  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.
