GenderedNews: Une approche computationnelle des \'ecarts de repr\'esentation des genres dans la presse fran\c{c}aise

TL;DR

GenderedNews is an online dashboard that uses NLP to measure gender imbalance in French online press, providing automated, weekly metrics on mentions and quotations of men and women to complement traditional studies.

Contribution

It introduces an automated, NLP-based approach to quantify gender representation in media, offering a scalable alternative to manual content analysis.

Findings

Automated metrics for gender imbalance in French online news.

Weekly data collection from seven major news outlets.

Visualization tools for analyzing gender representation trends.

Abstract

In this article, we present {\it GenderedNews} (\url{https://gendered-news.imag.fr}), an online dashboard which gives weekly measures of gender imbalance in French online press. We use Natural Language Processing (NLP) methods to quantify gender inequalities in the media, in the wake of global projects like the Global Media Monitoring Project. Such projects are instrumental in highlighting gender imbalance in the media and its very slow evolution. However, their generalisation is limited by their sampling and cost in terms of time, data and staff. Automation allows us to offer complementary measures to quantify inequalities in gender representation. We understand representation as the presence and distribution of men and women mentioned and quoted in the news -- as opposed to representation as stereotypification. In this paper, we first review different means adopted by previous studies on gender inequality in the media : qualitative content analysis, quantitative content analysis and computational methods. We then detail the methods adopted by {\it GenderedNews} and the two metrics implemented: the masculinity rate of mentions and the proportion of men quoted in online news. We describe the data collected daily (seven main titles of French online news media) and the methodology behind our metrics, as well as a few visualisations. We finally propose to illustrate possible analysis of our data by conducting an in-depth observation of a sample of two months of our database.