Occupational Visibility on YouTube : Gender and Skill-Level Biases in Video Recommendations

Abstract

"This study investigates the representation of gender and skill biases in YouTube’s video recommendation system for occupations in Germany. Using a dataset of 526,535 synonyms and variants of male, female, and neutral job titles, we analyse recommendations across a broad set of occupational domains, including computer science, preschool teaching, food manufacturing, mechatronics, police service, interior architecture, hairdressing, domestic services, and sales. This selection covers a wide spectrum of professions with different gender distributions and skill levels, ranging from helper roles to highly complex specialist tasks. The analysis reveals nuanced patterns in how video recommendations respond to gendered occupational terms. In female-dominated professions such aschild care, hairdressing, and domestic services, recommendations retrieved via male- and female-coded search terms show considerable overlap. In contrast, male-dominated fields such as mechatronics and police service display less consistent intersections, and in some cases, videos retrieved using neutral occupational terms exhibit a disproportionately higher share of negative sentiment. A detailed analysis of metadata and word frequency patterns highlights the influence of linguistic framing, educational focus, and cultural associations in shaping algorithmic recommendations. However, these factors alone do not fully account for the sentiment distributions or intersection structures observed. The findings underline the importance of multi-method research approaches to uncover algorithmic bias, and they point toward practical implications for platform developers, regulatory bodies, and media literacy initia tives. This work contributes to the broader discourse on fairness and inclusion in algorithmically mediated digital environments." (Authors' abstract, BIBB-Doku)