Automatic Classification of News Subjects in Broadcast News: Application to a Gender Bias Representation Analysis

TL;DR

This study develops a computational framework using LLMs and smaller models to classify news topics and analyze gender bias in French broadcast news, revealing underrepresentation of women in certain subjects.

Contribution

It introduces a novel approach combining LLMs and fine-tuning for topic classification and gender bias analysis in broadcast news datasets.

Findings

Women underrepresented in sports, politics, conflicts

Women have more speaking time in weather, commercials, health

Representation varies between private and public channels

Abstract

This paper introduces a computational framework designed to delineate gender distribution biases in topics covered by French TV and radio news. We transcribe a dataset of 11.7k hours, broadcasted in 2023 on 21 French channels. A Large Language Model (LLM) is used in few-shot conversation mode to obtain a topic classification on those transcriptions. Using the generated LLM annotations, we explore the finetuning of a specialized smaller classification model, to reduce the computational cost. To evaluate the performances of these models, we construct and annotate a dataset of 804 dialogues. This dataset is made available free of charge for research purposes. We show that women are notably underrepresented in subjects such as sports, politics and conflicts. Conversely, on topics such as weather, commercials and health, women have more speaking time than their overall average across all subjects. We also observe representations differences between private and public service channels.