Working the Crowd: Movies and Mass Politics

Tratner, Michael

doi:10.1353/crt.2003.0035

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Moreover, crowd scenes, where a large number of characters are simultaneously present on-screen, is a relevant cinematographic choice that shifts attention away from the role an individual character plays and instead emphasizes characteristics that are typically associated with herd-like behavior (Tratner, 2003). This perspective is particularly informative in the context of movies as the visual representation of groups suggests that the moods, practices, and opinions of characters existing within those groups may not be under their rational control.…”

Section: Cinematographic Techniquesmentioning

confidence: 99%

Representations of Racial Minorities in Popular Movies: A Content-Analytic Synergy of Computer Vision and Network Science

Malik¹,

Hopp²,

Weber³

2021

Preprint

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In the Hollywood film industry, racial minorities remain underrepresented. Characters from racially underrepresented groups receive less screen time, fewer central story positions, and frequently inherit plotlines, motivations, and actions that are primarily driven by White characters. Currently, there are no clearly defined, standardized, and scalable metrics for taking stock of racial minorities’ cinematographic representation. In this paper, we combine methodological tools from computer vision and network science to develop a content analytic framework for identifying visual and structural racial biases in film productions. We apply our approach on a set of 89 popular, full-length movies, demonstrating that this method provides a scalable examination of racial inclusion in film production and predicts movie performance. We integrate our method into larger theoretical discussions on audiences’ perception of racial minorities and illuminate future research trajectories towards the computational assessment of racial biases in audiovisual narratives.

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Section: Cinematographic Techniquesmentioning

confidence: 99%