Automatic Classification of News Subjects in Broadcast News: Application to a Gender Bias Representation Analysis
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 31 references that are in the map. First posted on arXiv 19 Jul 2024.
Builds on
15 of 31 references.
References · 15
- 2015Distilling the Knowledge in a Neural Networkcited by 10,324
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
- 2024Mixtral of Expertscited by 1,823
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
- 2013Do Deep Nets Really Need to be Deep?cited by 474
- 2019CamemBERT: a Tasty French Language Modelcited by 368
- 2023WhisperX: Time-Accurate Speech Transcription of Long-Form Audiocited by 326
- 2021Want To Reduce Labeling Cost? GPT-3 Can Helpcited by 196
- 2021GPT3Mix: Leveraging Large-scale Language Models for Text Augmentationcited by 187
- 2019FlauBERT: Unsupervised Language Model Pre-training for Frenchcited by 153
- 2023UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognitioncited by 72
- 2022Language Models in the Loop: Incorporating Prompting into Weak Supervisioncited by 52
3 more
- 2024NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Datacited by 34
- 2022ASR-Generated Text for Language Model Pre-training Applied to Speech Taskscited by 6
- 2024InaGVAD : a Challenging French TV and Radio Corpus Annotated for Speech Activity Detection and Speaker Gender Segmentationcited by 1
Built on by
Citing papers per year: 2026: 2.
2 papers.