize the need for greater transparency in advertising algorithms and robust regulatory measures to ensure that personalization technologies foster equity rather than deepening societal divides.