Gender Representation in TV and Radio: Automatic Information Extraction methods versus Manual Analyses
Year: 2024
Type: conference-paper
Abstract: This study investigates the relationship between automatic information extraction descriptors and manual analyses to describe gender representation disparities in TV and Radio. Automatic descriptors, including speech time, facial categorization and speech transcriptions are compared with channel reports on a vast 32,000-hour corpus of French broadcasts from 2023. Findings reveal systemic gender imbalances, with women underrepresented compared to men across all descriptors. Notably, manual channel reports show higher women's presence than automatic estimates and references to women are lower than their speech time. Descriptors share common dynamics during high and low audiences, war coverage, or private versus public channels. While women are more visible than audible in French TV, this trend is inverted in news with unseen journalists depicting male protagonists. A statistical test shows 3 main effects influencing references to women: program category, channel and speaker gender.
Source: Interspeech (USB)
Language: English
Subfield: Gender Studies
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
Awards: G3010527862, Gender Equality Monitor
OpenAlex ID: W4402112255
