Gender Representation in TV and Radio: Automatic Information Extraction methods versus Manual Analyses

TL;DR

This study compares automatic and manual methods for analyzing gender representation in French TV and radio, revealing systemic imbalances and differences between methods across various contexts.

Contribution

It introduces a comprehensive comparison between automatic extraction descriptors and manual analyses for gender representation in a large broadcast corpus.

Findings

Women are underrepresented compared to men across all descriptors.

Manual reports tend to overestimate women's presence relative to automatic estimates.

Gender references are influenced by program category, channel, and speaker gender.

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.