Gender representation in mass media has long been mainly studied by qualitatively analyzing content. This article illustrates how automated computational methods may be used in this context to scale up such empirical observations and increase their resolution and significance. We specifically apply a face and gender detection algorithm on a broad set of popular movies spanning more than three decades to carry out a large-scale appraisal of the on-screen presence of women and men. Beyond the confirmation of a strong under-representation of women, we exhibit a clear temporal trend towards fairer representativeness. We further contrast our findings with respect to a movie genre, budget, and various audience-related features such as movie gross and user ratings. We lastly propose a fine description of significant asymmetries in the mise-en-scène and mise-en-cadre of characters in relation to their gender and the spatial composition of a given frame.

长期以来，大众媒体中的性别表征主要是通过对内容进行定性分析来研究的。本文说明了如何在这种情况下使用自动化计算方法来扩大此类经验观察并提高其分辨率和重要性。我们专门将面部和性别检测算法应用于跨越三十多年的广泛流行电影，以对女性和男性在屏幕上的存在进行大规模评估。除了确认女性代表人数严重不足之外，我们还表现出明显的时间趋势，即更公平的代表性。我们在电影类型、预算和各种与观众相关的特征（如电影票房和用户评分）方面进一步对比了我们的发现。我们最后提出了对显着不对称的精细描述与性别和给定框架的空间构成相关的角色的场景调度和干部调度。