Age and Gender Representation on German TV

A Longitudinal Computational Analysis

Abstract

Television offers an enticing glimpse into the world, but its perspective is often skewed. When societal groups are systematically excluded from appearing on the screen, they lose the chance to represent their characteristics and interests. Recipients may then form distorted perceptions and attitudes towards those groups. Empirical research on the prevalence of such biases - especially across stations, time, and genre - has been limited by the effort of manual content analyses. We develop and validate a deep-learning based method for measuring age and gender of faces in video material. An analysis of approximately 16 million faces from six years of German mainstream TV across six stations is fused with existing program metadata indicating timing and genre of broadcasts, including advertisements. Multilevel regression models show a consistent and temporally stable discrimination against women and elderly people, along with a double discrimination of elderly women. A significant amount of variation across genres and systematic differences between public and private broadcasters furthermore indicate previously undocumented heterogeneity in the representation of societal groups on TV. We discuss potential implications of a genre-specific differentiation against the backdrop of societal trends.